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Sample records for seizure detection based

  1. Seizure detection algorithms based on EMG signals

    DEFF Research Database (Denmark)

    Conradsen, Isa

    Background: the currently used non-invasive seizure detection methods are not reliable. Muscle fibers are directly connected to the nerves, whereby electric signals are generated during activity. Therefore, an alarm system on electromyography (EMG) signals is a theoretical possibility. Objective......: to show whether medical signal processing of EMG data is feasible for detection of epileptic seizures. Methods: EMG signals during generalised seizures were recorded from 3 patients (with 20 seizures in total). Two possible medical signal processing algorithms were tested. The first algorithm was based...... the frequency-based algorithm was efficient for detecting the seizures in the third patient. Conclusion: Our results suggest that EMG signals could be used to develop an automatic seizuredetection system. However, different patients might require different types of algorithms /approaches....

  2. A physiology-based seizure detection system for multichannel EEG.

    Directory of Open Access Journals (Sweden)

    Chia-Ping Shen

    Full Text Available BACKGROUND: Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable. METHODOLOGY: This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn, statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching. PRINCIPAL FINDINGS: We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection. CONCLUSION: We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.

  3. Multi-modal Intelligent Seizure Acquisition (MISA) system - A new approach towards seizure detection based on full body motion measures

    DEFF Research Database (Denmark)

    Conradsen, Isa; Beniczky, Sándor; Wolf, Peter

    2009-01-01

    Many epilepsy patients cannot call for help during a seizure, because they are unconscious or because of the affection of their motor system or speech function. This can lead to injuries, medical complications and at worst death. An alarm system setting off at seizure onset could help to avoid ha...... is individualized (subject specific) it is able to detect all simulated seizures with a maximum of 1 false positive. Based on the results from the simulated seizures and normal movements the MISA system seems to be a promising approach to seizure detection....... hazards. Today no reliable alarm systems are available. A Multi-modal Intelligent Seizure Acquisition (MISA) system based on full body motion data seems as a good approach towards detection of epileptic seizures. The system is the first to provide a full body description for epilepsy applications. Three......) and audio and video recording. The results showed that a non-subject specific MISA system developed on data from the modalities: accelerometer (ACM), gyroscope and EMG is able to detect 98% of the simulated seizures and at the same time mistakes only 4 of the normal movements for seizures. If the system...

  4. Seizure Onset Detection based on a Uni- or Multi-modal Intelligent Seizure Acquisition (UISA/MISA) System

    DEFF Research Database (Denmark)

    Conradsen, Isa; Beniczky, Sándor; Wolf, Peter

    2010-01-01

    An automatic Uni- or Multi-modal Inteligent Seizure Acquisition (UISA/MISA) system is highly applicable for onset detection of epileptic seizures based on motion data. The modalities used are surface electromyography (sEMG), acceleration (ACC) and angular velocity (ANG). The new proposed automatic...

  5. Seizure-specific wavelet (Seizlet) design for epileptic seizure detection using CorrEntropy ellipse features based on seizure modulus maximas patterns.

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    Behnam, Morteza; Pourghassem, Hossein

    2017-01-30

    EEG signal analysis of pediatric patients plays vital role for making a decision to intervene in presurgical stages. In this paper, an offline seizure detection algorithm based on definition of a seizure-specific wavelet (Seizlet) is presented. After designing the Seizlet, by forming cone of influence map of the EEG signal, four types of layouts are analytically designed that are called Seizure Modulus Maximas Patterns (SMMP). By mapping CorrEntropy Induced Metric (CIM) series, four structural features based on least square estimation of fitted non-tilt conic ellipse are extracted that are called CorrEntropy Ellipse Features (CEF). The parameters of the SMMP and CEF are tuned by employing a hybrid optimization algorithm based on honeybee hive optimization in combination with Las Vegas randomized algorithm and Elman recurrent classifier. Eventually, the optimal features by AdaBoost classifiers in a cascade structure are classified into the seizure and non-seizure signals. The proposed algorithm is evaluated on 844h signals with 163 seizure events recorded from 23 patients with intractable seizure disorder and accuracy rate of 91.44% and false detection rate of 0.014 per hour are obtained by 7-channel EEG signals. To overcome the restrictions of general kernels and wavelet coefficient-based features, we designed the Seizlet as an exclusive kernel of seizure signal for first time. Also, the Seizlet-based patterns of EEG signals have been modeled to extract the seizure. The reported results demonstrate that our proposed Seizlet is effectiveness to extract the patterns of the epileptic seizure. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Detection of tonic epileptic seizures based on surface electromyography

    DEFF Research Database (Denmark)

    Larsen, Sigge N.; Conradsen, Isa; Beniczky, Sandor

    2014-01-01

    , median frequency, zero crossing rate and approximate entropy. These features were used as input in the random forest classifier to decide if a data segment was from a seizure or not. The goal was to develop a generic algorithm for all tonic seizures, but better results were achieved when certain...... parameters were adapted specifically for each patient. With patient specific parameters the algorithm obtained a sensitivity of 100% for four of six patients with false detection rates between 0.08 and 7.90 per hour....

  7. Early Seizure Detection by Applying Frequency-Based Algorithm Derived from the Principal Component Analysis.

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    Lee, Jiseon; Park, Junhee; Yang, Sejung; Kim, Hani; Choi, Yun Seo; Kim, Hyeon Jin; Lee, Hyang Woon; Lee, Byung-Uk

    2017-01-01

    The use of automatic electrical stimulation in response to early seizure detection has been introduced as a new treatment for intractable epilepsy. For the effective application of this method as a successful treatment, improving the accuracy of the early seizure detection is crucial. In this paper, we proposed the application of a frequency-based algorithm derived from principal component analysis (PCA), and demonstrated improved efficacy for early seizure detection in a pilocarpine-induced epilepsy rat model. A total of 100 ictal electroencephalographs (EEG) during spontaneous recurrent seizures from 11 epileptic rats were finally included for the analysis. PCA was applied to the covariance matrix of a conventional EEG frequency band signal. Two PCA results were compared: one from the initial segment of seizures (5 sec of seizure onset) and the other from the whole segment of seizures. In order to compare the accuracy, we obtained the specific threshold satisfying the target performance from the training set, and compared the False Positive (FP), False Negative (FN), and Latency (Lat) of the PCA based feature derived from the initial segment of seizures to the other six features in the testing set. The PCA based feature derived from the initial segment of seizures performed significantly better than other features with a 1.40% FP, zero FN, and 0.14 s Lat. These results demonstrated that the proposed frequency-based feature from PCA that captures the characteristics of the initial phase of seizure was effective for early detection of seizures. Experiments with rat ictal EEGs showed an improved early seizure detection rate with PCA applied to the covariance of the initial 5 s segment of visual seizure onset instead of using the whole seizure segment or other conventional frequency bands.

  8. Classifier models and architectures for EEG-based neonatal seizure detection

    International Nuclear Information System (INIS)

    Greene, B R; Marnane, W P; Lightbody, G; Reilly, R B; Boylan, G B

    2008-01-01

    Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with a poor long-term outcome. Early detection and treatment may improve prognosis. This paper aims to develop an optimal set of parameters and a comprehensive scheme for patient-independent multi-channel EEG-based neonatal seizure detection. We employed a dataset containing 411 neonatal seizures. The dataset consists of multi-channel EEG recordings with a mean duration of 14.8 h from 17 neonatal patients. Early-integration and late-integration classifier architectures were considered for the combination of information across EEG channels. Three classifier models based on linear discriminants, quadratic discriminants and regularized discriminants were employed. Furthermore, the effect of electrode montage was considered. The best performing seizure detection system was found to be an early integration configuration employing a regularized discriminant classifier model. A referential EEG montage was found to outperform the more standard bipolar electrode montage for automated neonatal seizure detection. A cross-fold validation estimate of the classifier performance for the best performing system yielded 81.03% of seizures correctly detected with a false detection rate of 3.82%. With post-processing, the false detection rate was reduced to 1.30% with 59.49% of seizures correctly detected. These results represent a comprehensive illustration that robust reliable patient-independent neonatal seizure detection is possible using multi-channel EEG

  9. A fuzzy rule-based system for epileptic seizure detection in intracranial EEG.

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    Aarabi, A; Fazel-Rezai, R; Aghakhani, Y

    2009-09-01

    We present a method for automatic detection of seizures in intracranial EEG recordings from patients suffering from medically intractable focal epilepsy. We designed a fuzzy rule-based seizure detection system based on knowledge obtained from experts' reasoning. Temporal, spectral, and complexity features were extracted from IEEG segments, and spatio-temporally integrated using the fuzzy rule-based system for seizure detection. A total of 302.7h of intracranial EEG recordings from 21 patients having 78 seizures was used for evaluation of the system. The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11s. There was only one missed seizure. Most of false detections were caused by high-amplitude rhythmic activities. The results from the system correlate well with those from expert visual analysis. The fuzzy rule-based seizure detection system enabled us to deal with imprecise boundaries between interictal and ictal IEEG patterns. This system may serve as a good seizure detection tool with high sensitivity and low false detection rate for monitoring long-term IEEG.

  10. Epileptic Seizure Detection and Prediction Based on Continuous Cerebral Blood Flow Monitoring – a Review

    Directory of Open Access Journals (Sweden)

    Senay Tewolde

    2015-01-01

    Full Text Available Epilepsy is the third most common neurological illness, affecting 1% of the world’s population. Despite advances in medicine, about 25 to 30% of the patients do not respond to or cannot tolerate the severe side effects of medical treatment, and surgery is not an option for the majority of patients with epilepsy. The objective of this article is to review the current state of research on seizure detection based on cerebral blood flow (CBF data acquired by thermal diffusion flowmetry (TDF, and CBF-based seizure prediction. A discussion is provided on the applications, advantages, and disadvantages of TDF in detecting and localizing seizure foci, as well as its role in seizure prediction. Also presented are an overview of the present challenges and possible future research directions (along with methodological guidelines of the CBF-based seizure detection and prediction methods.

  11. Assessment of a scalp EEG-based automated seizure detection system.

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    Kelly, K M; Shiau, D S; Kern, R T; Chien, J H; Yang, M C K; Yandora, K A; Valeriano, J P; Halford, J J; Sackellares, J C

    2010-11-01

    The purpose of this study was to evaluate and validate an offline, automated scalp EEG-based seizure detection system and to compare its performance to commercially available seizure detection software. The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ∼3653h with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (∼1200h with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persyst's Reveal®, version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms. The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24h, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24h, respectively. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p<0.05) smaller FDR. The study validates the performance of the IdentEvent™ seizure detection system. With comparable detection sensitivity, an improved false detection rate makes the automated seizure

  12. Improving early seizure detection.

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    Jouny, Christophe C; Franaszczuk, Piotr J; Bergey, Gregory K

    2011-12-01

    Over the last decade, the search for a method able to reliably predict seizures hours in advance has been largely replaced by the more realistic goal of very early detection of seizure onset, which would allow therapeutic or warning devices to be triggered prior to the onset of disabling clinical symptoms. We explore in this article the steps along the pathway from data acquisition to closed-loop applications that can and should be considered to design the most efficient early seizure detection. Microelectrodes, high-frequency oscillations, high sampling rate, high-density arrays, and modern analysis techniques are all elements of the recording and detection process that in combination with modeling studies can provide new insights into the dynamics of seizure onsets. Each of these steps needs to be considered if detection devices that will favorably impact the quality of life of patients are to be implemented. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

  13. Automated Algorithm for Generalized Tonic–Clonic Epileptic Seizure Onset Detection Based on sEMG Zero-Crossing Rate

    DEFF Research Database (Denmark)

    Conradsen, Isa; Beniczky, Sándor; Hoppe, Karsten

    2012-01-01

    Patients are not able to call for help during a generalized tonic–clonic epileptic seizure. Our objective was to develop a robust generic algorithm for automatic detection of tonic–clonic seizures, based on surface electromyography (sEMG) signals suitable for a portable device. Twenty-two seizure...

  14. Seizure Onset Detection based on one sEMG channel

    DEFF Research Database (Denmark)

    Conradsen, Isa; Beniczky, Sandor; Hoppe, Karsten

    2011-01-01

    with a Butterworth filter with a cut-off frequency of 150 Hz. The number of zero-crossings with a hysteresis of ±50μV is the only feature extracted. The number of counts in a window of 1 second and the number of windows to make a detection is tested with a leave-one-out method. On 6 patients the method performs...... with a sensitivity of 100%, a median latency of 7.6 seconds and a median false detection rate of 0.04/h....

  15. Novel feature extraction method based on weight difference of weighted network for epileptic seizure detection.

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    Fenglin Wang; Qingfang Meng; Hong-Bo Xie; Yuehui Chen

    2014-01-01

    The extraction method of classification feature is primary and core problem in all epileptic EEG detection algorithms, since it can seriously affect the performance of the detection algorithm. In this paper, a novel epileptic EEG feature extraction method based on the statistical parameter of weighted complex network is proposed. The EEG signal is first transformed into weighted network and the weight differences of all the nodes in the network are analyzed. Then the sum of top quintile weight differences is extracted as the classification feature. At last, the extracted feature is applied to classify the epileptic EEG dataset. Experimental results show that the single feature classification based on the extracted feature obtains higher classification accuracy up to 94.75%, which indicates that the extracted feature can distinguish the ictal EEG from interictal EEG and has great potentiality of real-time epileptic seizures detection.

  16. Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

    Science.gov (United States)

    Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

    2017-01-01

    Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

  17. An automatic patient-specific seizure onset detection method in intracranial EEG based on incremental nonlinear dimensionality reduction.

    Science.gov (United States)

    Zhang, Yizhuo; Xu, Guanghua; Wang, Jing; Liang, Lin

    2010-01-01

    Epileptic seizure features always include the morphology and spatial distribution of nonlinear waveforms in the electroencephalographic (EEG) signals. In this study, we propose a novel incremental learning scheme based on nonlinear dimensionality reduction for automatic patient-specific seizure onset detection. The method allows for identification of seizure onset times in long-term EEG signals acquired from epileptic patients. Firstly, a nonlinear dimensionality reduction (NDR) method called local tangent space alignment (LTSA) is used to reduce the dimensionality of available initial feature sets extracted with continuous wavelet transform (CWT). One-dimensional manifold which reflects the intrinsic dynamics of seizure onset is obtained. For each patient, IEEG recordings containing one seizure onset is sufficient to train the initial one-dimensional manifold. Secondly, an unsupervised incremental learning scheme is proposed to update the initial manifold when the unlabelled EEG segments flow in sequentially. The incremental learning scheme can cluster the new coming samples into the trained patterns (containing or not containing seizure onsets). Intracranial EEG recordings from 21 patients with duration of 193.8h and 82 seizures are used for the evaluation of the method. Average sensitivity of 98.8%, average uninteresting false positive rate of 0.24/h, average interesting false positives rate of 0.25/h, and average detection delay of 10.8s are obtained. Our method offers simple, accurate training with less human intervening and can be well used in off-line seizure detection. The unsupervised incremental learning scheme has the potential in identifying novel IEEG classes (different onset patterns) within the data. Copyright © 2010 Elsevier Ltd. All rights reserved.

  18. Using Dictionary Pair Learning for Seizure Detection.

    Science.gov (United States)

    Ma, Xin; Yu, Nana; Zhou, Weidong

    2018-02-13

    Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal linearly separable. In DPL, the synthesis dictionary and analysis dictionary are learned jointly from original training samples with alternating minimization method, and sparse coefficients are obtained by using of linear projection instead of costly [Formula: see text]-norm or [Formula: see text]-norm optimization. At last, the reconstructed residuals associated with seizure and nonseizure sub-dictionary pairs are calculated as the decision values, and the postprocessing is performed for improving the recognition rate and reducing the false detection rate of the system. A total of 530[Formula: see text]h from 20 patients with 81 seizures were used to evaluate the system. Our proposed method has achieved an average segment-based sensitivity of 93.39%, specificity of 98.51%, and event-based sensitivity of 96.36% with false detection rate of 0.236/h.

  19. EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

    Science.gov (United States)

    Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M

    2017-10-01

    The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

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    Talathi, S. S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2017-06-05

    Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.

  1. Channel selection for automatic seizure detection

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Kjaer, Troels Wesenberg; Madsen, Rasmus Elsborg

    2012-01-01

    Objective: To investigate the performance of epileptic seizure detection using only a few of the recorded EEG channels and the ability of software to select these channels compared with a neurophysiologist. Methods: Fifty-nine seizures and 1419 h of interictal EEG are used for training and testing...

  2. Detection of Epileptic Seizures with Multi-modal Signal Processing

    DEFF Research Database (Denmark)

    Conradsen, Isa

    The main focus of this dissertation lies within the area of epileptic seizure detection. Medically refractory epileptic patients suffer from the unawareness of when the next seizure sets in, and what the consequences will be. A wearable device based on uni- or multi-modalities able to detect and ...... implemented in a wireless sEMG device. A double-blind test on patients in the clinic, showed 100 % reliability for three of four patients, whereas it failed for the last patient, who had atypical GTC seizures....... and alarm whenever a seizure starts is of great importance to these patients and their relatives, in the sense, that the alert of the seizure will make them feel more safe. Thus the objective of the project is to investigate the movements of convulsive epileptic seizures and design seizure detection...... methods have been applied in different studies in order to achieve the goal of reliable seizure detection. In the first study we present a method where the support vector machine classifier is applied on features based on wavelet bands. This was used on multi-modal data from control subjects...

  3. Detection and Prediction of Epileptic Seizures

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas

    detected without any false positive detections. This was obtained using a generic algorithm on the signals from only a single frontal channel. Applying the same algorithm architecture on EEG data from two outpatient children monitored for approximately three entire days each, the sensitivity was 90......Approximately 50 million people worldwide suffer from epilepsy. Although 70% can control their seizures by anti-epileptic drugs, it is still a cumbersome disease to live with for a large group of patients. The current PhD dissertation investigates how these people can be helped by continous...... monitoring of their brain waves. More specifically, three issues were investigated: The feasibility of automatic seizure prediction, optimization of automatic seizure detection algorithms, and the link between intra- and extracranial EEG. Regarding feasibility of automatic seizure prediction, neither...

  4. Real-time phase correlation based integrated system for seizure detection

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    Romaine, James B.; Delgado-Restituto, Manuel; Leñero-Bardallo, Juan A.; Rodríguez-Vázquez, Ángel

    2017-05-01

    This paper reports a low area, low power, integer-based digital processor for the calculation of phase synchronization between two neural signals. The processor calculates the phase-frequency content of a signal by identifying the specific time periods associated with two consecutive minima. The simplicity of this phase-frequency content identifier allows for the digital processor to utilize only basic digital blocks, such as registers, counters, adders and subtractors, without incorporating any complex multiplication and or division algorithms. In fact, the processor, fabricated in a 0.18μm CMOS process, only occupies an area of 0.0625μm2 and consumes 12.5nW from a 1.2V supply voltage when operated at 128kHz. These low-area, low-power features make the proposed processor a valuable computing element in closed loop neural prosthesis for the treatment of neural diseases, such as epilepsy, or for extracting functional connectivity maps between different recording sites in the brain.

  5. Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: A proof-of-concept study

    Directory of Open Access Journals (Sweden)

    Carlen Peter L

    2011-04-01

    Full Text Available Abstract Background Epilepsy is a common neurological disorder characterized by recurrent electrophysiological activities, known as seizures. Without the appropriate detection strategies, these seizure episodes can dramatically affect the quality of life for those afflicted. The rationale of this study is to develop an unsupervised algorithm for the detection of seizure states so that it may be implemented along with potential intervention strategies. Methods Hidden Markov model (HMM was developed to interpret the state transitions of the in vitro rat hippocampal slice local field potentials (LFPs during seizure episodes. It can be used to estimate the probability of state transitions and the corresponding characteristics of each state. Wavelet features were clustered and used to differentiate the electrophysiological characteristics at each corresponding HMM states. Using unsupervised training method, the HMM and the clustering parameters were obtained simultaneously. The HMM states were then assigned to the electrophysiological data using expert guided technique. Minimum redundancy maximum relevance (mRMR analysis and Akaike Information Criterion (AICc were applied to reduce the effect of over-fitting. The sensitivity, specificity and optimality index of chronic seizure detection were compared for various HMM topologies. The ability of distinguishing early and late tonic firing patterns prior to chronic seizures were also evaluated. Results Significant improvement in state detection performance was achieved when additional wavelet coefficient rates of change information were used as features. The final HMM topology obtained using mRMR and AICc was able to detect non-ictal (interictal, early and late tonic firing, chronic seizures and postictal activities. A mean sensitivity of 95.7%, mean specificity of 98.9% and optimality index of 0.995 in the detection of chronic seizures was achieved. The detection of early and late tonic firing was

  6. Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: a proof-of-concept study.

    Science.gov (United States)

    Chiu, Alan Wl; Derchansky, Miron; Cotic, Marija; Carlen, Peter L; Turner, Steuart O; Bardakjian, Berj L

    2011-04-19

    Epilepsy is a common neurological disorder characterized by recurrent electrophysiological activities, known as seizures. Without the appropriate detection strategies, these seizure episodes can dramatically affect the quality of life for those afflicted. The rationale of this study is to develop an unsupervised algorithm for the detection of seizure states so that it may be implemented along with potential intervention strategies. Hidden Markov model (HMM) was developed to interpret the state transitions of the in vitro rat hippocampal slice local field potentials (LFPs) during seizure episodes. It can be used to estimate the probability of state transitions and the corresponding characteristics of each state. Wavelet features were clustered and used to differentiate the electrophysiological characteristics at each corresponding HMM states. Using unsupervised training method, the HMM and the clustering parameters were obtained simultaneously. The HMM states were then assigned to the electrophysiological data using expert guided technique. Minimum redundancy maximum relevance (mRMR) analysis and Akaike Information Criterion (AICc) were applied to reduce the effect of over-fitting. The sensitivity, specificity and optimality index of chronic seizure detection were compared for various HMM topologies. The ability of distinguishing early and late tonic firing patterns prior to chronic seizures were also evaluated. Significant improvement in state detection performance was achieved when additional wavelet coefficient rates of change information were used as features. The final HMM topology obtained using mRMR and AICc was able to detect non-ictal (interictal), early and late tonic firing, chronic seizures and postictal activities. A mean sensitivity of 95.7%, mean specificity of 98.9% and optimality index of 0.995 in the detection of chronic seizures was achieved. The detection of early and late tonic firing was validated with experimental intracellular electrical

  7. Automatic seizure detection in SEEG using high frequency activities in wavelet domain.

    Science.gov (United States)

    Ayoubian, L; Lacoma, H; Gotman, J

    2013-03-01

    Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80-500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decomposition, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

  8. Toward a Personalized Real-Time Diagnosis in Neonatal Seizure Detection

    DEFF Research Database (Denmark)

    Temko, Andriy; Sarkar, Achintya Kumar; Boylan, Geraldine

    2017-01-01

    The problem of creating a personalized seizure detection algorithm for newborns is tackled in this study. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system which is based on a combination of patient-adaptive (...

  9. Automatic ictal HFO detection for determination of initial seizure spread.

    Science.gov (United States)

    Graef, Andreas; Flamm, Christoph; Pirker, Susanne; Baumgartner, Christoph; Deistler, Manfred; Matz, Gerald

    2013-01-01

    High-frequency oscillations (HFOs) are a reliable indicator for the epileptic seizure onset zone (SOZ) in ECoG recordings. We propose a novel method for the automatic detection of ictal HFOs in the ripple band (80-250 Hz) based on CFAR matched sub-space filtering. This allows to track the early propagation of ictal HFOs, revealing initial and follow-up epileptic activity on the electrodes. We apply this methodology to two seizures from one patient suffering from focal epilepsy. The electrodes identified are in very good accordance with the visual HFO analysis by clinicians. Furthermore the electrodes with initial HFO activity are correlated well with the SOZ (conventional v-activity).

  10. Seizure Prediction and Detection via Phase and Amplitude Lock Values.

    Science.gov (United States)

    Myers, Mark H; Padmanabha, Akshay; Hossain, Gahangir; de Jongh Curry, Amy L; Blaha, Charles D

    2016-01-01

    A robust seizure prediction methodology would enable a "closed-loop" system that would only activate as impending seizure activity is detected. Such a system would eliminate ongoing stimulation to the brain, thereby eliminating such side effects as coughing, hoarseness, voice alteration, and paresthesias (Murphy et al., 1998; Ben-Menachem, 2001), while preserving overall battery life of the system. The seizure prediction and detection algorithm uses Phase/Amplitude Lock Values (PLV/ALV) which calculate the difference of phase and amplitude between electroencephalogram (EEG) electrodes local and remote to the epileptic event. PLV is used as the seizure prediction marker and signifies the emergence of abnormal neuronal activations through local neuron populations. PLV/ALVs are used as seizure detection markers to demarcate the seizure event, or when the local seizure event has propagated throughout the brain turning into a grand-mal event. We verify the performance of this methodology against the "CHB-MIT Scalp EEG Database" which features seizure attributes for testing. Through this testing, we can demonstrate a high degree of sensivity and precision of our methodology between pre-ictal and ictal events.

  11. Seizure Prediction and Detection via Phase and Amplitude Lock Values

    Directory of Open Access Journals (Sweden)

    Mark H Myers

    2016-03-01

    Full Text Available A robust seizure prediction methodology would enable a ‘closed-loop’ system that would only activate as impending seizure activity is detected. Such a system would eliminate ongoing stimulation to the brain, thereby eliminating such side effects as coughing, hoarseness, voice alteration, and paresthesias (Murphy et al., 1998, Ben-Menachem, 2001, while preserving overall battery life of the system. The seizure prediction and detection algorithm uses Phase/Amplitude Lock Values (PLV/ALV which calculate the difference of phase and amplitude between EEG electrodes local and remote to the epileptic event. PLV is used as the seizure prediction marker and signifies the emergence of abnormal neuronal activations through local neuron populations. PLV/ALVs are used as seizure detection markers to demarcate the seizure event, or when the local seizure event has propagated throughout the brain turning into a grand-mal event. We verify the performance of this methodology against the ‘CHB-MIT Scalp EEG Database’ which features seizure attributes for testing. Through this testing, we can demonstrate a high degree of sensivity and precision of our methodology between pre-ictal and ictal events.

  12. Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

    Science.gov (United States)

    Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

    2017-12-01

    Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

  13. Improved patient specific seizure detection during pre-surgical evaluation.

    LENUS (Irish Health Repository)

    Chua, Eric C-P

    2011-04-01

    There is considerable interest in improved off-line automated seizure detection methods that will decrease the workload of EEG monitoring units. Subject-specific approaches have been demonstrated to perform better than subject-independent ones. However, for pre-surgical diagnostics, the traditional method of obtaining a priori data to train subject-specific classifiers is not practical. We present an alternative method that works by adapting the threshold of a subject-independent to a specific subject based on feedback from the user.

  14. Reliable epileptic seizure detection using an improved wavelet neural network

    Directory of Open Access Journals (Sweden)

    Zarita Zainuddin

    2013-05-01

    Full Text Available BackgroundElectroencephalogram (EEG signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts. AimsThis study proposes a novel and reliable seizure detection system, where the statistical features extracted from the discrete wavelet transform are used in conjunction with an improved wavelet neural network (WNN to identify the occurrence of seizures. Method Experimental simulations were carried out on a well-known publicly available dataset, which was kindly provided by the Epilepsy Center, University of Bonn, Germany. The normal and epileptic EEG signals were first pre-processed using the discrete wavelet transform. Subsequently, a set of statistical features was extracted to train a WNNs-based classifier. ResultsThe study has two key findings. First, simulation results showed that the proposed improved WNNs-based classifier gave excellent predictive ability, where an overall classification accuracy of 98.87% was obtained. Second, by using the 10th and 90th percentiles of the absolute values of the wavelet coefficients, a better set of EEG features can be identified from the data, as the outliers are removed before any further downstream analysis.ConclusionThe obtained high prediction accuracy demonstrated the feasibility of the proposed seizure detection scheme. It suggested the prospective implementation of the proposed method in developing a real time automated epileptic diagnostic system with fast and accurate response that could assist neurologists in the decision making process.

  15. Seizure detection using the phase-slope index and multichannel ECoG

    KAUST Repository

    Rana, Puneet

    2012-04-01

    Detection and analysis of epileptic seizures is of clinical and research interest. We propose a novel seizure detection and analysis scheme based on the phase-slope index (PSI) of directed influence applied to multichannel electrocorticogram data. The PSI metric identifies increases in the spatio-temporal interactions between channels that clearly distinguish seizure from interictal activity. We form a global metric of interaction between channels and compare this metric to a threshold to detect the presence of seizures. The threshold is chosen based on a moving average of recent activity to accommodate differences between patients and slow changes within each patient over time. We evaluate detection performance over a challenging population of five patients with different types of epilepsy using a total of 47 seizures in nearly 258 h of recorded data. Using a common threshold procedure, we show that our approach detects all of the seizures in four of the five patients with a false detection rate less than two per hour. A variation on the global metric is proposed to identify which channels are strong drivers of activity in each patient. These metrics are computationally efficient and suitable for real-time application. © 2006 IEEE.

  16. An efficient detection of epileptic seizure by differentiation and spectral analysis of electroencephalograms.

    Science.gov (United States)

    Kang, Jae-Hwan; Chung, Yoon Gi; Kim, Sung-Phil

    2015-11-01

    Epilepsy is a critical neurological disorder resulting from abnormal hyper-excitability of neurons in the brain. Studies have shown that epilepsy can be detected in electroencephalography (EEG) recordings of patients suffering from seizures. The performance of EEG-based epileptic seizure detection relies largely on how well one can extract features from an EEG that characterize seizure activity. Conventional feature extraction methods using time-series analysis, spectral analysis and nonlinear dynamic analysis have advanced in recent years to improve detection. The computational complexity has also increased to obtain a higher detection rate. This study aimed to develop an efficient feature extraction method based on Hjorth's mobility to reduce computational complexity while maintaining high detection accuracy. A new feature extraction method was proposed by computing the spectral power of Hjorth's mobility components, which were effectively estimated by differentiating EEG signals in real-time. Using EEG data in five epileptic patients, this method resulted in a detection rate of 99.46% between interictal and epileptic EEG signals and 99.78% between normal and epileptic EEG signals, which is comparable to most advanced nonlinear methods. These results suggest that the spectral features of Hjorth's mobility components in EEG signals can represent seizure activity and may pave the way for developing a fast and reliable epileptic seizure detection method. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Automatic Epileptic Seizure Onset Detection Using Matching Pursuit

    DEFF Research Database (Denmark)

    Sorensen, Thomas Lynggaard; Olsen, Ulrich L.; Conradsen, Isa

    2010-01-01

    An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose...

  18. Comparative sensitivity of quantitative EEG (QEEG) spectrograms for detecting seizure subtypes.

    Science.gov (United States)

    Goenka, Ajay; Boro, Alexis; Yozawitz, Elissa

    2018-02-01

    To assess the sensitivity of Persyst version 12 QEEG spectrograms to detect focal, focal with secondarily generalized, and generalized onset seizures. A cohort of 562 seizures from 58 patients was analyzed. Successive recordings with 2 or more seizures during continuous EEG monitoring for clinical indications in the ICU or EMU between July 2016 and January 2017 were included. Patient ages ranged from 5 to 64 years (mean = 36 years). There were 125 focal seizures, 187 secondarily generalized and 250 generalized seizures from 58 patients analyzed. Seizures were identified and classified independently by two epileptologists. A correlate to the seizure pattern in the raw EEG was sought in the QEEG spectrograms in 4-6 h EEG epochs surrounding the identified seizures. A given spectrogram was interpreted as indicating a seizure, if at the time of a seizure it showed a visually significant departure from the pre-event baseline. Sensitivities for seizure detection using each spectrogram were determined for each seizure subtype. Overall sensitivities of the QEEG spectrograms for detecting seizures ranged from 43% to 72%, with highest sensitivity (402/562,72%) by the seizure detection trend. The asymmetry spectrogram had the highest sensitivity for detecting focal seizures (117/125,94%). The FFT spectrogram was most sensitive for detecting secondarily generalized seizures (158/187, 84%). The seizure detection trend was the most sensitive for generalized onset seizures (197/250,79%). Our study suggests that different seizure types have specific patterns in the Persyst QEEG spectrograms. Identifying these patterns in the EEG can significantly increase the sensitivity for seizure identification. Copyright © 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  19. Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: a case study.

    Science.gov (United States)

    Sharmila, A; Aman Raj, Suman; Shashank, Pandey; Mahalakshmi, P

    2018-01-01

    In this work, we have used a time-frequency domain analysis method called discrete wavelet transform (DWT) technique. This method stand out compared to other proposed methods because of its algorithmic elegance and accuracy. A wavelet is a mathematical function based on time-frequency analysis in signal processing. It is useful particularly because it allows a weak signal to be recovered from a noisy signal without much distortion. A wavelet analysis works by analysing the image and converting it to mathematical function which is decoded by the receiver. Furthermore, we have used Shannon entropy and approximate entropy (ApEn) for extracting the complexities associated with electroencephalographic (EEG) signals. The ApEn is a suitable feature to characterise the EEGs because its value drops suddenly due to excessive synchronous discharge of neurons in the brain during epileptic activity in this study. EEG signals are decomposed into six EEG sub-bands namely D1-D5 and A5 using DWT technique. Non-linear features such as ApEn and Shannon entropy are calculated from these sub-bands and support vector machine classifiers are used for classification purpose. This scheme is tested using EEG data recorded from five healthy subjects and five epileptic patients during the inter-ictal and ictal periods. The data are acquired from University of Bonn, Germany. The proposed method is evaluated through 15 classification problems, and obtained high classification accuracy of 100% for two cases and it indicates the good classifying performance of the proposed method.

  20. Band-sensitive seizure onset detection via CSP-enhanced EEG features.

    Science.gov (United States)

    Qaraqe, Marwa; Ismail, Muhammad; Serpedin, Erchin

    2015-09-01

    This paper presents two novel epileptic seizure onset detectors. The detectors rely on a common spatial pattern (CSP)-based feature enhancement stage that increases the variance between seizure and nonseizure scalp electroencephalography (EEG). The proposed feature enhancement stage enables better discrimination between seizure and nonseizure features. The first detector adopts a conventional classification stage using a support vector machine (SVM) that feeds the energy features extracted from different subbands to an SVM for seizure onset detection. The second detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results have demonstrated that the first detector achieves a sensitivity of 95.2%, detection latency of 6.43s, and false alarm rate of 0.59perhour. The second detector achieves a sensitivity of 100%, detection latency of 7.28s, and false alarm rate of 1.2per hour for the MAJORITY fusion method. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data

    DEFF Research Database (Denmark)

    Conradsen, Isa; Beniczky, Sándor; Wolf, Peter

    2012-01-01

    The objective is to develop a non-invasive automatic method for detection of epileptic seizures with motor manifestations. Ten healthy subjects who simulated seizures and one patient participated in the study. Surface electromyography (sEMG) and motion sensor features were extracted as energy...

  2. Detection of seizure and epilepsy using higher order statistics in the EMD domain.

    Science.gov (United States)

    Alam, S M Shafiul; Bhuiyan, M I H

    2013-03-01

    In this paper, a method using higher order statistical moments of EEG signals calculated in the empirical mode decomposition (EMD) domain is proposed for detecting seizure and epilepsy. The appropriateness of these moments in distinguishing the EEG signals is investigated through an extensive analysis in the EMD domain. An artificial neural network is employed as the classifier of the EEG signals wherein these moments are used as features. The performance of the proposed method is studied using a publicly available benchmark database for various classification cases that include healthy, interictal (seizure-free interval) and ictal (seizure), healthy and seizure, nonseizure and seizure, and interictal and ictal, and compared with that of several recent methods based on time-frequency analysis and statistical moments. It is shown that the proposed method can provide, in almost all the cases, 100% accuracy, sensitivity, and specificity, especially in the case of discriminating seizure activities from the nonseizure ones for patients with epilepsy while being much faster as compared to the time-frequency analysis-based techniques.

  3. Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds

    Directory of Open Access Journals (Sweden)

    Mengxuan Gao

    2017-02-01

    Full Text Available Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug development, it is difficult to predict seizure-inducing side effects. Here, we introduced a machine learning-based in vitro system designed to detect seizure-inducing side effects. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices, while 14 drugs were bath-perfused at 5 different concentrations each. For each experimental condition, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs that induced seizure-like events and identified diphenhydramine, enoxacin, strychnine and theophylline as “seizure-inducing” drugs, which indeed were reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to detect the seizure-inducing side effects of preclinical drugs.

  4. Low-complexity image processing for real-time detection of neonatal clonic seizures.

    Science.gov (United States)

    Ntonfo, Guy Mathurin Kouamou; Ferrari, Gianluigi; Raheli, Riccardo; Pisani, Francesco

    2012-05-01

    In this paper, we consider a novel low-complexity real-time image-processing-based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation technique, on a per-window basis, where a time window is defined as a sequence of consecutive video frames. While processing is first carried out on a single window basis, we extend our approach to interlaced windows. The performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through receiver operating characteristic curves, considering video recordings of newborns affected by neonatal seizures.

  5. Robust deep network with maximum correntropy criterion for seizure detection.

    Science.gov (United States)

    Qi, Yu; Wang, Yueming; Zhang, Jianmin; Zhu, Junming; Zheng, Xiaoxiang

    2014-01-01

    Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE) as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC) is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE), the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.

  6. Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection

    Directory of Open Access Journals (Sweden)

    Yu Qi

    2014-01-01

    Full Text Available Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE, the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.

  7. Automated real-time detection of tonic-clonic seizures using a wearable EMG device

    DEFF Research Database (Denmark)

    Beniczky, Sándor; Conradsen, Isa; Henning, Oliver

    2018-01-01

    OBJECTIVE: To determine the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) using a wearable surface EMG device. METHODS: We prospectively tested the technical performance and diagnostic accuracy of real-time seizure detection using a wearable surface EMG device. The s...

  8. Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing.

    Science.gov (United States)

    Buteneers, Pieter; Verstraeten, David; van Mierlo, Pieter; Wyckhuys, Tine; Stroobandt, Dirk; Raedt, Robrecht; Hallez, Hans; Schrauwen, Benjamin

    2011-11-01

    In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training. The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452hours from 23 GAERS and 982hours from 15 kainate-induced temporal-lobe epilepsy rats. During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques. A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-the-art technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%. Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. Patterns of epileptic seizure occurrence.

    Science.gov (United States)

    Amengual-Gual, Marta; Sánchez Fernández, Iván; Loddenkemper, Tobias

    2018-02-23

    The occurrence of epileptic seizures in seemingly random patterns takes a great toll on persons with epilepsy and their families. Seizure prediction may markedly improve epilepsy management and, therefore, the quality of life of persons with epilepsy. Literature review. Seizures tend to occur following complex non-random patterns. Circadian oscillators may contribute to the rhythmic patterns of seizure occurrence. Complex mathematical models based on chaos theory try to explain and even predict seizure occurrence. There are several patterns of epileptic seizure occurrence based on seizure location, seizure semiology, and hormonal factors, among others. These patterns are most frequently described for large populations. Inter-individual variability and complex interactions between the rhythmic generators continue to make it more difficult to predict seizures in any individual person. The increasing use of large databases and machine learning techniques may help better define patterns of seizure occurrence in individual patients. Improvements in seizure detection -such as wearable seizure detectors- and in seizure prediction -such as machine learning techniques and artificial as well as biological intelligence- promise to provide further progress in the field of epilepsy and are being applied to closed-loop systems for the treatment of epilepsy. Seizures tend to occur following complex and patient-specific patterns despite their apparently random occurrence. A better understanding of these patterns and current technological advances may allow the implementation of closed-loop detection, prediction, and treatment systems in routine clinical practice. Copyright © 2018. Published by Elsevier B.V.

  10. Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.

    Directory of Open Access Journals (Sweden)

    Amir Eftekhar

    Full Text Available This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70-100% and low false predictions (dependant on training procedure. The cases of highest false predictions are found in the frontal origin with 0.31-0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40-50% for a false prediction rate of less than 0.15/hour.

  11. Non-linear models in focus localization, seizure detection and prediction

    DEFF Research Database (Denmark)

    Henriksen, Jonas

    One of the most devastating problems for epilepsy patients is the unpredictable nature of seizures. Not knowing when or where a seizure occurs has severe consequences in social interaction, ability to work, driving a car, go swimming etc. Traditionally the patient and the doctor work together...... to know when and how often there is seizure activity in the brain. It is therefore interesting to make an objective and automatic detection of the quantity of seizure activity, which is not reliable on competence or fatigue by the epileptologist. With the best algorithm it has been possible to obtain...

  12. Using wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy.

    Science.gov (United States)

    Heldberg, Beeke E; Kautz, Thomas; Leutheuser, Heike; Hopfengartner, Rudiger; Kasper, Burkhard S; Eskofier, Bjoern M

    2015-08-01

    Epilepsy is a disease of the central nervous system. Nearly 70% of people with epilepsy respond to a proper treatment, but for a successful therapy of epilepsy, physicians need to know if and when seizures occur. The gold standard diagnosis tool video-electroencephalography (vEEG) requires patients to stay at hospital for several days. A wearable sensor system, e.g. a wristband, serving as diagnostic tool or event monitor, would allow unobtrusive ambulatory long-term monitoring while reducing costs. Previous studies showed that seizures with motor symptoms such as generalized tonic-clonic seizures can be detected by measuring the electrodermal activity (EDA) and motion measuring acceleration (ACC). In this study, EDA and ACC from 8 patients were analyzed. In extension to previous studies, different types of seizures, including seizures without motor activity, were taken into account. A hierarchical classification approach was implemented in order to detect different types of epileptic seizures using data from wearable sensors. Using a k-nearest neighbor (kNN) classifier an overall sensitivity of 89.1% and an overall specificity of 93.1% were achieved, for seizures without motor activity the sensitivity was 97.1% and the specificity was 92.9%. The presented method is a first step towards a reliable ambulatory monitoring system for epileptic seizures with and without motor activity.

  13. Personalized epilepsy seizure detection using random forest classification over one-dimension transformed EEG data

    OpenAIRE

    Orellana, Marco; Cerqueira, Fabio

    2016-01-01

    This work presents a computational method for improving seizure detection for epilepsy diagnosis. Epilepsy is the second most common neurological disease impacting between 40 and 50 million of patients in the world and its proper diagnosis using electroencephalographic signals implies a long and expensive process which involves medical specialists. The proposed system is a patient-dependent offline system which performs an automatic detection of seizures in brainwaves applying a random forest...

  14. Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification.

    Science.gov (United States)

    Wang, Yuanfa; Li, Zunchao; Feng, Lichen; Zheng, Chuang; Zhang, Wenhao

    2017-01-01

    An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.

  15. Automatic detection of non-convulsive seizures: A reduced complexity approach

    Directory of Open Access Journals (Sweden)

    Tazeem Fatma

    2016-10-01

    Full Text Available Detection of non-convulsive seizures (NCSz is a challenging task because they lack convulsions, meaning no physical visible symptoms are there to detect the presence of a seizure activity. Hence their diagnosis is not easy, also continuous observation of full length EEG for the detection of non-convulsive seizures (NCSz by an expert or a technician is a very exhaustive, time consuming job. A technique for the automatic detection of NCSz is proposed in this paper. The database used in this research was recorded at the All India Institute of Medical Sciences (AIIMS, New Delhi. 13 EEG recordings of 9 subjects consisting of a total 23 seizures of 29.42 min duration were used for analysis. Normalized modified Wilson amplitude is used as a key feature to classify between normal and seizure activity. The main advantage of this study lies in the fact that no classifier is used here and hence algorithm is very simple and computationally fast. With the use of only one feature, all of the seizures under test were detected correctly, and hence the median sensitivity and specificity of 100% and 99.21% were achieved respectively.

  16. Effective implementation of time-frequency matched filter with adapted pre and postprocessing for data-dependent detection of newborn seizures.

    Science.gov (United States)

    Khlif, M S; Colditz, P B; Boashash, B

    2013-12-01

    Neonatal EEG seizures often manifest as nonstationary and multicomponent signals, necessitating analysis in the time-frequency (TF) domain. This paper presents a novel neonatal seizure detector based on effective implementation of the TF matched filter. In the detection process, the TF signatures of EEG seizure are extracted to construct the TF templates used by the matched filter. Matching pursuit (MP) decomposition and narrowband filtering are proposed for the reduction of artifacts prior to seizure detection. Geometrical correlation is used to consolidate the multichannel detections and to reduce the number of false detections due to remnant artifacts. A data-dependent threshold is defined for the classification of EEG. Using 30 newborn EEG records with seizures, the classification process yielded an overall detection accuracy of 92.4% with good detection rate (GDR) of 84.8% and false detection rate of 0.36FD/h. Better detection performance (accuracy >95%) was recorded for relatively long EEG records with short seizure events. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

  17. A machine learning system for automated whole-brain seizure detection

    Directory of Open Access Journals (Sweden)

    P. Fergus

    2016-01-01

    Full Text Available Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain’s electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures. Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier.

  18. Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings.

    Science.gov (United States)

    Baldassano, Steven N; Brinkmann, Benjamin H; Ung, Hoameng; Blevins, Tyler; Conrad, Erin C; Leyde, Kent; Cook, Mark J; Khambhati, Ankit N; Wagenaar, Joost B; Worrell, Gregory A; Litt, Brian

    2017-06-01

    There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  19. The impact of seizures on epilepsy outcomes: A national, community-based survey.

    Science.gov (United States)

    Josephson, Colin B; Patten, Scott B; Bulloch, Andrew; Williams, Jeanne V A; Lavorato, Dina; Fiest, Kirsten M; Secco, Mary; Jette, Nathalie

    2017-05-01

    The aim of this study was to examine the impact of seizures on persons living with epilepsy in a national, community-based setting. The data source was the Survey of Living with Neurological Conditions in Canada (SLNCC), a cohort derived from a national population-based survey of noninstitutionalized persons aged 15 or more years. Participants had to be on a seizure drug or to have had a seizure in the past 5 years to meet the definition of active epilepsy. The respondents were further stratified by seizure status: the seizure group experienced ≥1 seizure in the past 5 years versus the no seizure group who were seizure-free in the past ≥5 years regardless of medication status. Weighted overall and stratified prevalence estimates and odds ratios were used to estimate associations. The SLNCC included 713 persons with epilepsy with a mean age of 45.4 (standard deviation 18.0) years. Fewer people in the seizure group (42.7%) reported being much better than a year ago versus those in the no seizure group (70.1%). Of those with seizures, 32.1% (95% confidence interval [95% CI] 18.8-45.3) had symptoms suggestive of major depression (as per the Patient Health Questionnaire-9) compared to 7.7% (95% CI 3.4-11.9) of those without seizures. Driving, educational, and work opportunities were also significantly limited, whereas stigma was significantly greater in those with seizures. This community-based study emphasizes the need for seizure freedom to improve clinical and psychosocial outcomes in persons with epilepsy. Seizure freedom has an important influence on overall health, as those with at least one seizure over the prior 5 years had an increased risk of mood disorders, worse quality of life, and faced significantly more stigma. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

  20. Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

    Science.gov (United States)

    Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

    2018-03-01

    As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

  1. Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG.

    Science.gov (United States)

    Rabbi, Ahmed F; Aarabi, Ardalan; Fazel-Rezai, Reza

    2010-01-01

    In this paper, we present a method for epileptic seizure prediction from intracranial EEG recordings. We applied correlation dimension, a nonlinear dynamics based univariate characteristic measure for extracting features from EEG segments. Finally, we designed a fuzzy rule-based system for seizure prediction. The system is primarily designed based on expert's knowledge and reasoning. A spatial-temporal filtering method was used in accordance with the fuzzy rule-based inference system for issuing forecasting alarms. The system was evaluated on EEG data from 10 patients having 15 seizures.

  2. Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

    Directory of Open Access Journals (Sweden)

    Azizi E.

    2016-06-01

    Full Text Available Background: Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder. Objective: In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children’s Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity. Method: In this algorithm, L-sec epochs of signals are displayed in form of a thirdorder tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN is used to classify the selected features. Results: The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours. Conclusion: Today, the lack of an automated system to detect or predict the seizure onset is strongly felt.

  3. Low-Power Implantable Device for Onset Detection and Subsequent Treatment of Epileptic Seizures: A Review

    Directory of Open Access Journals (Sweden)

    Muhammad Tariqus Salam

    2010-01-01

    Full Text Available Over the past few years, there has been growing interest in neuro-responsive intracerebral local treatments of seizures, such as focal drug delivery, focal cooling, or electrical stimulation. This mode of treatment requires an effective intracerebral electroencephalographic acquisition system, seizure detector, brain stimulator, and wireless system that consume ultra-low power. This review focuses on alternative brain stimulation treatments for medically intractable epilepsy patients. We mainly discuss clinical studies of long-term responsive stimulation and suggest safer optimized therapeutic options for epilepsy. Finally, we conclude our study with the proposed low-power, implantable fully integrated device that automatically detects low-voltage fast activity ictal onsets and triggers focal treatment to disrupt seizure progression. The detection performance was verified using intracerebral electroencephalographic recordings from two patients with epilepsy. Further experimental validation of this prototype is underway.

  4. Analyzing reliability of seizure diagnosis based on semiology.

    Science.gov (United States)

    Jin, Bo; Wu, Han; Xu, Jiahui; Yan, Jianwei; Ding, Yao; Wang, Z Irene; Guo, Yi; Wang, Zhongjin; Shen, Chunhong; Chen, Zhong; Ding, Meiping; Wang, Shuang

    2014-12-01

    This study aimed to determine the accuracy of seizure diagnosis by semiological analysis and to assess the factors that affect diagnostic reliability. A total of 150 video clips of seizures from 50 patients (each with three seizures of the same type) were observed by eight epileptologists, 12 neurologists, and 20 physicians (internists). The videos included 37 series of epileptic seizures, eight series of physiologic nonepileptic events (PNEEs), and five series of psychogenic nonepileptic seizures (PNESs). After observing each video, the doctors chose the diagnosis of epileptic seizures or nonepileptic events for the patient; if the latter was chosen, they further chose the diagnosis of PNESs or PNEEs. The overall diagnostic accuracy rate for epileptic seizures and nonepileptic events increased from 0.614 to 0.660 after observations of all three seizures (p semiological diagnosis of seizures is greatly affected by the seizure type as well as the doctor's experience. Although the overall reliability is limited, it can be improved by observing more seizures. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer

    DEFF Research Database (Denmark)

    Beniczky, S.; Hjalgrim, Helle; Polster, T.

    2013-01-01

    Our objective was to assess the clinical reliability of a wrist-worn, wireless accelerometer sensor for detecting generalized tonic-clonic seizures (GTCS). Seventy-three consecutive patients (age 6-68 years; median 37 years) at risk of having GTCS and who were admitted to the long-term video-elec...

  6. A Realistic Seizure Prediction Study Based on Multiclass SVM.

    Science.gov (United States)

    Direito, Bruno; Teixeira, César A; Sales, Francisco; Castelo-Branco, Miguel; Dourado, António

    2017-05-01

    A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

  7. Generic Single-Channel Detection of Absence Seizures

    DEFF Research Database (Denmark)

    Petersen, Eline B.; Duun-Henriksen, Jonas; Mazzaretto, Andrea

    2011-01-01

    is obtained for the derivation F7-FP1. Using this channel a sensitivity of 99.1 %, positive predictive value of 94.8 %, mean detection latency of 3.7 s, and false detection rate value of 0.5/h was obtained. The topographical visualization of the results clearly shows that the frontal, midline, and parietal...

  8. Detection of Paroxysms in Long-Term, Single-Channel EEG-Monitoring of Patients with Typical Absence Seizures

    Science.gov (United States)

    Kjaer, Troels W.; Sorensen, Helge B. D.; Groenborg, Sabine; Pedersen, Charlotte R.

    2017-01-01

    Absence seizures are associated with generalized 2.5–5 Hz spike-wave discharges in the electroencephalogram (EEG). Rarely are patients, parents, or physicians aware of the duration or incidence of seizures. Six patients were monitored with a portable EEG-device over four times 24 h to evaluate how easily outpatients are monitored and how well an automatic seizure detection algorithm can identify the absences. Based on patient-specific modeling, we achieved a sensitivity of 98.4% with only 0.23 false detections per hour. This yields a clinically satisfying performance with a positive predictive value of 87.1%. Portable EEG-recorders identifying paroxystic events in epilepsy outpatients are a promising tool for patients and physicians dealing with absence epilepsy. Albeit the small size of the EEG-device, some children still complained about the obtrusive nature of the device. We aim at developing less obtrusive though still very efficient devices, e.g., hidden in the ear canal or below the skin. PMID:29018634

  9. Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search.

    Science.gov (United States)

    Behnam, Morteza; Pourghassem, Hossein

    2016-08-01

    Epileptic seizure prediction using EEG signal analysis is an important application for drug therapy and pediatric patient monitoring. Time series estimation to obtain the future samples of EEG signal has vital role for detecting seizure attack. In this paper, a novel density-based real-time seizure prediction algorithm based on a trained offline seizure detection algorithm is proposed. In the offline seizure detection procedure, after signal preprocessing, histogram-based statistical features are extracted from signal probability distribution. By defining a deterministic polynomial model on the normalized histogram, a novel syntactic feature that is named Interpolated Histogram Feature (IHF) is proposed. Moreover, with this feature, Seizure Distribution Model (SDM) as a descriptor of the seizure and non-seizure signals is presented. By using a novel hybrid optimization algorithm based on Bayesian classifier and Hunting Search (HuS) algorithm, the optimal features are selected. To detect the seizure attacks in the online mode, a Multi-Layer Perceptron (MLP) classifier is trained with the optimal features in the offline procedure. For online prediction, the enhanced Recursive Least Square (RLS) filter is applied to estimate sample-by-sample of the EEG signal. Also, a density-based signal tracking scenario is introduced to update and tune the parameters of RLS filtering algorithm. Our prediction algorithm is evaluated on 104 hours of EEG signals recorded from 23 pediatric patients. Our online signal prediction algorithm provides the accuracy rate of 86.56% and precision rate of 86.53% simultaneously using the trained MLP classifier from the offline mode. The recall rate of seizure prediction is 97.27% and the false prediction rate of 0.00215 per hour is achieved as well. Ultimately, the future samples of EEG signal are estimated, and the time of seizure signal prediction is also converged to 6.64 seconds. In our proposed real-time algorithm, by implementing a density-based

  10. Hyperventilation and photic stimulation are useful additions to a placebo-based suggestive seizure induction protocol in patients with psychogenic nonepileptic seizures.

    Science.gov (United States)

    Popkirov, Stoyan; Grönheit, Wenke; Wellmer, Jörg

    2015-05-01

    The early and definitive diagnosis of psychogenic nonepileptic seizures is a common challenge in epileptology practice. Suggestive seizure induction is a valuable tool to aid the differentiation between epileptic and psychogenic nonepileptic seizures, especially when long-term video-EEG monitoring is inconclusive or unavailable. In this retrospective analysis, we compared the diagnostic yield of a classical, placebo-based induction protocol with that of an extended protocol that includes hyperventilation and photic stimulation as means of suggestion while also implementing more open, standardized patient information. We investigated whether the diversification of suggestive seizure induction has an effect on diagnostic yield and whether it preempts the administration of placebo. Data from 52 patients with confirmed psychogenic nonepileptic seizures were analyzed. While suggestive seizure induction using only placebo-based suggestion provoked a typical event in 13 of 20 patients (65%), the extended protocol was positive in 27 of 34 cases (84%); this improvement was not significant (p=0.11). Noninvasive suggestion techniques accounted for 78% of inductions, avoiding placebo administration in a majority of patients. Still, placebo remains an important part of suggestive seizure induction, responsible for 22% (6 out of 27) of successful inductions using our extended protocol. Our study demonstrates that the diversification of suggestive seizure induction is feasible and beneficial for both patients and diagnosticians. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy

    Science.gov (United States)

    Zhang, Honghui; Su, Jianzhong; Wang, Qingyun; Liu, Yueming; Good, Levi; Pascual, Juan M.

    2018-03-01

    This paper explores the internal dynamical mechanisms of epileptic seizures through quantitative modeling based on full brain electroencephalogram (EEG) signals. Our goal is to provide seizure prediction and facilitate treatment for epileptic patients. Motivated by an earlier mathematical model with incorporated synaptic plasticity, we studied the nonlinear dynamics of inherited seizures through a differential equation model. First, driven by a set of clinical inherited electroencephalogram data recorded from a patient with diagnosed Glucose Transporter Deficiency, we developed a dynamic seizure model on a system of ordinary differential equations. The model was reduced in complexity after considering and removing redundancy of each EEG channel. Then we verified that the proposed model produces qualitatively relevant behavior which matches the basic experimental observations of inherited seizure, including synchronization index and frequency. Meanwhile, the rationality of the connectivity structure hypothesis in the modeling process was verified. Further, through varying the threshold condition and excitation strength of synaptic plasticity, we elucidated the effect of synaptic plasticity to our seizure model. Results suggest that synaptic plasticity has great effect on the duration of seizure activities, which support the plausibility of therapeutic interventions for seizure control.

  12. Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy.

    Science.gov (United States)

    Zhang, Honghui; Su, Jianzhong; Wang, Qingyun; Liu, Yueming; Good, Levi; Pascual, Juan

    2018-03-01

    This paper explores the internal dynamical mechanisms of epileptic seizures through quantitative modeling based on full brain electroencephalogram (EEG) signals. Our goal is to provide seizure prediction and facilitate treatment for epileptic patients. Motivated by an earlier mathematical model with incorporated synaptic plasticity, we studied the nonlinear dynamics of inherited seizures through a differential equation model. First, driven by a set of clinical inherited electroencephalogram data recorded from a patient with diagnosed Glucose Transporter Deficiency, we developed a dynamic seizure model on a system of ordinary differential equations. The model was reduced in complexity after considering and removing redundancy of each EEG channel. Then we verified that the proposed model produces qualitatively relevant behavior which matches the basic experimental observations of inherited seizure, including synchronization index and frequency. Meanwhile, the rationality of the connectivity structure hypothesis in the modeling process was verified. Further, through varying the threshold condition and excitation strength of synaptic plasticity, we elucidated the effect of synaptic plasticity to our seizure model. Results suggest that synaptic plasticity has great effect on the duration of seizure activities, which support the plausibility of therapeutic interventions for seizure control.

  13. Epileptic Seizure Classification of EEGs Using Time-Frequency Analysis Based Multiscale Radial Basis Functions.

    Science.gov (United States)

    Li, Yang; Wang, Xu-Dong; Luo, Mei-Lin; Li, Ke; Yang, Xiao-Feng; Guo, Qi

    2018-03-01

    The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus, distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time-frequency representation in EEG signals is proposed by means of multiscale radial basis functions (MRBF) and a modified particle swarm optimization (MPSO) to improve both time and frequency resolution simultaneously, which is a novel MRBF-MPSO framework of the time-frequency feature extraction for epileptic EEG signals. The dimensionality of extracted features can be greatly reduced by the principle component analysis algorithm before the most discriminative features selected are fed into a support vector machine (SVM) classifier with the radial basis function (RBF) in order to separate epileptic seizure from seizure-free EEG signals. The classification performance of the proposed method has been evaluated by using several state-of-art feature extraction algorithms and other five different classifiers like linear discriminant analysis, and logistic regression. The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.

  14. Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations.

    Science.gov (United States)

    Malinowska, Urszula; Bergey, Gregory K; Harezlak, Jaroslaw; Jouny, Christophe C

    2015-08-01

    We investigate the relevance of high frequency oscillations (HFO) for biomarkers of epileptogenic tissue and indicators of preictal state before complex partial seizures in humans. We introduce a novel automated HFO detection method based on the amplitude and features of the HFO events. We examined intracranial recordings from 33 patients and compared HFO rates and characteristics between channels within and outside the seizure onset zone (SOZ). We analyzed changes of HFO activity from interictal to preictal and to ictal periods. The average HFO rate is higher for SOZ channels compared to non-SOZ channels during all periods. Amplitudes and durations of HFO are higher for events within the SOZ in all periods compared to non-SOZ events, while their frequency is lower. All analyzed HFO features increase for the ictal period. HFO may occur in all channels but their rate is significantly higher within SOZ and HFO characteristics differ from HFO outside the SOZ, but the effect size of difference is small. The present results show that based on accumulated dataset it is possible to distinguish HFO features different for SOZ and non-SOZ channels, and to show changes in HFO characteristics during the transition from interictal to preictal and to ictal periods. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  15. M Current-Based Therapies for Nerve Agent Seizures

    Science.gov (United States)

    2013-07-01

    seizures and status epilepticus. Ann Neurol. 2009; 65:326-336. 5. Todorovic, MS, Cowan, ML, Balint , CA, et al. Characterization of status...organophosphates in rats Marko S. Todorovic, Morgan L. Cowan, Corrinee A. Balint , Chengsan Sun, Jaideep Kapur ∗ Department of Neurology

  16. Combination of EEG Complexity and Spectral Analysis for Epilepsy Diagnosis and Seizure Detection

    Directory of Open Access Journals (Sweden)

    Wan-Lin Chang

    2010-01-01

    Full Text Available Approximately 1% of the world's population has epilepsy, and 25% of epilepsy patients cannot be treated sufficiently by any available therapy. If an automatic seizure-detection system was available, it could reduce the time required by a neurologist to perform an off-line diagnosis by reviewing electroencephalogram (EEG data. It could produce an on-line warning signal to alert healthcare professionals or to drive a treatment device such as an electrical stimulator to enhance the patient's safety and quality of life. This paper describes a systematic evaluation of current approaches to seizure detection in the literature. This evaluation was then used to suggest a reliable, practical epilepsy detection method. The combination of complexity analysis and spectrum analysis on an EEG can perform robust evaluations on the collected data. Principle component analysis (PCA and genetic algorithms (GAs were applied to various linear and nonlinear methods. The best linear models resulted from using all of the features without other processing. For the nonlinear models, applying PCA for feature reduction provided better results than applying GAs. The feasibility of executing the proposed methods on a personal computer for on-line processing was also demonstrated.

  17. Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value.

    Science.gov (United States)

    Elahian, Bahareh; Yeasin, Mohammed; Mudigoudar, Basanagoud; Wheless, James W; Babajani-Feremi, Abbas

    2017-10-01

    Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ). We computed the PLV between the phase of the amplitude of high gamma activity (80-150Hz) and the phase of lower frequency rhythms (4-30Hz) from ECoG recordings obtained from 10 patients with epilepsy (21 seizures). We extracted five features from the PLV and used a machine learning approach based on logistic regression to build a model that classifies electrodes as SOZ or non-SOZ. More than 96% of electrodes identified as the SOZ by our algorithm were within the resected area in six seizure-free patients. In four non-seizure-free patients, more than 31% of the identified SOZ electrodes by our algorithm were outside the resected area. In addition, we observed that the seizure outcome in non-seizure-free patients correlated with the number of non-resected SOZ electrodes identified by our algorithm. This machine learning approach, based on features extracted from the PLV, effectively identified electrodes within the SOZ. The approach has the potential to assist clinicians in surgical decision-making when pre-surgical intracranial recordings are utilized. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  18. Regularity and Matching Pursuit feature extraction for the detection of epileptic seizures.

    Science.gov (United States)

    Z-Flores, Emigdio; Trujillo, Leonardo; Sotelo, Arturo; Legrand, Pierrick; Coria, Luis N

    2016-06-15

    The neurological disorder known as epilepsy is characterized by involuntary recurrent seizures that diminish a patient's quality of life. Automatic seizure detection can help improve a patient's interaction with her/his environment, and while many approaches have been proposed the problem is still not trivially solved. In this work, we present a novel methodology for feature extraction on EEG signals that allows us to perform a highly accurate classification of epileptic states. Specifically, Hölderian regularity and the Matching Pursuit algorithm are used as the main feature extraction techniques, and are combined with basic statistical features to construct the final feature sets. These sets are then delivered to a Random Forests classification algorithm to differentiate between epileptic and non-epileptic readings. Several versions of the basic problem are tested and statistically validated producing perfect accuracy in most problems and 97.6% accuracy on the most difficult case. A comparison with recent literature, using a well known database, reveals that our proposal achieves state-of-the-art performance. The experimental results show that epileptic states can be accurately detected by combining features extracted through regularity analysis, the Matching Pursuit algorithm and simple time-domain statistical analysis. Therefore, the proposed method should be considered as a promising approach for automatic EEG analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. A signal processing based analysis and prediction of seizure onset in patients with epilepsy

    Science.gov (United States)

    Namazi, Hamidreza; Kulish, Vladimir V.

    2016-01-01

    One of the main areas of behavioural neuroscience is forecasting the human behaviour. Epilepsy is a central nervous system disorder in which nerve cell activity in the brain becomes disrupted, causing seizures or periods of unusual behaviour, sensations and sometimes loss of consciousness. An estimated 5% of the world population has epileptic seizure but there is not any method to cure it. More than 30% of people with epilepsy cannot control seizure. Epileptic seizure prediction, refers to forecasting the occurrence of epileptic seizures, is one of the most important but challenging problems in biomedical sciences, across the world. In this research we propose a new methodology which is based on studying the EEG signals using two measures, the Hurst exponent and fractal dimension. In order to validate the proposed method, it is applied to epileptic EEG signals of patients by computing the Hurst exponent and fractal dimension, and then the results are validated versus the reference data. The results of these analyses show that we are able to forecast the onset of a seizure on average of 25.76 seconds before the time of occurrence. PMID:26586477

  20. What makes a good home-based nocturnal seizure detector? A value sensitive design

    NARCIS (Netherlands)

    Van Andel, Judith; Leijten, Frans; Van Delden, Hans; van Thiel, Ghislaine

    2015-01-01

    A device for the in-home detection of nocturnal seizures is currently being developed in the Netherlands, to improve care for patients with severe epilepsy. It is recognized that the design of medical technology is not value neutral: perspectives of users and developers are influential in design,

  1. Epileptic seizure prediction based on a bivariate spectral power methodology.

    Science.gov (United States)

    Bandarabadi, Mojtaba; Teixeira, Cesar A; Direito, Bruno; Dourado, Antonio

    2012-01-01

    The spectral power of 5 frequently considered frequency bands (Alpha, Beta, Gamma, Theta and Delta) for 6 EEG channels is computed and then all the possible pairwise combinations among the 30 features set, are used to create a 435 dimensional feature space. Two new feature selection methods are introduced to choose the best candidate features among those and to reduce the dimensionality of this feature space. The selected features are then fed to Support Vector Machines (SVMs) that classify the cerebral state in preictal and non-preictal classes. The outputs of the SVM are regularized using a method that accounts for the classification dynamics of the preictal class, also known as "Firing Power" method. The results obtained using our feature selection approaches are compared with the ones obtained using minimum Redundancy Maximum Relevance (mRMR) feature selection method. The results in a group of 12 patients of the EPILEPSIAE database, containing 46 seizures and 787 hours multichannel recording for out-of-sample data, indicate the efficiency of the bivariate approach as well as the two new feature selection methods. The best results presented sensitivity of 76.09% (35 of 46 seizures predicted) and a false prediction rate of 0.15(-1).

  2. First seizure: EEG and neuroimaging following an epileptic seizure.

    Science.gov (United States)

    Pohlmann-Eden, Bernd; Newton, Mark

    2008-01-01

    An early EEG (within 48 h) and high-resolution magnetic resonance imaging (hr_MRI) are the methods of choice for an accurate diagnosis after a first seizure presentation. Together with a careful history and examination, they will allow definition of the epilepsy syndrome in two-thirds of patients and help assess the individual risk for seizure recurrence, which is determined by the specific syndrome and is highest with focal epileptiform activity on EEG. Despite the heterogeneity of first seizure studies, EEG and etiology are consistently found to be the best predictors for seizure recurrence and prognosis. The additional yield of sleep-deprived EEG and sleep EEG is uncertain; yet MRI is essential for detecting brain tumors and other structural bases for new epilepsy. The rate occurrence of remote symptomatic seizures increases significantly with age and the most common etiology in the elderly with a first seizure is stroke; however, its exact relevance to epileptogenicity is yet to be defined. There is a striking lack of systematic studies using early EEG and hr_MRI in order to better characterize epileptogenic areas and elucidate the mechanisms of seizure provocation.

  3. Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models

    NARCIS (Netherlands)

    Luijtelaar, E.L.J.M. van; Luttjohann, A.K.; Makarov, V.V.; Maksimenko, V.A.; Koronovskii, A.A.; Hramov, A.E.

    2016-01-01

    BACKGROUND: Genetic rat models for childhood absence epilepsy have become instrumental in developing theories on the origin of absence epilepsy, the evaluation of new and experimental treatments, as well as in developing new methods for automatic seizure detection, prediction, and/or interference of

  4. Febrile Seizure Simulation

    Directory of Open Access Journals (Sweden)

    Victor Cisneros

    2017-01-01

    Full Text Available Audience: This simulation session is appropriate for medical students, community physicians, or residents in emergency medicine, neurology, pediatrics, or family medicine. Introduction: Febrile seizures are the most common form of seizures in childhood; they are thought to occur in 2-5% of all children.1-3 Febrile seizures are defined as a seizure in association with a febrile illness in children without a central nervous system infection, previous afebrile seizure, known brain disorder, or electrolyte abnormalities. 1,2 They typically occur between 6 months and 18 months of age though they can occur up to 5 years of age.3 Febrile seizures are categorized as: simple (generalized seizure lasting less than 15 minutes in a child aged 6 months to 5 years, and less than 1 in a 24 hour period or complex (a focal seizure or generalized seizure lasting greater than 15 minutes, or multiple seizures in a 24 hour period. 1,3 Treatment for febrile seizures is based on treating the underlying cause of the fever and giving reassurance and education to the parents.2 Mortality is extremely rare, and there is no difference in the patient’s cognitive abilities after a febrile seizure, even when the seizure is prolonged.1 Objectives: At the end of this simulation session, the learner will be able to: 1 discuss the management of febrile seizures 2 discuss when placement of an advanced airway is indicated in the management of a febrile seizure 3 list the risk factors for febrile seizures 4 prepare a differential diagnosis for the causes of febrile seizures 5 educate family members on febrile seizures. Methods: This educational session is a high-fidelity simulation.

  5. Rapidly Learned Identification of Epileptic Seizures from Sonified EEG

    Directory of Open Access Journals (Sweden)

    Psyche eLoui

    2014-10-01

    Full Text Available Sonification refers to a process by which data are converted into sound, providing an auditory alternative to visual display. Currently, the prevalent method for diagnosing seizures in epilepsy is by visually reading a patient’s electroencephalogram (EEG. However, sonification of the EEG data provides certain advantages due to the nature of human auditory perception. We hypothesized that human listeners will be able to identify seizures from EEGs using the auditory modality alone, and that accuracy of seizure identification will increase after a short training session. Here we describe an algorithm we have used to sonify EEGs of both seizure and non-seizure activity, followed by a training study in which subjects listened to short clips of sonified EEGs and determine whether each clip was of seizure or normal activity, both before and after a short training session. Results show that before training subjects performed at chance level in differentiating seizures vs. non-seizures, but there was a significant improvement of accuracy after the training session. After training, subjects successfully distinguished seizures from non-seizures using the auditory modality alone. Further analyses using signal detection theory demonstrated improvement in sensitivity and reduction in response bias as a result of training. This study demonstrates the potential of sonified EEGs to be used for the detection of seizures. Future studies will attempt to increase accuracy using novel training and sonification modifications, with the goals of managing, predicting, and ultimately controlling seizures using sonification as a possible biofeedback-based intervention for epilepsy.

  6. A novel real-time patient-specific seizure diagnosis algorithm based on analysis of EEG and ECG signals using spectral and spatial features and improved particle swarm optimization classifier.

    Science.gov (United States)

    Nasehi, Saadat; Pourghassem, Hossein

    2012-08-01

    This paper proposes a novel real-time patient-specific seizure diagnosis algorithm based on analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect seizure onset. In this algorithm, spectral and spatial features are selected from seizure and non-seizure EEG signals by Gabor functions and principal component analysis (PCA). Furthermore, four features based on heart rate acceleration are extracted from ECG signals to form feature vector. Then a neural network classifier based on improved particle swarm optimization (IPSO) learning algorithm is developed to determine an optimal nonlinear decision boundary. This classifier allows to adjust the parameters of the neural network classifier, efficiently. This algorithm can automatically detect the presence of seizures with minimum delay which is an important factor from a clinical viewpoint. The performance of the proposed algorithm is evaluated on a dataset consisting of 154 h records and 633 seizures from 12 patients. The results indicate that the algorithm can recognize the seizures with the smallest latency and higher good detection rate (GDR) than other presented algorithms in the literature. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Juvenile myoclonic epilepsy 25 years after seizure onset: a population-based study.

    Science.gov (United States)

    Camfield, Carol S; Camfield, Peter R

    2009-09-29

    To document the long-term evolution of juvenile myoclonic epilepsy (JME) in a population-based cohort. All patients developing JME by 16 years of age in Nova Scotia between 1977 and 1985 were contacted in 2006-2008. Twenty-four patients (17 women) had JME, 3.5% of all childhood-onset epilepsy. Age at first seizure was 10.4 +/- 4.3 years. We contacted 23 of 24 (96%) at a mean age of 36 +/- 4.8 years. All were initially treated with antiepileptic drugs (AEDs). At the end of a 25.8 +/- 2.4-year follow-up, 11 (48%) had discontinued treatment: 6 were seizure-free (without AEDs for 5-23 years), 3 had myoclonic seizures only (without AEDs for >18 years), and 2 continued with rare seizures. Convulsive status epilepticus occurred in 8 (36%) and 3 had intractable epilepsy. About 70% reported good satisfaction with their health, work, friendships, and social life (Likert scales). Despite 87% high school graduation, 31% were unemployed. Sixteen live with a partner, 7 alone. Nine received antidepressant medications. Ten women had > or =1 pregnancy and 4 men fathered a child. Eleven pregnancies (80%) were unplanned, outside of a stable relationship. At least 1 major unfavorable social outcome was noted in 76%. Our sample size is modest but the long follow-up and population-based sampling is unique. All seizure types in juvenile myoclonic epilepsy (JME) resolved in 17% and for 13%, only myoclonus persisted. Therefore, one-third of people with JME have troublesome seizures vanish and antiepileptic drug treatment is no longer needed. Depression, social isolation, unemployment, and social impulsiveness complicate the lives of many patients.

  8. Detecting interictal discharges in first seizure patients: ambulatory EEG or EEG after sleep deprivation?

    NARCIS (Netherlands)

    Geut, I.; Weenink, S.; Knottnerus, I.L.H.; van Putten, Michel J.A.M.

    2017-01-01

    Purpose Uncertainty about recurrence after a first unprovoked seizure is a significant psychological burden for patients, and motivates the need for diagnostic tools with high sensitivity and specificity to assess recurrence risk. As the sensitivity of a routine EEG after a first unprovoked seizure

  9. The delta between postoperative seizure freedom and persistence: Automatically detected focal slow waves after epilepsy surgery

    Directory of Open Access Journals (Sweden)

    Margit Schönherr

    2017-01-01

    Significance: The quantity of delta activity could be used as a diagnostic marker for recurrent seizures. The close relation to epileptic spike localizations and the resection volume of patients with successful second surgery imply involvement in seizure recurrence. This initial evidence suggests a potential application in the planning of second epilepsy surgery.

  10. Clinical course of untreated tonic-clonic seizures in childhood: prospective, hospital based study.

    NARCIS (Netherlands)

    C.A. van Donselaar (Cees); O.F. Brouwer (Oebele); A.T. Geerts (Ada); W.F.M. Arts (Willem Frans); H. Stroink (Hans); A.C.B. Peters (Boudewijn)

    1997-01-01

    textabstractTo assess declaration and acceleration in the disease process in the initial phase of epilepsy in children with new onset tonic-clonic seizures. STUDY DESIGN: Hospital based follow up study. SETTING: Two university hospitals, a general hospital, and a children's hospital in the

  11. Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM.

    Science.gov (United States)

    Janjarasjitt, Suparerk

    2017-10-01

    In this study, wavelet-based features of single-channel scalp EEGs recorded from subjects with intractable seizure are examined for epileptic seizure classification. The wavelet-based features extracted from scalp EEGs are simply based on detail and approximation coefficients obtained from the discrete wavelet transform. Support vector machine (SVM), one of the most commonly used classifiers, is applied to classify vectors of wavelet-based features of scalp EEGs into either seizure or non-seizure class. In patient-based epileptic seizure classification, a training data set used to train SVM classifiers is composed of wavelet-based features of scalp EEGs corresponding to the first epileptic seizure event. Overall, the excellent performance on patient-dependent epileptic seizure classification is obtained with the average accuracy, sensitivity, and specificity of, respectively, 0.9687, 0.7299, and 0.9813. The vector composed of two wavelet-based features of scalp EEGs provide the best performance on patient-dependent epileptic seizure classification in most cases, i.e., 19 cases out of 24. The wavelet-based features corresponding to the 32-64, 8-16, and 4-8 Hz subbands of scalp EEGs are the mostly used features providing the best performance on patient-dependent classification. Furthermore, the performance on both patient-dependent and patient-independent epileptic seizure classifications are also validated using tenfold cross-validation. From the patient-independent epileptic seizure classification validated using tenfold cross-validation, it is shown that the best classification performance is achieved using the wavelet-based features corresponding to the 64-128 and 4-8 Hz subbands of scalp EEGs.

  12. Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.

    Science.gov (United States)

    Alexandre Teixeira, César; Direito, Bruno; Bandarabadi, Mojtaba; Le Van Quyen, Michel; Valderrama, Mario; Schelter, Bjoern; Schulze-Bonhage, Andreas; Navarro, Vincent; Sales, Francisco; Dourado, António

    2014-05-01

    The ability of computational intelligence methods to predict epileptic seizures is evaluated in long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy, also known as refractory epilepsy. This extensive study in seizure prediction considers the 278 patients from the European Epilepsy Database, collected in three epilepsy centres: Hôpital Pitié-là-Salpêtrière, Paris, France; Universitätsklinikum Freiburg, Germany; Centro Hospitalar e Universitário de Coimbra, Portugal. For a considerable number of patients it was possible to find a patient specific predictor with an acceptable performance, as for example predictors that anticipate at least half of the seizures with a rate of false alarms of no more than 1 in 6 h (0.15 h⁻¹). We observed that the epileptic focus localization, data sampling frequency, testing duration, number of seizures in testing, type of machine learning, and preictal time influence significantly the prediction performance. The results allow to face optimistically the feasibility of a patient specific prospective alarming system, based on machine learning techniques by considering the combination of several univariate (single-channel) electroencephalogram features. We envisage that this work will serve as benchmark data that will be of valuable importance for future studies based on the European Epilepsy Database. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  13. Prehospital Care for the Adult and Pediatric Seizure Patient: Current Evidence Based Recommendations

    Directory of Open Access Journals (Sweden)

    Eric C. Silverman

    2017-04-01

    Full Text Available Introduction: We sought to develop evidence-based recommendations for the prehospital evaluation and treatment of adult and pediatric patients with a seizure and to compare these recommendations against the current protocol used by the 33 emergency medical services (EMS agencies in California. Methods: We performed a review of the evidence in the prehospital treatment of patients with a seizure, and then compared the seizure protocols of each of the 33 EMS agencies for consistency with these recommendations. We analyzed the type and route of medication administered, number of additional rescue doses permitted, and requirements for glucose testing prior to medication. The treatment for eclampsia and seizures in pediatric patients were analyzed separately. Results: Protocols across EMS Agencies in California varied widely. We identified multiple drugs, dosages, routes of administration, re-dosing instructions, and requirement for blood glucose testing prior to medication delivery. Blood glucose testing prior to benzodiazepine administration is required by 61% (20/33 of agencies for adult patients and 76% (25/33 for pediatric patients. All agencies have protocols for giving intramuscular benzodiazepines and 76% (25/33 have protocols for intranasal benzodiazepines. Intramuscular midazolam dosages ranged from 2 to 10 mg per single adult dose, 2 to 8 mg per single pediatric dose, and 0.1 to 0.2 mg/kg as a weight-based dose. Intranasal midazolam dosages ranged from 2 to 10 mg per single adult or pediatric dose, and 0.1 to 0.2 mg/kg as a weight-based dose. Intravenous/intrasosseous midazolam dosages ranged from 1 to 6 mg per single adult dose, 1 to 5 mg per single pediatric dose, and 0.05 to 0.1 mg/kg as a weight-based dose. Eclampsia is specifically addressed by 85% (28/33 of agencies. Forty-two percent (14/33 have a protocol for administering magnesium sulfate, with intravenous dosages ranging from 2 to 6 mg, and 58% (19/33 allow benzodiazepines to be

  14. Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

    Science.gov (United States)

    Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

    2014-07-01

    Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy.

  15. Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network

    Directory of Open Access Journals (Sweden)

    Rezvan Abbasi

    2017-08-01

    Full Text Available Electroencephalogram signals (EEG have always been used in medical diagnosis. Evaluation of the statistical characteristics of EEG signals is actually the foundation of all brain signal processing methods. Since the correct prediction of disease status is of utmost importance, the goal is to use those models that have minimum error and maximum reliability. In anautomatic epileptic seizure detection system, we should be able to distinguish between EEG signals before, during and after seizure. Extracting useful characteristics from EEG data can greatly increase the classification accuracy. In this new approach, we first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform(DWT and then we derive statistical characteristics such as maximum, minimum, average and standard deviation for each sub-band. A multilayer perceptron (MLPneural network was used to assess the different scenarios of healthy and seizure among the collected signal sets. In order to assess the success and effectiveness of the proposed method, the confusion matrix was used and its accuracy was achieved98.33 percent. Due to the limitations and obstacles in analyzing EEG signals, the proposed method can greatly help professionals experimentally and visually in the classification and diagnosis of epileptic seizures.

  16. Binge drinking during pregnancy and risk of seizures in childhood: a study based on the Danish National Birth Cohort

    DEFF Research Database (Denmark)

    Sun, Yuelian; Strandberg-Larsen, Katrine; Vestergaard, Mogens

    2009-01-01

    Seizures are often found in children with fetal alcohol syndrome, but it is not known whether binge drinking during pregnancy by nonalcoholic women is associated with an increased risk of seizure disorders in children. The authors conducted a population-based cohort study of 80,526 liveborn......, and febrile seizures was retrieved from the Danish National Hospital Register. Results showed that exposure to binge drinking episodes during pregnancy was not associated with an increased risk of seizure disorders in children, except for those exposed at 11-16 gestational weeks. These children had a 3...... singletons in the Danish National Birth Cohort (1996-2002). Information on maternal binge drinking (intake of > or = 5 drinks on a single occasion) was collected in 2 computer-assisted telephone interviews during pregnancy. Children were followed for up to 8 years. Information on neonatal seizures, epilepsy...

  17. Data-Driven Approaches for Computation in Intelligent Biomedical Devices: A Case Study of EEG Monitoring for Chronic Seizure Detection

    Directory of Open Access Journals (Sweden)

    Naveen Verma

    2011-04-01

    Full Text Available Intelligent biomedical devices implies systems that are able to detect specific physiological processes in patients so that particular responses can be generated. This closed-loop capability can have enormous clinical value when we consider the unprecedented modalities that are beginning to emerge for sensing and stimulating patient physiology. Both delivering therapy (e.g., deep-brain stimulation, vagus nerve stimulation, etc. and treating impairments (e.g., neural prosthesis requires computational devices that can make clinically relevant inferences, especially using minimally-intrusive patient signals. The key to such devices is algorithms that are based on data-driven signal modeling as well as hardware structures that are specialized to these. This paper discusses the primary application-domain challenges that must be overcome and analyzes the most promising methods for this that are emerging. We then look at how these methods are being incorporated in ultra-low-energy computational platforms and systems. The case study for this is a seizure-detection SoC that includes instrumentation and computation blocks in support of a system that exploits patient-specific modeling to achieve accurate performance for chronic detection. The SoC samples each EEG channel at a rate of 600 Hz and performs processing to derive signal features on every two second epoch, consuming 9 μJ/epoch/channel. Signal feature extraction reduces the data rate by a factor of over 40×, permitting wireless communication from the patient’s head while reducing the total power on the head by 14×.

  18. Viruses and febrile seizures

    NARCIS (Netherlands)

    Zeijl, J.H. van

    2004-01-01

    We conclude that viral infections are the main cause of febrile seizures, with an important role for influenza A, HHV-6 and HHV-7. We showed that several viral infections not only contribute to initial febrile seizures, but also to recurrences. Viruses could not be detected in the CSF of children

  19. Characterization of early partial seizure onset: frequency, complexity and entropy.

    Science.gov (United States)

    Jouny, Christophe C; Bergey, Gregory K

    2012-04-01

    A clear classification of partial seizures onset features is not yet established. Complexity and entropy have been very widely used to describe dynamical systems, but a systematic evaluation of these measures to characterize partial seizures has never been performed. Eighteen different measures including power in frequency bands up to 300 Hz, Gabor atom density (GAD), Higuchi fractal dimension (HFD), Lempel-Ziv complexity, Shannon entropy, sample entropy, and permutation entropy, were selected to test sensitivity to partial seizure onset. Intracranial recordings from 45 patients with mesial temporal, neocortical temporal and neocortical extratemporal seizure foci were included (331 partial seizures). GAD, Lempel-Ziv complexity, HFD, high frequency activity, and sample entropy were the most reliable measures to assess early seizure onset. Increases in complexity and occurrence of high-frequency components appear to be commonly associated with early stages of partial seizure evolution from all regions. The type of measure (frequency-based, complexity or entropy) does not predict the efficiency of the method to detect seizure onset. Differences between measures such as GAD and HFD highlight the multimodal nature of partial seizure onsets. Improved methods for early seizure detection may be achieved from a better understanding of these underlying dynamics. Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  20. Auricular Acupuncture May Suppress Epileptic Seizures via Activating the Parasympathetic Nervous System: A Hypothesis Based on Innovative Methods

    Directory of Open Access Journals (Sweden)

    Wei He

    2012-01-01

    Full Text Available Auricular acupuncture is a diagnostic and treatment system based on normalizing the body's dysfunction. An increasing number of studies have demonstrated that auricular acupuncture has a significant effect on inducing parasympathetic tone. Epilepsy is a neurological disorder consisting of recurrent seizures resulting from excessive, uncontrolled electrical activity in the brain. Autonomic imbalance demonstrating an increased sympathetic activity and a reduced parasympathetic activation is involved in the development and progress of epileptic seizures. Activation of the parasympathetic nervous system such as vagus nerve stimulation has been used for the treatment of intractable epilepsy. Here, we propose that auricular acupuncture may suppress epileptic seizures via activating the parasympathetic nervous system.

  1. Auricular Acupuncture May Suppress Epileptic Seizures via Activating the Parasympathetic Nervous System: A Hypothesis Based on Innovative Methods.

    Science.gov (United States)

    He, Wei; Rong, Pei-Jing; Li, Liang; Ben, Hui; Zhu, Bing; Litscher, Gerhard

    2012-01-01

    Auricular acupuncture is a diagnostic and treatment system based on normalizing the body's dysfunction. An increasing number of studies have demonstrated that auricular acupuncture has a significant effect on inducing parasympathetic tone. Epilepsy is a neurological disorder consisting of recurrent seizures resulting from excessive, uncontrolled electrical activity in the brain. Autonomic imbalance demonstrating an increased sympathetic activity and a reduced parasympathetic activation is involved in the development and progress of epileptic seizures. Activation of the parasympathetic nervous system such as vagus nerve stimulation has been used for the treatment of intractable epilepsy. Here, we propose that auricular acupuncture may suppress epileptic seizures via activating the parasympathetic nervous system.

  2. SCN8A Epileptic Encephalopathy: Detection of Fetal Seizures Guides Multidisciplinary Approach to Diagnosis and Treatment.

    Science.gov (United States)

    McNally, Melanie A; Johnson, Julia; Huisman, Thierry A; Poretti, Andrea; Baranano, Kristin W; Baschat, Ahmet A; Stafstrom, Carl E

    2016-11-01

    SCN8A mutations are rare and cause a phenotypically heterogeneous early onset epilepsy known as early infantile epileptic encephalopathy type 13 (EIEE13, OMIM #614558). There are currently no clear genotype-phenotype correlations to help guide patient counseling and management. We describe a patient with EIEE13 (de novo heterozygous pathogenic mutation in SCN8A - p.Ile240Val (ATT>GTT)) who presented prenatally with maternally reported intermittent, rhythmic movements that, when observed on ultrasound, were concerning for fetal seizures. Ultrasound also revealed abnormal developmental states. With maternal administration of levetiracetam, the rhythmic fetal movements stopped. After birth, the patient developed treatment-refractory multi-focal epilepsy confirmed by electroencephalogram. Neuroimaging revealed restricted diffusion in the superior cerebellar peduncles, a finding not reported previously in EIEE13. This is the first report of EIEE13 associated with clinical prenatal-onset seizures. Ultrasonography can be useful for identifying fetal seizures, which may be treatable in utero. Ideally, the clinical approach to fetal seizures should involve a multidisciplinary team spanning the pre- and postnatal course to expedite early diagnosis and optimize management, as illustrated by this patient. Copyright © 2016 Elsevier Inc. All rights reserved.

  3. Cause-specific mortality in adults with unprovoked seizures. A population-based incidence cohort study.

    Science.gov (United States)

    Rafnsson, V; Olafsson, E; Hauser, W A; Gudmundsson, G

    2001-10-01

    To determine the cause-specific mortality relative to that expected in a population-based incidence cohort of people with unprovoked seizures. The cohort comprises 224 inhabitants of Iceland first diagnosed as suffering from unprovoked seizures during a 5-year period from 1960 to 1964. The expected number of deaths was calculated by multiplying person-years of observation within 5-year age categories for each year from diagnosis through 1995 by cause-specific and sex-specific national death rates for those aged 20 years and above. The standardized mortality ratio (SMR) and 95% confidence intervals (95% CI) were calculated. All-cause mortality was increased among men (SMR 2.25, 95% CI 1.56-3.14) but not women (SMR 0.79, 95% CI 0.38-1.46). Among men, there were 8 deaths from accidents, poisoning and violence observed versus 2.82 expected (SMR 2.84, 95% CI 1.22-5.59) and 4 deaths from suicide versus 0.69 expected (SMR 5.80, 95% CI 1.56-14.84). All-cause mortality for men was still elevated after restriction of analysis to those with seizures of unknown etiology (SMR 1.73, 95% CI 1.05-2.67) with the excess deaths attributable to suicide (SMR 5.26, 95% CI 1.06-15.38). Both males and females with remote symptomatic unprovoked seizures had an increase in all-cause mortality due to excess mortality from all cancers, cerebrovascular disease and accidents. When compared with the age-, time-period- and gender-specific mortality in the general population, there is excess mortality in men but not women. The increased mortality for men is partly attributable to excess mortality from accidents and suicides. Copyright 2001 S. Karger AG, Basel

  4. Clinical features of poststroke epileptic seizures

    Directory of Open Access Journals (Sweden)

    T. V. Danilova

    2015-01-01

    Full Text Available Poststroke epileptic seizures are detected in 30–40% of patients over 60 years of age. Objective: to explore the clinical features of epileptic seizures in stroke, risk factors for their development to form the bases for prediction and elaboration of optimal therapy. Patients and methods. 468 patients with ischemic stroke were examined. A study group included 265 patients (176 men and 89 women aged 31–89 years with epileptic seizures; a control group comprised 203 non-epileptic patients (126 men and 77 women aged 31–91 years. The patients of both groups were matched for age, clinical characteristics, and pathogenetic subtypes of stroke. Instrumental examinations were performed in the attack-free interval. Neurological status was evaluated using the conventional procedure (the National Institute of Health Stroke Scale; brain magnetic resonance imaging (MRI, magnetic resonance angiography, electroencephalography, extraand transcranial duplex sound of cerebral vessels, by estimating the level and degree of stenosis and cerebrovascular responsiveness. Results and discussion. Focal seizures were noted to more frequently develop with a preponderance of simple partial seizures within the first 7 days of stroke, with neurological worsening in the acute period of the disease. Stroke in the left carotid and vertebrobasilar beds may provoke the development of early seizures. The cortical localization of ischemic foci and pre-stroke chronic brain ischemia with the signs of circulatory comorbidity in the anterior and posterior circulatory systems may be a risk factor of epileptic seizures. There was an association of the type of an epileptic seizure and the size of an ischemic focus, as evidenced by MRI, with a tendency towards the generalization of seizures in the extensive ischemic foci. A tendency toward the generalization of epileptic seizures was established in the development of stroke in the left carotid bed, as well as in critical stenoses and

  5. Quantifying motion in video recordings of neonatal seizures by feature trackers based on predictive block matching.

    Science.gov (United States)

    Karayiannis, N B; Sami, A; Frost, J D; Wise, M S; Mizrahi, E M

    2004-01-01

    This work introduces predictive block matching, a method developed to track motion in video by exploiting the advantages of block motion estimation and adaptive block matching. The proposed method relies on a pure translation motion model to estimate the displacement of a block between two successive video frames before initiating the search for the best match of the block tracked throughout the frame sequence. The search for the best match relies on adaptive block matching, which employs an update strategy based on Kalman filtering to account for the changing appearance of the block. Predictive block matching was used to extract motor activity signals from video recordings of neonatal seizures.

  6. Statistical validation of event predictors: A comparative study based on the field of seizure prediction

    International Nuclear Information System (INIS)

    Feldwisch-Drentrup, Hinnerk; Schulze-Bonhage, Andreas; Timmer, Jens; Schelter, Bjoern

    2011-01-01

    The prediction of events is of substantial interest in many research areas. To evaluate the performance of prediction methods, the statistical validation of these methods is of utmost importance. Here, we compare an analytical validation method to numerical approaches that are based on Monte Carlo simulations. The comparison is performed in the field of the prediction of epileptic seizures. In contrast to the analytical validation method, we found that for numerical validation methods insufficient but realistic sample sizes can lead to invalid high rates of false positive conclusions. Hence we outline necessary preconditions for sound statistical tests on above chance predictions.

  7. What makes a good home-based nocturnal seizure detector? A value sensitive design.

    Directory of Open Access Journals (Sweden)

    Judith van Andel

    Full Text Available A device for the in-home detection of nocturnal seizures is currently being developed in the Netherlands, to improve care for patients with severe epilepsy. It is recognized that the design of medical technology is not value neutral: perspectives of users and developers are influential in design, and design choices influence these perspectives. However, during development processes, these influences are generally ignored and value-related choices remain implicit and poorly argued for. In the development process of the seizure detector we aimed to take values of all stakeholders into consideration. Therefore, we performed a parallel ethics study, using "value sensitive design." Analysis of stakeholder communication (in meetings and e-mail messages identified five important values, namely, health, trust, autonomy, accessibility, and reliability. Stakeholders were then asked to give feedback on the choice of these values and how they should be interpreted. In a next step, the values were related to design choices relevant for the device, and then the consequences (risks and benefits of these choices were investigated. Currently the process of design and testing of the device is still ongoing. The device will be validated in a trial in which the identified consequences of design choices are measured as secondary endpoints. Value sensitive design methodology is feasible for the development of new medical technology and can help designers substantiate the choices in their design.

  8. What makes a good home-based nocturnal seizure detector? A value sensitive design.

    Science.gov (United States)

    van Andel, Judith; Leijten, Frans; van Delden, Hans; van Thiel, Ghislaine

    2015-01-01

    A device for the in-home detection of nocturnal seizures is currently being developed in the Netherlands, to improve care for patients with severe epilepsy. It is recognized that the design of medical technology is not value neutral: perspectives of users and developers are influential in design, and design choices influence these perspectives. However, during development processes, these influences are generally ignored and value-related choices remain implicit and poorly argued for. In the development process of the seizure detector we aimed to take values of all stakeholders into consideration. Therefore, we performed a parallel ethics study, using "value sensitive design." Analysis of stakeholder communication (in meetings and e-mail messages) identified five important values, namely, health, trust, autonomy, accessibility, and reliability. Stakeholders were then asked to give feedback on the choice of these values and how they should be interpreted. In a next step, the values were related to design choices relevant for the device, and then the consequences (risks and benefits) of these choices were investigated. Currently the process of design and testing of the device is still ongoing. The device will be validated in a trial in which the identified consequences of design choices are measured as secondary endpoints. Value sensitive design methodology is feasible for the development of new medical technology and can help designers substantiate the choices in their design.

  9. Temporal pole abnormalities detected by 3 T MRI in temporal lobe epilepsy due to hippocampal sclerosis: No influence on seizure outcome after surgery.

    Science.gov (United States)

    Casciato, Sara; Picardi, Angelo; D'Aniello, Alfredo; De Risi, Marco; Grillea, Giovanni; Quarato, Pier Paolo; Mascia, Addolorata; Grammaldo, Liliana G; Meldolesi, Giulio Nicolo'; Morace, Roberta; Esposito, Vincenzo; Di Gennaro, Giancarlo

    2017-05-01

    To assess the clinical significance of temporal pole abnormalities (temporopolar blurring, TB, and temporopolar atrophy, TA) detected by using 3 Tesla MRI in the preoperative workup in patients with temporal lobe epilepsy due to hippocampal sclerosis (TLE-HS) who underwent surgery. We studied 78 consecutive patients with TLE-HS who underwent surgery and were followed up for at least 2 years. Based on findings of pre-surgical 3 Tesla MRI, patients were subdivided in subgroups according to the presence of TB or TA. Subgroups were compared on demographic, clinical, neuropsychological data and seizure outcome. TB was found in 39 (50%) patients, while TA was found in 32 (41%) patients, always ipsilateral to HS, with a considerable degree of overlap (69%) between TB and TA (p=0.01). Patients with temporopolar abnormalities did not significantly differ from those without TB or TA with regard to sex, age, age of epilepsy onset, duration of epilepsy, history of febrile convulsions or birth complications, side of surgery, seizure frequency at surgery, presence of GTCSs, and, in particular, seizure outcome. On the other hand, TB patients show a less frequent family history of epilepsy (p<.05) while age at epilepsy onset showed a trend to be lower in the TB group (p=.09). Patients with temporopolar atrophy did not significantly differ from those without TA on any variable, except for age at epilepsy onset, which was significantly lower for the TA group (p<.05). History of birth complications and longer duration of epilepsy also showed a trend to be associated with TA (p=.08). Multivariate analysis corroborated the association between temporopolar abnormalities and absence of family history of epilepsy and history of birth complications. High-field 3 T MRI in the preoperative workup for epilepsy surgery confirms that temporopolar abnormalities are frequent findings in TLE-HS patients and may be helpful to lateralize the epileptogenic zone. Their presence did not influence

  10. Management of provoked seizure

    Directory of Open Access Journals (Sweden)

    Misra Usha

    2011-01-01

    Full Text Available A provoked seizure may be due to structural damage (resulting from traumatic brain injury, brain tumor, stroke, tuberculosis, or neurocysticercosis or due to metabolic abnormalities (such as alcohol withdrawal and renal or hepatic failure. This article is a part of the Guidelines for Epilepsy in India. This article reviews the problem of provoked seizure and its management and also provides recommendations based on currently available information. Seizure provoked by metabolic disturbances requires correction of the triggering factors. Benzodiazepines are recommended for treatment of seizure due to alcohol withdrawal; gabapentin for seizure seen in porphyria; and antiepileptic drugs (AED, that are not inducer of hepatic enzymes, in the seizures seen in hepatic dysfunction. In severe traumatic brain injury, with or without seizure, phenytoin (PHT may be given for 7 days. In ischemic or hemorrhagic stroke one may individualize the AED therapy. In cerebral venous sinus thrombosis (CVST, AED may be prescribed if there is seizure or computed tomographic (CT abnormalities or focal weakness; the treatment, in these cases, has to be continued for 1 year. Prophylactic AED is not recommended in cases of brain tumor and neurosurgical procedures and if patient is on an AED it can be stopped after 1 week.

  11. Maternal use of antibiotics and the risk of childhood febrile seizures: a Danish population-based cohort.

    Directory of Open Access Journals (Sweden)

    Jessica E Miller

    Full Text Available OBJECTIVE: In a large population-based cohort in Denmark to examine if maternal use of antibiotics during pregnancy, as a marker of infection, increases the risk of febrile seizures in childhood in a large population-based cohort in Denmark. METHODS: All live-born singletons born in Denmark between January 1, 1996 and September 25, 2004 and who were alive on the 90(th day of life were identified from the Danish National Birth Registry. Diagnoses of febrile seizures were obtained from the Danish National Hospital Register and maternal use of antibiotics was obtained from the National Register of Medicinal Product Statistics. Hazard ratios (HR and 95% confidence intervals (95% CI were estimated by Cox proportional hazard regression models. RESULTS: We followed 551,518 singletons for up to 5 years and identified a total of 21,779 children with a diagnosis of febrile seizures. Slightly increased hazard ratios were observed among most exposure groups when compared to the unexposed group, ex. HR 1.08 95% CI: 1.05-1.11 for use of any systemic antibiotic during pregnancy. CONCLUSION: We found weak associations between the use of pharmacologically different antibiotics during pregnancy and febrile seizures in early childhood which may indicate that some infections, or causes or effects of infections, during pregnancy could affect the fetal brain and induce susceptibility to febrile seizures.

  12. Febrile Seizures

    Science.gov (United States)

    ... chance that the child may be injured by falling or may choke on food or saliva in the mouth. Using proper first aid for seizures can help avoid these hazards. There is no evidence that short febrile seizures cause brain damage. Large studies have found that even children ...

  13. Auricular Acupuncture May Suppress Epileptic Seizures via Activating the Parasympathetic Nervous System: A Hypothesis Based on Innovative Methods

    OpenAIRE

    He, Wei; Rong, Pei-Jing; Li, Liang; Ben, Hui; Zhu, Bing; Litscher, Gerhard

    2012-01-01

    Auricular acupuncture is a diagnostic and treatment system based on normalizing the body's dysfunction. An increasing number of studies have demonstrated that auricular acupuncture has a significant effect on inducing parasympathetic tone. Epilepsy is a neurological disorder consisting of recurrent seizures resulting from excessive, uncontrolled electrical activity in the brain. Autonomic imbalance demonstrating an increased sympathetic activity and a reduced parasympathetic activation is inv...

  14. A fully-asynchronous low-power implantable seizure detector for self-triggering treatment.

    Science.gov (United States)

    Mirzaei, Marjan; Salam, Muhammad Tariqus; Nguyen, Dang K; Sawan, Mohamad

    2013-10-01

    In this paper, we present a new asynchronous seizure detector that is part of an implantable integrated device intended to identify electrographic seizure onset and trigger a focal treatment to block the seizure progression. The proposed system has a low-power front-end bioamplifier and a seizure detector with intelligent mechanism to reduce power dissipation. This system eliminates the unnecessary clock gating during normal neural activity monitoring mode and reduces power dissipation in the seizure detector; as a result, this device is suitable for long-term implantable applications. The proposed system includes analog and digital building blocks with programmable parameters for extracting electrographic seizure onset information from real-time EEG recordings. Sensitivity of the detector is enhanced by optimizing the variable parameters based on specific electrographic seizure onset activities of each patient. The detection algorithm was validated using Matlab tools and implemented in standard 0.13 μm CMOS process with total die area of 1.5 × 1.5 mm². The fabricated chip is validated offline using intracranial EEG recordings from two patients with refractory epilepsy. Total power consumption of the chip is 9 μW and average detection delay is 13.7 s after seizure onset, well before the onset of clinical manifestation. The proposed system achieves an accurate detection performance with 100% sensitivity and no false alarms during the analyses of 15 seizures and 19 non-seizure datasets.

  15. Computing network-based features from intracranial EEG time series data: Application to seizure focus localization.

    Science.gov (United States)

    Hao, Stephanie; Subramanian, Sandya; Jordan, Austin; Santaniello, Sabato; Yaffe, Robert; Jouny, Christophe C; Bergey, Gregory K; Anderson, William S; Sarma, Sridevi V

    2014-01-01

    The surgical resection of the epileptogenic zone (EZ) is the only effective treatment for many drug-resistant epilepsy (DRE) patients, but the pre-surgical identification of the EZ is challenging. This study investigates whether the EZ exhibits a computationally identifiable signature during seizures. In particular, we compute statistics of the brain network from intracranial EEG (iEEG) recordings and track the evolution of network connectivity before, during, and after seizures. We define each node in the network as an electrode and weight each edge connecting a pair of nodes by the gamma band cross power of the corresponding iEEG signals. The eigenvector centrality (EVC) of each node is tracked over two seizures per patient and the electrodes are ranked according to the corresponding EVC value. We hypothesize that electrodes covering the EZ have a signature EVC rank evolution during seizure that differs from electrodes outside the EZ. We tested this hypothesis on multi-channel iEEG recordings from 2 DRE patients who had successful surgery (i.e., seizures were under control with or without medications) and 1 patient who had unsuccessful surgery. In the successful cases, we assumed that the resected region contained the EZ and found that the EVC rank evolution of the electrodes within the resected region had a distinct "arc" signature, i.e., the EZ ranks first rose together shortly after seizure onset and then fell later during seizure.

  16. Febrile seizures

    Science.gov (United States)

    ... cry or moan. If standing, the child will fall. The child may vomit or bite their tongue. Sometimes, children ... of febrile seizures is not related to future risk of epilepsy. Children who would develop epilepsy anyway will sometimes have ...

  17. Dopey's seizure.

    Science.gov (United States)

    Dan, B; Christiaens, F

    1999-06-01

    Angelman syndrome is a neurogenetic condition namely characterized by developmental delay, virtual absence of expressive verbal language, peculiar organization of movement, seizures and happy demeanor. This syndrome has been recognized since 1965, but it seems that Walt Disney presented an original depiction of it in his first full-length animated film, including myoclonic jerks and an apparently generalized tonic-clonic seizure. Copyright 1999 BEA Trading Ltd.

  18. Envenomation Seizures.

    Science.gov (United States)

    Kharal, Ghulam Abbas; Darby, Richard Ryan; Cohen, Adam B

    2018-01-01

    Insect sting-related envenomation rarely produces seizures. We present a patient with confusion and seizures that began 24 hours after a yellow jacket (wasp) sting. Given the rapid onset and resolution of symptoms, as well as accompanying dermatological and orbital features, and the lack of any infectious or structural abnormalities identified, the toxic effect of the wasp venom (and related anaphylaxis reaction) was believed to be the cause of his presentation.

  19. Guidelines for seizure management in palliative care: Proposal for an updated clinical practice model based on a systematic literature review.

    Science.gov (United States)

    León Ruiz, M; Rodríguez Sarasa, M L; Sanjuán Rodríguez, L; Pérez Nieves, M T; Ibáñez Estéllez, F; Arce Arce, S; García-Albea Ristol, E; Benito-León, J

    2017-02-27

    Very little has been written on seizure management in palliative care (PC). Given this situation, and considering the forthcoming setting up of the Palliative Care Unit at our neurorehabilitation centre, the Clínica San Vicente, we decided to establish a series of guidelines on the use of antiepileptic drugs (AEDs) for handling seizures in PC. We conducted a literature search in PubMed to identify articles, recent manuals, and clinical practice guidelines on seizure management in PC published by the most relevant scientific societies. Clinical practice guidelines are essential to identify patients eligible for PC, manage seizures adequately, and avoid unnecessary distress to these patients and their families. Given the profile of these patients, we recommend choosing AEDs with a low interaction potential and which can be administered by the parenteral route, preferably intravenously. Diazepam and midazolam appear to be the most suitable AEDs during the acute phase whereas levetiracetam, valproic acid, and lacosamide are recommended for refractory cases and long-term treatment. These guidelines provide general recommendations that must be adapted to each particular clinical case. Nevertheless, we will require further well-designed randomised controlled clinical trials including large samples of patients eligible for PC to draft a consensus document recommending adequate, rational, and effective use of AEDs, based on a high level of evidence, in this highly complex area of medical care. Copyright © 2017 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.

  20. Automatic Detection of Childhood Absence Epilepsy Seizures: Toward a Monitoring Device

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Madsen, Rasmus E.; Remvig, Line S.

    2012-01-01

    long-term prognoses, balancing antiepileptic effects and side effects. The electroencephalographic appearance of paroxysms in childhood absence epilepsy is fairly homogeneous, making it feasible to develop patient-independent automatic detection. We implemented a state-of-the-art algorithm...

  1. Nonseizure SUDEP: Sudden unexpected death in epilepsy without preceding epileptic seizures.

    Science.gov (United States)

    Lhatoo, Samden D; Nei, Maromi; Raghavan, Manoj; Sperling, Michael; Zonjy, Bilal; Lacuey, Nuria; Devinsky, Orrin

    2016-07-01

    To describe the phenomenology of monitored sudden unexpected death in epilepsy (SUDEP) occurring in the interictal period where death occurs without a seizure preceding it. We report a case series of monitored definite and probable SUDEP where no electroclinical evidence of underlying seizures was found preceding death. Three patients (two definite and one probable) had SUDEP. They had a typical high SUDEP risk profile with longstanding intractable epilepsy and frequent generalized tonic-clonic seizures (GTCS). All patients had varying patterns of respiratory and bradyarrhythmic cardiac dysfunction with profound electroencephalography (EEG) suppression. In two patients, patterns of cardiorespiratory failure were similar to those seen in some patients in the Mortality in Epilepsy Monitoring Units Study (MORTEMUS). SUDEP almost always occur postictally, after GTCS and less commonly after a partial seizure. Monitored SUDEP or near-SUDEP cases without a seizure have not yet been reported in literature. When nonmonitored SUDEP occurs in an ambulatory setting without an overt seizure, the absence of EEG information prevents the exclusion of a subtle seizure. These cases confirm the existence of nonseizure SUDEP; such deaths may not be prevented by seizure detection-based devices. SUDEP risk in patients with epilepsy may constitute a spectrum of susceptibility wherein some are relatively immune, death occurs in others with frequent GTCS with one episode of seizure ultimately proving fatal, while in others still, death may occur even in the absence of a seizure. We emphasize the heterogeneity of SUDEP phenomena. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

  2. A retrospective population-based study on seizures related to childhood vaccination

    NARCIS (Netherlands)

    von Spiczak, Sarah; Helbig, Ingo; Drechsel-Baeuerle, Ursula; Muhle, Hiltrud; van Baalen, Andreas; van Kempen, Marjan J.; Lindhout, Dick; Scheffer, Ingrid E.; Berkovic, Samuel F.; Stephani, Ulrich; Keller-Stanislawski, Brigitte

    Purpose: Cases of severe childhood epilepsies in temporal association with vaccination have great impact on the acceptance of vaccination programs by parents and health care providers. However, little is known about the type and frequency of seizures and epilepsy syndromes following vaccination.

  3. Intraoperative seizures during craniotomy under general anesthesia.

    Science.gov (United States)

    Howe, John; Lu, Xiaoying; Thompson, Zoe; Peterson, Gordon W; Losey, Travis E

    2016-05-01

    An acute symptomatic seizure is a clinical seizure occurring at the time of or in close temporal association with a brain insult. We report an acute symptomatic seizure occurring during a surgical procedure in a patient who did not have a prior history of epilepsy and who did not have a lesion associated with an increased risk of epilepsy. To characterize the incidence and clinical features of intraoperative seizures during craniotomy under general anesthesia, we reviewed cases where continuous EEG was acquired during craniotomy. Records of 400 consecutive cases with propofol as general anesthesia during craniotomy were reviewed. Demographic data, indication for surgery, clinical history, history of prior seizures, duration of surgery and duration of burst suppression were recorded. Cases where seizures were observed were analyzed in detail. Two out of 400 patients experienced intraoperative seizures, including one patient who appeared to have an acute symptomatic seizure related to the surgical procedure itself and a second patient who experienced two seizures likely related to an underlying diagnosis of epilepsy. This is the first report of an acute symptomatic seizure secondary to a neurosurgical procedure. Overall, 0.5% of patients monitored experienced seizures, indicating that intraoperative seizures are rare, and EEG monitoring during craniotomies is of low yield in detecting seizures. Copyright © 2016. Published by Elsevier Ltd.

  4. Predicting epileptic seizures in advance.

    Directory of Open Access Journals (Sweden)

    Negin Moghim

    Full Text Available Epilepsy is the second most common neurological disorder, affecting 0.6-0.8% of the world's population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling, is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance.

  5. Rate based failure detection

    Science.gov (United States)

    Johnson, Brett Emery Trabun; Gamage, Thoshitha Thanushka; Bakken, David Edward

    2018-01-02

    This disclosure describes, in part, a system management component and failure detection component for use in a power grid data network to identify anomalies within the network and systematically adjust the quality of service of data published by publishers and subscribed to by subscribers within the network. In one implementation, subscribers may identify a desired data rate, a minimum acceptable data rate, desired latency, minimum acceptable latency and a priority for each subscription. The failure detection component may identify an anomaly within the network and a source of the anomaly. Based on the identified anomaly, data rates and or data paths may be adjusted in real-time to ensure that the power grid data network does not become overloaded and/or fail.

  6. A computational study of stimulus driven epileptic seizure abatement.

    Directory of Open Access Journals (Sweden)

    Peter Neal Taylor

    Full Text Available Active brain stimulation to abate epileptic seizures has shown mixed success. In spike-wave (SW seizures, where the seizure and background state were proposed to coexist, single-pulse stimulations have been suggested to be able to terminate the seizure prematurely. However, several factors can impact success in such a bistable setting. The factors contributing to this have not been fully investigated on a theoretical and mechanistic basis. Our aim is to elucidate mechanisms that influence the success of single-pulse stimulation in noise-induced SW seizures. In this work, we study a neural population model of SW seizures that allows the reconstruction of the basin of attraction of the background activity as a four dimensional geometric object. For the deterministic (noise-free case, we show how the success of response to stimuli depends on the amplitude and phase of the SW cycle, in addition to the direction of the stimulus in state space. In the case of spontaneous noise-induced seizures, the basin becomes probabilistic introducing some degree of uncertainty to the stimulation outcome while maintaining qualitative features of the noise-free case. Additionally, due to the different time scales involved in SW generation, there is substantial variation between SW cycles, implying that there may not be a fixed set of optimal stimulation parameters for SW seizures. In contrast, the model suggests an adaptive approach to find optimal stimulation parameters patient-specifically, based on real-time estimation of the position in state space. We discuss how the modelling work can be exploited to rationally design a successful stimulation protocol for the abatement of SW seizures using real-time SW detection.

  7. Localization of pediatric seizure semiology.

    Science.gov (United States)

    Vendrame, Martina; Zarowski, Marcin; Alexopoulos, Andreas V; Wyllie, Elaine; Kothare, Sanjeev V; Loddenkemper, Tobias

    2011-10-01

    The aim of this study was to evaluate the relationship between semiology of seizures in children and adolescents to the corresponding EEG localization. Charts of 225 consecutive pediatric epilepsy patients undergoing Video-EEG monitoring (VEM) over 2 years were reviewed. Seizure semiology recorded during VEM was classified according to ILAE seizure semiology terminology and EEG localization, and analyzed based on onset as defined by the EEG data (generalized, frontal, temporal, parietal, occipital or multilobar). A total of 1008 seizures were analyzed in 225 children (mean age 8.5 years, range 0-20), with 50% boys. Auras and seizures with automatisms arose predominantly from the temporal lobes (psemiologies relate to specific brain regions, with overlap between focal and generalized semiological seizure types, as identified electrographically. Semiology of seizures can provide important information for epilepsy localization, and should not be overlooked, especially in patients undergoing pre-surgical evaluation. Separation of clinical seizure description and EEG findings may be useful, in particular when only incomplete information is available. i.e. during the first office visit. Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  8. Epilepsy or seizures - discharge

    Science.gov (United States)

    ... this page: //medlineplus.gov/ency/patientinstructions/000128.htm Epilepsy or seizures - discharge To use the sharing features on this page, please enable JavaScript. You have epilepsy . People with epilepsy have seizures. A seizure is ...

  9. Seizures and Teens: Stress, Sleep, & Seizures

    Science.gov (United States)

    Shafer, Patricia Osborne

    2007-01-01

    Most parents are used to erratic sleep patterns and mood swings in their teenagers. When these occur in an adolescent with seizures, however, the parent may wonder if sleep and mood problems are related to seizures. Sorting out the cause and effects of sleep in an adolescent with seizures can be confusing. Since stress can be a contributor to both…

  10. Sustained consumption of cocoa-based dark chocolate enhances seizure-like events in the mouse hippocampus.

    Science.gov (United States)

    Cicvaric, Ana; Bulat, Tanja; Bormann, Daniel; Yang, Jiaye; Auer, Bastian; Milenkovic, Ivan; Cabatic, Maureen; Milicevic, Radoslav; Monje, Francisco J

    2018-03-01

    While the consumption of caffeine and cocoa has been associated with a variety of health benefits to humans, some authors have proposed that excessive caffeine intake may increase the frequency of epileptic seizures in humans and reduce the efficiency of antiepileptic drugs. Little is known, however, about the proconvulsant potential of the sustained, excessive intake of cocoa on hippocampal neural circuits. Using the mouse as an experimental model, we examined the effects of the chronic consumption of food enriched in cocoa-based dark chocolate on motor and mood-related behaviours as well as on the excitability properties of hippocampal neurons. Cocoa food enrichment did not affect body weights or mood-related behaviours but rather promoted general locomotion and improved motor coordination. However, ex vivo electrophysiological analysis revealed a significant enhancement in seizure-like population spike bursting at the neurogenic dentate gyrus, which was paralleled by a significant reduction in the levels of GABA-α1 receptors thus suggesting that an excessive dietary intake of cocoa-enriched food might alter some of the synaptic elements involved in epileptogenesis. These data invite further multidisciplinary research aiming to elucidate the potential deleterious effects of chocolate abuse on behaviour and brain hyperexcitability.

  11. Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

    Science.gov (United States)

    Lie, Octavian V; van Mierlo, Pieter

    2017-01-01

    The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

  12. Seizure semiology: value in identifying seizure origin.

    Science.gov (United States)

    Jan, Mohammed M S; Girvin, John P

    2008-03-01

    The diagnosis of epilepsy depends upon a number of factors, particularly detailed and accurate seizure history, or semiology. Other diagnostic data, consisting of electroencephalography, video-monitoring of the seizures, and magnetic resonance imaging, are important in any comprehensive epilepsy program, particularly with respect to lateralizing and localizing the seizure focus, if such a focus exists, and with respect to determining the type of seizure or seizure syndrome. The aim of this review is to present a survey of important semiologic characteristics of various seizures that provide the historian with observations, which help to lateralize and localize epileptic zones. Clinical semiology is the starting point of understanding a seizure disorder and making the diagnosis of epilepsy. While it may not provide unequivocal evidence of localization of the epileptic focus, nevertheless it usually directs subsequent investigations, whose concordance is necessary for the ultimate localization.

  13. Seizure semiology: an important clinical clue to the diagnosis of autoimmune epilepsy.

    Science.gov (United States)

    Lv, Rui-Juan; Ren, Hai-Tao; Guan, Hong-Zhi; Cui, Tao; Shao, Xiao-Qiu

    2018-02-01

    The purpose of this study is to analyze the seizure semiologic characteristics of patients with autoimmune epilepsy (AE) and describe the investigation characteristics of AE using a larger sample size. This observational retrospective case series study was conducted from a tertiary epilepsy center between May 2014 and March 2017. Cases of new-onset seizures were selected based on laboratory evidence of autoimmunity. At the same time, typical mesial temporal lobe epilepsy (MTLE) patients with hippocampal sclerosis (HS) were recruited as the control group from the subjects who underwent presurgical evaluation during the same period. A total of 61 patients with AE were identified. Specific autoimmune antibodies were detected in 39 patients (63.93%), including anti-VGKC in 23 patients (37.70%), anti-NMDA-R in 9 patients (14.75%), anti-GABA B -R in 6 patients (9.84%), and anti-amphiphysin in 1 patient (1.64%). Regarding the seizure semiology, no significant differences were noted between AE patients with autoantibody and patients with suspected AE without antibody. Compared to typical MTLE patients with HS, both AE patients with autoantibody and patients with suspected AE without antibody had the same seizure semiologic characteristics, including more frequent SPS or CPS, shorter seizure duration, rare postictal confusion, and common sleeping SGTC seizures. This study highlights important seizure semiologic characteristics of AE. Patients with autoimmune epilepsy had special seizure semiologic characteristics. For patients with autoimmune epilepsy presenting with new-onset seizures in isolation or with a seizure-predominant neurological disorder, the special seizure semiologic characteristics may remind us to test neuronal nuclear/cytoplasmic antibodies early and initiate immunomodulatory therapies as soon as possible. Furthermore, the absence of neural-specific autoantibodies does not rule out AE.

  14. Prevalence of SCN1A-related dravet syndrome among children reported with seizures following vaccination: a population-based ten-year cohort study.

    Science.gov (United States)

    Verbeek, Nienke E; van der Maas, Nicoline A T; Jansen, Floor E; van Kempen, Marjan J A; Lindhout, Dick; Brilstra, Eva H

    2013-01-01

    To determine the prevalence of Dravet syndrome, an epileptic encephalopathy caused by SCN1A-mutations, often with seizure onset after vaccination, among infants reported with seizures following vaccination. To determine differences in characteristics of reported seizures after vaccination in children with and without SCN1A-related Dravet syndrome. Data were reviewed of 1,269 children with seizures following immunization in the first two years of life, reported to the safety surveillance system of the Dutch national immunization program between 1 January 1997 and 31 December 2006. Selective, prospective follow-up was performed of children with clinical characteristics compatible with a diagnosis of Dravet syndrome. In 21.9% (n = 279) of children, a diagnosis of Dravet syndrome could not be excluded based on available clinical data (median age at follow-up 16 months). Additional follow-up data were obtained in 83.9% (n = 234) of these children (median age 8.5 years). 15 (1.2% of 1,269; 95%CI:0.6 to 1.8%) children were diagnosed with SCN1A-related Dravet syndrome. Of all reported seizures following vaccinations in the first year of life, 2.5% (95%CI:1.3 to 3.6%) were due to SCN1A-related Dravet syndrome, as were 5.9% of reported seizures (95%CI:3.1 to 8.7%) after 2(nd) or 3(rd) DTP-IPV-Hib vaccination. Seizures in children with SCN1A-related Dravet syndrome occurred more often with a body temperature below 38.5°C (57.9% vs. 32.6%, p = 0.020) and reoccurred more often after following vaccinations (26.7% vs. 4.0%, p = 0.003), than in children without a diagnosis of SCN1A-related Dravet Syndrome. Although Dravet syndrome is a rare genetic epilepsy syndrome, 2.5% of reported seizures following vaccinations in the first year of life in our cohort occurred in children with this disorder. Knowledge on the specific characteristics of vaccination-related seizures in this syndrome might promote early diagnosis and indirectly, public faith in vaccination safety.

  15. Prevalence of SCN1A-Related Dravet Syndrome among Children Reported with Seizures following Vaccination: A Population-Based Ten-Year Cohort Study

    Science.gov (United States)

    Verbeek, Nienke E.; van der Maas, Nicoline A. T.; Jansen, Floor E.; van Kempen, Marjan J. A.; Lindhout, Dick; Brilstra, Eva H.

    2013-01-01

    Objectives To determine the prevalence of Dravet syndrome, an epileptic encephalopathy caused by SCN1A-mutations, often with seizure onset after vaccination, among infants reported with seizures following vaccination. To determine differences in characteristics of reported seizures after vaccination in children with and without SCN1A-related Dravet syndrome. Methods Data were reviewed of 1,269 children with seizures following immunization in the first two years of life, reported to the safety surveillance system of the Dutch national immunization program between 1 January 1997 and 31 December 2006. Selective, prospective follow-up was performed of children with clinical characteristics compatible with a diagnosis of Dravet syndrome. Results In 21.9% (n = 279) of children, a diagnosis of Dravet syndrome could not be excluded based on available clinical data (median age at follow-up 16 months). Additional follow-up data were obtained in 83.9% (n = 234) of these children (median age 8.5 years). 15 (1.2% of 1,269; 95%CI:0.6 to 1.8%) children were diagnosed with SCN1A-related Dravet syndrome. Of all reported seizures following vaccinations in the first year of life, 2.5% (95%CI:1.3 to 3.6%) were due to SCN1A-related Dravet syndrome, as were 5.9% of reported seizures (95%CI:3.1 to 8.7%) after 2nd or 3rd DTP-IPV-Hib vaccination. Seizures in children with SCN1A-related Dravet syndrome occurred more often with a body temperature below 38.5°C (57.9% vs. 32.6%, p = 0.020) and reoccurred more often after following vaccinations (26.7% vs. 4.0%, p = 0.003), than in children without a diagnosis of SCN1A-related Dravet Syndrome. Conclusions Although Dravet syndrome is a rare genetic epilepsy syndrome, 2.5% of reported seizures following vaccinations in the first year of life in our cohort occurred in children with this disorder. Knowledge on the specific characteristics of vaccination-related seizures in this syndrome might promote early diagnosis and indirectly

  16. Prevalence of SCN1A-related dravet syndrome among children reported with seizures following vaccination: a population-based ten-year cohort study.

    Directory of Open Access Journals (Sweden)

    Nienke E Verbeek

    Full Text Available OBJECTIVES: To determine the prevalence of Dravet syndrome, an epileptic encephalopathy caused by SCN1A-mutations, often with seizure onset after vaccination, among infants reported with seizures following vaccination. To determine differences in characteristics of reported seizures after vaccination in children with and without SCN1A-related Dravet syndrome. METHODS: Data were reviewed of 1,269 children with seizures following immunization in the first two years of life, reported to the safety surveillance system of the Dutch national immunization program between 1 January 1997 and 31 December 2006. Selective, prospective follow-up was performed of children with clinical characteristics compatible with a diagnosis of Dravet syndrome. RESULTS: In 21.9% (n = 279 of children, a diagnosis of Dravet syndrome could not be excluded based on available clinical data (median age at follow-up 16 months. Additional follow-up data were obtained in 83.9% (n = 234 of these children (median age 8.5 years. 15 (1.2% of 1,269; 95%CI:0.6 to 1.8% children were diagnosed with SCN1A-related Dravet syndrome. Of all reported seizures following vaccinations in the first year of life, 2.5% (95%CI:1.3 to 3.6% were due to SCN1A-related Dravet syndrome, as were 5.9% of reported seizures (95%CI:3.1 to 8.7% after 2(nd or 3(rd DTP-IPV-Hib vaccination. Seizures in children with SCN1A-related Dravet syndrome occurred more often with a body temperature below 38.5°C (57.9% vs. 32.6%, p = 0.020 and reoccurred more often after following vaccinations (26.7% vs. 4.0%, p = 0.003, than in children without a diagnosis of SCN1A-related Dravet Syndrome. CONCLUSIONS: Although Dravet syndrome is a rare genetic epilepsy syndrome, 2.5% of reported seizures following vaccinations in the first year of life in our cohort occurred in children with this disorder. Knowledge on the specific characteristics of vaccination-related seizures in this syndrome might promote early diagnosis

  17. Predictors of acute symptomatic seizures after intracranial hemorrhage in infants.

    Science.gov (United States)

    Bansal, Seema; Kebede, Tewodros; Dean, Nathan P; Carpenter, Jessica L

    2014-10-01

    To determine the prevalence of acute symptomatic seizures in infants with supratentorial intracranial hemorrhage, to identify potential risk factors, and to determine the effect of acute seizures on long-term morbidity and mortality. Children less than 24 months with intracranial hemorrhage were identified from a neurocritical care database. All patients who received seizure prophylaxis beginning at admission were included in the study. Risk factors studied were gender, etiology, location of hemorrhage, seizure(s) on presentation, and the presence of parenchymal injury. Acute clinical and electrographic seizures were identified from hospital medical records. Subsequent development of late seizures was determined based on clinical information from patients' latest follow-up. Patients with idiopathic neonatal intracranial hemorrhage, premature infants, and those with prior history of seizures were excluded from analysis. Seventy-two infants met inclusion criteria. None. Forty percent of infants had acute symptomatic seizures. The prevalence was similar regardless of whether etiology of hemorrhage was traumatic or nontraumatic. Seizures on presentation and parenchymal injury were independent risk factors of acute seizures (p = 0.001 and p = 0.006, respectively). Younger children and women were also at higher risk (p Acute seizures were not predictive of mortality, but nearly twice as many patients with acute seizures developed late seizures when compared with those without. Electrographic seizures and parenchymal injury were also predictive of development of late seizures (p hemorrhage are at high risk for acute symptomatic seizures. This is regardless of the etiology of hemorrhage. Younger patients, women, patients with parenchymal injury, and patients presenting with seizure are most likely to develop acute seizures. Although the benefits of seizure prophylaxis have not been studied in this specific population, these results suggest that it is an important component

  18. Seizure disorders and epilepsy.

    Science.gov (United States)

    Ozuna, J

    2000-01-01

    Seizures are uncontrolled hypersynchronous electrical discharges of neurons in the brain that interfere with normal function. They are a symptom of an underlying disorder. Epilepsy is a condition of recurring seizures that do not have a reversible metabolic cause. Seizures can be confused with a variety of other conditions, so an understanding of seizure manifestations is crucial in making an accurate diagnosis. Drug therapy is the mainstay of epilepsy treatment, but surgery and vagal nerve stimulation are options for selected refractory cases. Psychosocial consequences of recurring seizures are often more significant to patients than the seizures themselves.

  19. Management Of Post Stroke Seizures

    Directory of Open Access Journals (Sweden)

    Kavian Ghandehari

    2017-02-01

    treatment for patients with post-stroke seizures is still a controversial issue. Prospective studies in the literature showed that immediate treatment after a first unprovoked seizure does not improve the long-term remission rate. However, because of the physical and psychological influences of recurrent seizures, prophylactic treatment should be considered after a first unprovoked event in an elderly person at high risk of recurrence, taking into consideration the individuality of the patient and a discussion with the patient and his/her family about the risks and benefits of both options latest studies regarding post-stroke seizure treatment showed that 'new-generation' drugs, such as lamotrigine, gabapentin and levetiracetam, in low doses would be reasonable. Although several studies suggest that seizures alter the functional recovery after a stroke, it remains difficult to determine whether or not the occurrence of a second seizure in an untreated stroke patient might hamper the overall outcome. However, repeated seizures and status epilepticus worsen the neurological and mental condition of stroke patienton The decision to initiate antiepileptic drug treatment after a first or a second post-stroke seizure should therefore be individualized, primarily based on the functional impact of the first seizure episode and the patient's preference. Several converging findings suggest that the majority of first-generation antiepileptic drugs, particularly phenytoin, are not the most appropriate choice in stroke patients because of their potential harmful impact on functional recovery and bone health, their suboptimal pharmacokinetic profile and interaction with anticoagulants or salicylates, their greater likelihood to be poorly tolerated, and the lack of level A evidence regarding their specific use in elderly patients. Among the new-generation drugs that do not interact with anticoagulants, antiplatelet agents, or bone health, lamotrigine and gabapentine are the only two drugs

  20. Automated extraction of temporal motor activity signals from video recordings of neonatal seizures based on adaptive block matching.

    Science.gov (United States)

    Karayiannis, Nicolaos B; Sami, Abdul; Frost, James D; Wise, Merrill S; Mizrahi, Eli M

    2005-04-01

    This paper presents an automated procedure developed to extract quantitative information from video recordings of neonatal seizures in the form of motor activity signals. This procedure relies on optical flow computation to select anatomical sites located on the infants' body parts. Motor activity signals are extracted by tracking selected anatomical sites during the seizure using adaptive block matching. A block of pixels is tracked throughout a sequence of frames by searching for the most similar block of pixels in subsequent frames; this search is facilitated by employing various update strategies to account for the changing appearance of the block. The proposed procedure is used to extract temporal motor activity signals from video recordings of neonatal seizures and other events not associated with seizures.

  1. Clinical characteristics of seizures associated with viral gastroenteritis in children.

    Science.gov (United States)

    Ueda, Hitoshi; Tajiri, Hitoshi; Kimura, Sadami; Etani, Yuri; Hosoi, Gaku; Maruyama, Tomoko; Noma, Haruyoshi; Kusumoto, Yoshio; Takano, Tomoko; Baba, Yoshiko; Nagai, Toshizaburo

    2015-01-01

    We analyzed the clinical features of seizures during gastroenteritis in children by comparing the norovirus and rotavirus pathogen, and the impact of fever, if present, during the seizure episodes. Retrospective analysis was performed on 293 consecutive pediatric patients admitted with viral gastroenteritis to Osaka General Hospital between November 2007 and May 2009. Eighteen patients developed seizures, 12 of whom were positive for norovirus and six for rotavirus, as revealed by antigen detection. Of these 18 seizure patients, eight presented without fever (the aFS group) and 10 presented with febrile episodes (FS group). Seizure patients in the rotavirus group (83%) were more likely to be febrile than those in the norovirus group (58%). Compared with the aFS group, 90% of patients in the FS group presented seizures at an early stage of gastroenteritis. The frequency of clustered seizures in the FS group was considerably higher than that of febrile seizures in general and was also as high as that of "convulsions with mild gastroenteritis (CwG)". All seizure patients, whether febrile or afebrile, presented with generalized tonic clonic seizures (GTCS), complex partial seizures (CPS), or both. Diazepam (DZP) was less effective and carbamazepine (CBZ) was completely effective for the cessation of seizures in the FS group, similar to the drug response observed in CwG. The causative pathogen (norovirus or rotavirus) affected the frequency of febrile episodes during gastroenteritis, but fever had little effect on the clinical features of seizures. However, seizures occurred earlier during gastroenteritis in the FS group. On the whole, the clinical features of febrile seizures during viral gastroenteritis may closely resemble those of "convulsions with mild gastroenteritis" (CwG) than those of febrile seizures in general with respect to the frequency of clustered seizures and the antiepileptic drug responses and may have a pathogenic mechanism distinct from those of

  2. Comparison of the diagnostic accuracy of PET/MRI to PET/CT-acquired FDG brain exams for seizure focus detection: a prospective study

    Energy Technology Data Exchange (ETDEWEB)

    Paldino, Michael J.; Jones, Jeremy Y.; Mahmood, Nadia; Sher, Andrew; Hayatghaibi, Shireen; Seghers, Victor [Texas Children' s Hospital, Department of Radiology, Houston, TX (United States); Yang, Erica [SimonMed Imaging, Department of Radiology, Scottsdale, AZ (United States); Zhang, Wei [Texas Children' s Hospital, Outcomes and Impact Service, Houston, TX (United States); Krishnamurthy, Ramkumar [Nationwide Children' s Hospital, Department of Radiology, Columbus, OH (United States)

    2017-10-15

    There is great interest in positron emission tomography (PET)/magnetic resonance (MR) as a clinical tool due to its capacity to provide diverse diagnostic information in a single exam. The goal of this exam is to compare the diagnostic accuracy of PET/MR-acquired [F-18]2-fluoro-2-deoxyglucose (FDG) brain exams to that of PET/CT with respect to identifying seizure foci in children with localization-related epilepsy. Institutional Review Board approval and informed consent were obtained for this Health Insurance Portability and Accountability Act-compliant, prospective study. All patients referred for clinical FDG-PET/CT exams of the brain at our institution for a diagnosis of localization-related epilepsy were prospectively recruited to undergo an additional FDG-PET acquisition on a tandem PET/MR system. Attenuation-corrected FDG images acquired at PET/MR and PET/CT were interpreted independently by five expert readers. Readers were blinded to the scanner used for acquisition and attenuation correction as well as all other clinical and imaging data. A Likert scale scoring system (1-5) was used to assess image quality. The locale of seizure origin determined at multidisciplinary epilepsy surgery work rounds was considered the reference standard. Non-inferiority testing for paired data was used to compare the diagnostic accuracy of PET/MR to that of PET/CT. The final study population comprised 35 patients referred for a diagnosis of localization-related epilepsy (age range: 2-19 years; median: 11 years; 21 males, 14 females). Image quality did not differ significantly between the two modalities. The accuracy of PET/MR was not inferior to that of PET/CT for localization of a seizure focus (P=0.017). The diagnostic accuracy of FDG-PET images acquired on a PET/MR scanner and generated using MR-based attenuation correction was not inferior to that of PET images processed by traditional CT-based correction. (orig.)

  3. Breakthrough seizures after starting vilazodone for depression.

    Science.gov (United States)

    McKean, James; Watts, Hannah; Mokszycki, Robert

    2015-03-01

    Vilazodone is a new selective serotonin reuptake inhibitor (SSRI) and serotonin 5-HT1a partial agonist that is approved by the United States Food and Drug Administration to treat major depression. SSRI-induced seizures are rare and are more likely to be associated with larger doses and severe symptoms such as those present in serotonin syndrome. Several case reports have implicated SSRIs, buspirone, or the combination of these agents as the cause of seizures, but these reports were confounded with either coingestions or doses that exceeded FDA recommendations. We describe a 22-year-old woman with a history of seizure disorder who had been seizure free for the previous 8 years and experienced two breakthrough seizures shortly after starting vilazodone. Her dose of vilazodone had recently been titrated to 40 mg/day when she experienced the first seizure. She was instructed to taper vilazodone over the next several days, then discontinue the drug, and then follow up with her neurologist. Based on the patient's history, physical examination, and recent dose increase, it was plausible that vilazodone was the cause of the seizures. Use of the Naranjo adverse drug reaction probability scale indicated a possible relationship (score of 4) between her development of seizures and vilazodone therapy. The pharmacodynamics of this particular class of SSRI has both proconvulsive and anticonvulsive mechanisms. This is of particular concern in patients with a history of seizure disorder who are starting antidepressive therapy. In persons with epilepsy who are taking vilazodone and experience breakthrough seizures, practitioners should consider this drug as a potential cause of these seizures. Thus, until future research and experience with vilazodone can provide a definitive answer, clinicians should be cautious when prescribing this medication to treat depression in patients with a history of seizure disorder. © 2015 Pharmacotherapy Publications, Inc.

  4. Data mining for prospective early detection of safety signals in the Vaccine Adverse Event Reporting System (VAERS): a case study of febrile seizures after a 2010-2011 seasonal influenza virus vaccine.

    Science.gov (United States)

    Martin, David; Menschik, David; Bryant-Genevier, Marthe; Ball, Robert

    2013-07-01

    Reports of data mining results as an initial indication of a prospectively detected safety signal in the US Vaccine Adverse Event Reporting System (VAERS) have been limited. In April 2010 a vaccine safety signal for febrile seizures after Fluvax(®) and Fluvax(®) Junior was identified in Australia without the aid of data mining. In order to refine Northern Hemisphere influenza vaccine safety surveillance, VAERS data mining analyses based on vaccine brand name were initiated during the 2010-2011 influenza season. We describe the strategies that led to the finding of a novel safety signal using empirical Bayesian data mining. The primary US VAERS analysis calculated an empirical Bayesian geometric mean (EBGM), which was adjusted for age group, sex and year received. A secondary age-stratified analysis calculated a separate EBGM for 11 pre-defined age subsets. These bi-weekly analyses were generated with database restrictions that separated live and inactivated vaccines as well as with the US VAERS database. A cutoff of 2.0 at the fifth percentile of the confidence interval (CI) for the EBGM, the EB05, was used to identify vaccine adverse event combinations for further evaluation. Examination of potential interactions among concomitantly administered vaccines is based on the Interaction Signal Score (INTSS), which is a relative measure of how much excess disproportionality is present in the three-dimensional combination of two vaccines and one adverse event term. An INTSS >1 indicates that the CI for the three-dimensional analysis is larger than and does not overlap with the CI from the highest two-dimensional analysis. We subsequently examined the possibility of masking by removing all 2,095 Fluzone(®) 2010-2011 reports from the 10 December 2010 version of the VAERS database. In addition, we calculated relative reporting ratios to observe the relative contribution of adjustment and the Multi-Item Gamma Poisson Shrinker (MGPS) algorithm to EBGM values. On 10

  5. Non-imidazole-based histamine H3 receptor antagonists with anticonvulsant activity in different seizure models in male adult rats

    Directory of Open Access Journals (Sweden)

    Sadek B

    2016-11-01

    Full Text Available Bassem Sadek,1 Ali Saad,1 Gniewomir Latacz,2 Kamil Kuder,2 Agnieszka Olejarz,2 Tadeusz Karcz,2 Holger Stark,3 Katarzyna Kieć-Kononowicz2 1Department of Pharmacology and Therapeutics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates; 2Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland; 3Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical and Medicinal Chemistry, Heinrich Heine University, Düsseldorf, Germany Abstract: A series of twelve novel non-imidazole-based ligands (3–14 was developed and evaluated for its in vitro binding properties at the human histamine H3 receptor (hH3R. The novel ligands were investigated for their in vivo protective effects in different seizure models in male adult rats. Among the H3R ligands (3–14 tested, ligand 14 showed significant and dose-dependent reduction in the duration of tonic hind limb extension in maximal electroshock (MES-induced seizure model subsequent to acute systemic administration (5, 10, and 20 mg/kg, intraperitoneally, whereas ligands 4, 6, and 7 without appreciable protection in MES model were most promising in pentylenetetrazole (PTZ model. Moreover, the protective effect observed for ligand 14 in MES model was lower than that observed for the reference drug phenytoin and was entirely abrogated when rats were co-administered with the brain-penetrant H1R antagonist pyrilamine (PYR but not the brain-penetrant H2R antagonist zolantidine (ZOL, demonstrating that histaminergic neurotransmission by activation of postsynaptically located H1Rs seems to be involved in the protective action. On the contrary, PYR and ZOL failed to abrogate the full protection provided by 4 in PTZ model and the moderate protective effect by 14 in strychnine (STR model. Moreover, the experimental and in silico estimation of properties such as metabolism was

  6. Alternative therapies for seizures: promises and dangers.

    Science.gov (United States)

    Sirven, Joseph I

    2007-09-01

    Complementary and alternative medicine (CAM) is increasingly being used for a multitude of medical problems, one of them being seizures. This article discusses the prevalence of CAM use for seizures and epilepsy. Evidence-based data regarding CAM for epilepsy are presented as well as potential safety concerns regarding ephedra and cannabis use.

  7. Effect of Seizure Clustering on Epilepsy Outcome

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2008-05-01

    Full Text Available A prospective, long-term population-based study was performed to determine whether seizure clustering (3 or more afebrile seizures during a 24 hour period is associated with drug resistance and increased mortality in childhood-onset epilepsy, in a study at University of Turku, Finland, and the Epilepsy Research Group, Berlin, Germany.

  8. Athletes with seizure disorders.

    Science.gov (United States)

    Knowles, Byron Don; Pleacher, Michael D

    2012-01-01

    Individuals with seizure disorders have long been restricted from participation in certain sporting activities. Those with seizure disorders are more likely than their peers to have a sedentary lifestyle and to develop obesity. Regular participation in physical activity can improve both physical and psychosocial outcomes for persons with seizure disorders. Seizure activity often is reduced among those patients who regularly engage in aerobic activity. Recent literature indicates that the diagnosis of seizure disorders remains highly stigmatizing in the adolescent population. Persons with seizure disorders may be more accepted by peer groups if they are allowed to participate in sports and recreational activities. Persons with seizure disorders are encouraged to participate in regular aerobic activities. They may participate in team sports and contact or collision activities provided that they utilize appropriate protective equipment. There seems to be no increased risk of injury or increasing seizure activity as the result of such participation. Persons with seizure disorders still are discouraged from participating in scuba diving and skydiving. The benefits of participation in regular sporting activity far outweigh any risk to the athlete with a seizure disorder who chooses to participate in sports.

  9. Termination patterns of complex partial seizures: An intracranial EEG study.

    Science.gov (United States)

    Afra, Pegah; Jouny, Christopher C; Bergey, Gregory K

    2015-11-01

    While seizure onset patterns have been the subject of many reports, there have been few studies of seizure termination. In this study we report the incidence of synchronous and asynchronous termination patterns of partial seizures recorded with intracranial arrays. Data were collected from patients with intractable complex partial seizures undergoing presurgical evaluations with intracranial electrodes. Patients with seizures originating from mesial temporal and neocortical regions were grouped into three groups based on patterns of seizure termination: synchronous only (So), asynchronous only (Ao), or mixed (S/A, with both synchronous and asynchronous termination patterns). 88% of the patients in the MT group had seizures with a synchronous pattern of termination exclusively (38%) or mixed (50%). 82% of the NC group had seizures with synchronous pattern of termination exclusively (52%) or mixed (30%). In the NC group, there was a significant difference of the range of seizure durations between So and Ao groups, with Ao exhibiting higher variability. Seizures with synchronous termination had low variability in both groups. Synchronous seizure termination is a common pattern for complex partials seizures of both mesial temporal or neocortical onset. This may reflect stereotyped network behavior or dynamics at the seizure focus. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  10. Management of a First Seizure.

    Science.gov (United States)

    Bergey, Gregory K

    2016-02-01

    Assessment of the patient with a first seizure is a common and important neurologic issue. Less than 50% of patients who have a first unprovoked seizure have a second seizure; thus, the evaluation should focus on determining the patient's risk of seizure recurrence. A number of population studies, including some classic reports, have identified the relative risk factors for subsequent seizure recurrence. The 2014 update of the International League Against Epilepsy definition of epilepsy incorporates these findings, and in 2015, the American Academy of Neurology published a guideline that analyzed the available data. Provoked or acute symptomatic seizures do not confer increased risk for subsequent unprovoked seizure recurrence. Multiple seizures in a given 24-hour period do not increase the risk of seizure recurrence. Remote symptomatic seizures, an epileptiform EEG, a significant brain imaging abnormality, and nocturnal seizures are risk factors for seizure recurrence. Antiepileptic drug therapy delays the time to second seizure but may not influence long-term remission.

  11. Seizure semiology and aging.

    Science.gov (United States)

    Silveira, Diosely C; Jehi, Lara; Chapin, Jessica; Krishnaiengar, Suparna; Novak, Eric; Foldvary-Schaefer, Nancy; Najm, Imad

    2011-02-01

    The incidence of epilepsy is high in older individuals. However, epilepsy in the elderly may be underdiagnosed and undertreated because of diagnostic difficulties. The main goal of this study was to determine whether seizure semiology differs between older and younger adults with epilepsy in the outpatient setting. Fifty patients with focal epilepsy aged 55 years and older and 50 patients aged between 18 and 45 years were included. Review of medical records contained detailed seizure description. There were no differences in seizure semiology between groups, except that subtle perceptions of transient confusion were seen in older patients but not in younger patients (P=0.0028). Older patients had less generalized motor seizures, but the differences between groups did not reach significance (P=0.01). Older patients may present with subtle symptoms of seizures characterized by brief periods of confusion, which may contribute to greater difficulty diagnosing seizures in the elderly. Copyright © 2010 Elsevier Inc. All rights reserved.

  12. A model based approach in observing the activity of neuronal populations for the prediction of epileptic seizures

    International Nuclear Information System (INIS)

    Chong, M.S.; Nesic, D.; Kuhlmann, L.; Postoyan, R.; Varsavsky, A.; Cook, M.

    2010-01-01

    Full text: Epilepsy is a common neurological disease that affects 0.5-1 % of the world's population. In cases where known treatments cannot achieve complete recovery, seizure prediction is essential so that preventive measures can be undertaken to prevent resultant injury. The elcctroencephalogram (EEG) is a widely used diagnostic tool for epilepsy. However, the EEG does not provide a detailed view of the underlying seizure causing neuronal mechanisms. Knowing the dynamics of the neuronal population is useful because tracking the evolution of the neuronal mechanisms will allow us to track the brain's progression from interictal to ictal state. Wendling and colleagues proposed a parameterised mathematical model that represents the activity of interconnected neuronal populations. By modifying the parameters, this model is able to reproduce signals that are very similar to the real EEG depicting commonly observed patterns during interictal and ictal periods. The transition from non-seizure to seizure activity, as seen in the EEG. is hypothesised to be due to the impairment of inhibition. Using Wendling's model, we designed a deterministic nonlinear estimator to recover the average membrane potential of the neuronal populations from a single channel EEG signal. for any fixed and known parameter values. Our nonlinear estimator is analytically proven to asymptotically converge to the true state of the model and illustrated in simulations. We were able to computationally observe the dynamics of the three neuronal populations described in the model: excitatory, fast and slow inhibitory populations. This forms a first step towards the prediction of epileptic seiwres. (author)

  13. Febrile seizures and risk of schizophrenia

    DEFF Research Database (Denmark)

    Vestergaard, Mogens; Pedersen, Carsten Bøcker; Christensen, Jakob

    2005-01-01

    BACKGROUND: Febrile seizure is a benign condition for most children, but experiments in animals and neuroimaging studies in humans suggest that some febrile seizures may damage the hippocampus, a brain area of possible importance in schizophrenia. METHODS: A population-based cohort of all children...... with schizophrenia. A history of febrile seizures was associated with a 44% increased risk of schizophrenia [relative risk (RR)=1.44; 95% confidence interval (CI), 1.07-1.95] after adjusting for confounding factors. The association between febrile seizures and schizophrenia remained virtually unchanged when...... restricting the analyses to people with no history of epilepsy. A history of both febrile seizures and epilepsy was associated with a 204% increased risk of schizophrenia (RR=3.04; 95% CI, 1.36-6.79) as compared with people with no such history. CONCLUSIONS: We found a slightly increased risk of schizophrenia...

  14. Source and sink nodes in absence seizures.

    Science.gov (United States)

    Rodrigues, Abner C; Machado, Birajara S; Caboclo, Luis Otavio S F; Fujita, Andre; Baccala, Luiz A; Sameshima, Koichi

    2016-08-01

    As opposed to focal epilepsy, absence seizures do not exhibit a clear seizure onset zone or focus since its ictal activity rapidly engages both brain hemispheres. Yet recent graph theoretical analysis applied to absence seizures EEG suggests the cortical focal presence, an unexpected feature for this type of epilepsy. In this study, we explore the characteristics of absence seizure by classifying the nodes as to their source/sink natures via weighted directed graph analysis based on connectivity direction and strength estimation using information partial directed coherence (iPDC). By segmenting the EEG signals into relatively short 5-sec-long time windows we studied the evolution of coupling strengths from both sink and source nodes, and the network dynamics of absence seizures in eight patients.

  15. Can developmental venous anomalies cause seizures?

    Science.gov (United States)

    Dussaule, Claire; Masnou, Pascal; Nasser, Ghaïdaa; Archambaud, Frédérique; Cauquil-Michon, Cécile; Gagnepain, Jean-Paul; Bouilleret, Viviane; Denier, Christian

    2017-12-01

    Developmental venous anomalies (DVAs) are congenital anatomical variants of normal venous drainage of normal brain. Although DVAs are often discovered on the occasion of a seizure, their involvement in epilepsy is poorly studied. Our objective was to determine whether DVA can cause seizures, in the cases where there is no associated lesion, including no cavernoma or dysplasia. Based on clinical history, cerebral MRI, EEG recording, and 18 F-FDG PET, we report 4 patients with DVA revealed by seizures. The first patient had a convulsive seizure caused by a hemorrhagic infarction due to thrombosis of her DVA. The second patient had a left temporo-parietal DVA next to a nonspecific lesion, possibly a sequelae of a venous infarction. The last two patients disclosed an isolated and uncomplicated DVA with a concordant epileptic focus confirmed on ictal video EEG recording. We reviewed literature and identified 21 other published cases of seizures caused by complications of a DVA and 9 patients that may have a direct link between epilepsy and an isolated and uncomplicated DVA. Seizures are linked to a DVA in two main situations: presence of an associated epileptogenic lesion, such as cavernoma or dysplasia, and occurrence of a complication of the DVA. Before concluding that a seizure is caused by a DVA, it is essential to perform full MRI protocols to search them. It remains rare and uncertain that isolated and uncomplicated DVA can cause seizures. In this last situation, physiopathological processes are probably different in each patient.

  16. Value and limitations of seizure semiology in localizing seizure onset.

    Science.gov (United States)

    So, Elson L

    2006-08-01

    Seizure semiology has been the foundation of clinical diagnosis of seizure disorders. This article discusses the value and the limitations of behavioral features of seizure episodes in localizing seizure onset. Studies have shown that some semiologic features of seizures are highly accurate in the hemispheric lateralization and lobar localization of seizures. There is good agreement between blinded reviewers in lateralizing video-recorded seizures in temporal lobe and extratemporal lobe epilepsies. However, seizure semiology alone should not be used to determine the site of seizure onset. Each semiologic feature may falsely localize seizure onset. Seizure semiology in some patients may signify the site of seizure propagation rather than origination. Moreover, seizure semiology may not be as reliable in multifocal epilepsies as it is in unifocal epilepsies. Many semiologic features of seizures of adults are often missing in seizures of children. Seizure semiology should be analyzed and integrated with EEG and neuroimaging data to localize the seizure focus. A sample of the recorded seizures should be shown to the patient's relatives or friends to verify that it is representative of habitual seizures.

  17. Generalized tonic-clonic seizure

    Science.gov (United States)

    ... tonic-clonic seizures have vision, taste, smell, or sensory changes, hallucinations, or dizziness before the seizure. This ... longer (called the post-ictal state) Loss of memory (amnesia) about the seizure episode Headache Weakness of ...

  18. Epilepsy after Febrile Seizures

    DEFF Research Database (Denmark)

    Seinfeld, S. A.; Pellock, J M; Kjeldsen, Lone Marianne Juel

    2016-01-01

    Background A history of complex febrile seizures can increase the risk of epilepsy, but the role of genetic factors is unclear. This analysis evaluated the relationship between febrile seizures and epilepsy. Methods Information on the history of seizures was obtained by a questionnaire from twin...... epilepticus. There were 78 twins who developed epilepsy. The highest rate of epilepsy (22.2%) occurred in the febrile status epilepticus group. Concordance was highest in simple group. Conclusion A twin with febrile status epilepticus is at the highest risk of developing epilepsy, but simple febrile seizures...... and emotional burden. It is currently not possible to accurately identify which children will develop recurrent febrile seizures, epilepsy, or neuropsychological comorbidities. © 2016 Elsevier Inc. All rights reserved....

  19. Reliability of seizure semiology in patients with 2 seizure foci.

    Science.gov (United States)

    Rathke, Kevin M; Schäuble, Barbara; Fessler, A James; So, Elson L

    2011-06-01

    To determine whether seizure semiology is reliable in localizing and distinguishing seizures at 2 independent brain foci in the same patient. Two masked reviewers localized seizures from 2 foci by their clinical semiology and intracranial electroencephalograms (EEGs). Epilepsy monitoring unit of referral comprehensive epilepsy program. Seventeen consecutive patients (51 seizures) with sufficient video and intracranial EEG data were identified by reviewing medical records of 366 patients older than 10 years. The primary outcome measures were interobserver agreement between the 2 masked reviewers; the proportion of seizures localized by semiology; the proportion of localized seizures concordant with intracranial EEG localization; and comparison between concordant and nonconcordant seizures in latency of intracranial EEG seizure spread. Interobserver agreement was 41% (κ score, 0.16). Only 30 of 51 seizures (59%) were localized by seizure semiology. The focus localized by semiology was concordant with the location of intracranial EEG seizure onset in 16 of 30 seizures (53%). No significant difference was observed between concordant and nonconcordant seizures in relation to the speed with which the EEG discharge spread from the location of seizure onset to another lobar region (P = .09, Wilcoxon rank sum test). Clinical seizure semiology is not as useful as intracranial EEG in localizing seizure onset in patients with dual seizure foci.

  20. Pediatric intracerebral hemorrhage: acute symptomatic seizures and epilepsy.

    Science.gov (United States)

    Beslow, Lauren A; Abend, Nicholas S; Gindville, Melissa C; Bastian, Rachel A; Licht, Daniel J; Smith, Sabrina E; Hillis, Argye E; Ichord, Rebecca N; Jordan, Lori C

    2013-04-01

    electroencephalography may detect electrographic seizures in some subjects. Single remote symptomatic seizures occur in many, and development of epilepsy is estimated to occur in 13% of patients at 2 years. Elevated intracranial pressure requiring acute intervention is a risk factor for acute seizures after presentation, remote symptomatic seizures, and epilepsy.

  1. Febrile seizures and risk of schizophrenia

    DEFF Research Database (Denmark)

    Vestergaard, Mogens; Pedersen, Carsten Bøcker; Christensen, Jakob

    2005-01-01

    BACKGROUND: Febrile seizure is a benign condition for most children, but experiments in animals and neuroimaging studies in humans suggest that some febrile seizures may damage the hippocampus, a brain area of possible importance in schizophrenia. METHODS: A population-based cohort of all children...... restricting the analyses to people with no history of epilepsy. A history of both febrile seizures and epilepsy was associated with a 204% increased risk of schizophrenia (RR=3.04; 95% CI, 1.36-6.79) as compared with people with no such history. CONCLUSIONS: We found a slightly increased risk of schizophrenia...

  2. [Semiology and propagation of epileptic seizures].

    Science.gov (United States)

    Gellner, A-K; Fritsch, B

    2013-06-01

    The evaluation of episodic seizure-like symptoms is a common challenge in the neurologist's daily routine. The clinical signs (semiology) are the most important puzzle pieces to distinguish epileptic seizures from other episodic entities. Due to the often far-reaching health and social consequences of the diagnosis of epilepsy, the early and rigorous assessment of episodic symptoms by means of the patient history is important. This assessment is based on knowledge of the association of certain semiologies with epileptic syndromes and brain regions; however, certain limitations and pitfalls have to be considered. Typical propagation pathways of seizure activity determine the serial occurrence of semiological features and provide supplementary information.

  3. Evaluation of Seizure Attacks in Patients with Cerebrovascular Accident

    Directory of Open Access Journals (Sweden)

    Ebrahim Koochaki

    2013-04-01

    Full Text Available Background: The most common reason for seizure in elderly duration is the stroke. This study was conducted aiming to assess the frequency of seizure attack occurrence in those patients. Materials and Methods: This investigation was carried out through a cross-sectional method for one year on 330 patients admitted to the neurology ward as diagnosed with stroke. The required data was collected through the researcher-made questionnaire from the patients suffering from stoke which was diagnosed based on clinical findings, CT-Scan and MRI as required. Results: Among 330 patient suffering from stroke (162 men and 168 women, 48 cases (14.5% were suffering from seizure. Six percent of the patients had early seizure and another 8.5% had late seizure. Among 162 men suffering from the stroke, 32 ones were without seizures and 30 men were suffering the seizure. A number of 150 women out of total 168 ones suffering from the stroke, had no seizure and 18 others had seizures; frequency of seizure occurrence was more in male samples (p=0.044. In the people under 60 year, there were mostly early types of seizure (45% and in the age range above 60 year, it was mostly late type (89.3%. A 68.5% of the patients suffering from the seizure had experienced ischemic stroke. However, the frequency of seizure occurrence in the patients with hemorrhagic stroke was statistically greater (p=0.003. Conclusion: This examination showed that occurrence of seizure attacks in the people with stroke is 14.5% and it is seen more in the hemorrhagic strokes than ischemic ones. The frontoparietal area is the most common location involved and tonic clonic was the most common seizure in the patients suffering from it who have experienced the stroke

  4. Administrative management of the soldier with seizures.

    Science.gov (United States)

    Gunderson, C H

    1991-07-01

    Based on improvement in our understanding of the prognosis of young adults with new onset seizures, and cumulative experience with the rules in effect for the last 30 years, a substantial change in the regulations affecting the fitness and profiling of these soldiers has been made. In general, these liberalize retention and profiling, set limits on the duration of trials of duty, provide for fitness determinations in soldiers with pseudo-seizures, and specify when neurologic consultation is required.

  5. Epileptic Seizures Prediction Using Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Syed Muhammad Usman

    2017-01-01

    Full Text Available Epileptic seizures occur due to disorder in brain functionality which can affect patient’s health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures’ sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects.

  6. A novel metabolism-based phenotypic drug discovery platform in zebrafish uncovers HDACs 1 and 3 as a potential combined anti-seizure drug target.

    Science.gov (United States)

    Ibhazehiebo, Kingsley; Gavrilovici, Cezar; de la Hoz, Cristiane L; Ma, Shun-Chieh; Rehak, Renata; Kaushik, Gaurav; Meza Santoscoy, Paola L; Scott, Lucas; Nath, Nandan; Kim, Do-Young; Rho, Jong M; Kurrasch, Deborah M

    2018-01-24

    Despite the development of newer anti-seizure medications over the past 50 years, 30-40% of patients with epilepsy remain refractory to treatment. One explanation for this lack of progress is that the current screening process is largely biased towards transmembrane channels and receptors, and ignores intracellular proteins and enzymes that might serve as efficacious molecular targets. Here, we report the development of a novel drug screening platform that harnesses the power of zebrafish genetics and combines it with in vivo bioenergetics screening assays to uncover therapeutic agents that improve mitochondrial health in diseased animals. By screening commercially available chemical libraries of approved drugs, for which the molecular targets and pathways are well characterized, we were able to reverse-identify the proteins targeted by efficacious compounds and confirm the physiological roles that they play by utilizing other pharmacological ligands. Indeed, using an 870-compound screen in kcna1-morpholino epileptic zebrafish larvae, we uncovered vorinostat (Zolinza™; suberanilohydroxamic acid, SAHA) as a potent anti-seizure agent. We further demonstrated that vorinostat decreased average daily seizures by ∼60% in epileptic Kcna1-null mice using video-EEG recordings. Given that vorinostat is a broad histone deacetylase (HDAC) inhibitor, we then delineated a specific subset of HDACs, namely HDACs 1 and 3, as potential drug targets for future screening. In summary, we have developed a novel phenotypic, metabolism-based experimental therapeutics platform that can be used to identify new molecular targets for future drug discovery in epilepsy. © The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain.

  7. On the nature of seizure dynamics.

    Science.gov (United States)

    Jirsa, Viktor K; Stacey, William C; Quilichini, Pascale P; Ivanov, Anton I; Bernard, Christophe

    2014-08-01

    Seizures can occur spontaneously and in a recurrent manner, which defines epilepsy; or they can be induced in a normal brain under a variety of conditions in most neuronal networks and species from flies to humans. Such universality raises the possibility that invariant properties exist that characterize seizures under different physiological and pathological conditions. Here, we analysed seizure dynamics mathematically and established a taxonomy of seizures based on first principles. For the predominant seizure class we developed a generic model called Epileptor. As an experimental model system, we used ictal-like discharges induced in vitro in mouse hippocampi. We show that only five state variables linked by integral-differential equations are sufficient to describe the onset, time course and offset of ictal-like discharges as well as their recurrence. Two state variables are responsible for generating rapid discharges (fast time scale), two for spike and wave events (intermediate time scale) and one for the control of time course, including the alternation between 'normal' and ictal periods (slow time scale). We propose that normal and ictal activities coexist: a separatrix acts as a barrier (or seizure threshold) between these states. Seizure onset is reached upon the collision of normal brain trajectories with the separatrix. We show theoretically and experimentally how a system can be pushed toward seizure under a wide variety of conditions. Within our experimental model, the onset and offset of ictal-like discharges are well-defined mathematical events: a saddle-node and homoclinic bifurcation, respectively. These bifurcations necessitate a baseline shift at onset and a logarithmic scaling of interspike intervals at offset. These predictions were not only confirmed in our in vitro experiments, but also for focal seizures recorded in different syndromes, brain regions and species (humans and zebrafish). Finally, we identified several possible biophysical

  8. Temporal Lobe Seizure

    Science.gov (United States)

    ... pregnancy Temporal lobe seizure Symptoms & causes Diagnosis & treatment Advertisement Mayo Clinic does not endorse companies or products. ... a Job Site Map About This Site Twitter Facebook Google YouTube Pinterest Mayo Clinic is a not- ...

  9. Supplementary Sensorimotor Seizures

    OpenAIRE

    J Gordon Millichap

    1995-01-01

    The electroclinical and neuroimaging features, and response to antiepileptic drugs in 12 children with seizures involving the supplementary sensory motor area (SSMA) are reported from the British Columbia’s Children’s Hospital, Vancouver, BC, Canada.

  10. Seizure Disorders in Pregnancy

    Science.gov (United States)

    ... Can taking antiseizure medications during pregnancy harm my baby? • Should I stop taking my antiseizure medications during pregnancy? • What extra steps may my health care provider take when monitoring my pregnancy? • If I have a seizure disorder, ...

  11. Effect of Immunotherapy on Seizure Outcome in Patients with Autoimmune Encephalitis: A Prospective Observational Registry Study.

    Directory of Open Access Journals (Sweden)

    Jung-Ick Byun

    Full Text Available To evaluate the seizure characteristics and outcome after immunotherapy in adult patients with autoimmune encephalitis (AE and new-onset seizure.Adult (age ≥18 years patients with AE and new-onset seizure who underwent immunotherapy and were followed-up for at least 6 months were included. Seizure frequency was evaluated at 2-4 weeks and 6 months after the onset of the initial immunotherapy and was categorized as "seizure remission", "> 50% seizure reduction", or "no change" based on the degree of its decrease.Forty-one AE patients who presented with new-onset seizure were analysed. At 2-4 weeks after the initial immunotherapy, 51.2% of the patients were seizure free, and 24.4% had significant seizure reduction. At 6 months, seizure remission was observed in 73.2% of the patients, although four patients died during hospitalization. Rituximab was used as a second-line immunotherapy in 12 patients who continued to have seizures despite the initial immunotherapy, and additional seizure remission was achieved in 66.6% of them. In particular, those who exhibited partial response to the initial immunotherapy had a better seizure outcome after rituximab, with low adverse events.AE frequently presented as seizure, but only 18.9% of the living patients suffered from seizure at 6 months after immunotherapy. Aggressive immunotherapy can improve seizure outcome in patients with AE.

  12. Effect of Immunotherapy on Seizure Outcome in Patients with Autoimmune Encephalitis: A Prospective Observational Registry Study

    Science.gov (United States)

    Jung, Keun-Hwa; Sunwoo, Jun-Sang; Moon, Jangsup; Lim, Jung-Ah; Lee, Doo Young; Shin, Yong-Won; Kim, Tae-Joon; Lee, Keon-Joo; Lee, Woo-Jin; Lee, Han-Sang; Jun, Jinsun; Kim, Dong-Yub; Kim, Man-Young; Kim, Hyunjin; Kim, Hyeon Jin; Suh, Hong Il; Lee, Yoojin; Kim, Dong Wook; Jeong, Jin Ho; Choi, Woo Chan; Bae, Dae Woong; Shin, Jung-Won; Jeon, Daejong; Park, Kyung-Il; Jung, Ki-Young; Chu, Kon; Lee, Sang Kun

    2016-01-01

    Objective To evaluate the seizure characteristics and outcome after immunotherapy in adult patients with autoimmune encephalitis (AE) and new-onset seizure. Methods Adult (age ≥18 years) patients with AE and new-onset seizure who underwent immunotherapy and were followed-up for at least 6 months were included. Seizure frequency was evaluated at 2–4 weeks and 6 months after the onset of the initial immunotherapy and was categorized as “seizure remission”, “> 50% seizure reduction”, or “no change” based on the degree of its decrease. Results Forty-one AE patients who presented with new-onset seizure were analysed. At 2–4 weeks after the initial immunotherapy, 51.2% of the patients were seizure free, and 24.4% had significant seizure reduction. At 6 months, seizure remission was observed in 73.2% of the patients, although four patients died during hospitalization. Rituximab was used as a second-line immunotherapy in 12 patients who continued to have seizures despite the initial immunotherapy, and additional seizure remission was achieved in 66.6% of them. In particular, those who exhibited partial response to the initial immunotherapy had a better seizure outcome after rituximab, with low adverse events. Conclusion AE frequently presented as seizure, but only 18.9% of the living patients suffered from seizure at 6 months after immunotherapy. Aggressive immunotherapy can improve seizure outcome in patients with AE. PMID:26771547

  13. Fibromyalgia and seizures.

    Science.gov (United States)

    Tatum, William O; Langston, Michael E; Acton, Emily K

    2016-06-01

    The purpose of this case-matched study was to determine how frequently fibromyalgia is associated with different paroxysmal neurological disorders and explore the utility of fibromyalgia as a predictor for the diagnosis of psychogenic non-epileptic seizures. The billing diagnosis codes of 1,730 new, non-selected patient encounters were reviewed over a three-year period for an epileptologist in a neurology clinic to identify all patients with historical diagnoses of fibromyalgia. The frequency with which epileptic seizures, psychogenic non-epileptic seizures, and physiological non-epileptic events were comorbid with fibromyalgia was assessed. Age and gender case-matched controls were used for a between-group comparison. Wilcoxon tests were used to analyse interval data, and Chi-square was used to analyse categorical data (pFibromyalgia was retrospectively identified in 95/1,730 (5.5%) patients in this cohort. Females represented 95% of the fibromyalgia sample (age: 53 years; 95% CI: 57, 51). Forty-three percent of those with fibromyalgia had a non-paroxysmal, neurological primary clinical diagnosis, most commonly chronic pain. Paroxysmal events were present in 57% of fibromyalgia patients and 54% of case-matched controls. Among patients with fibromyalgia and paroxysmal disorders, 11% had epileptic seizures, 74% had psychogenic non-epileptic seizures, and 15% had physiological non-epileptic events, compared to case-matched controls with 37% epileptic seizures, 51% psychogenic non-epileptic events, and 12% physiological non-epileptic events (p = 0.009). Fibromyalgia was shown to be a predictor for the diagnosis of psychogenic non-epileptic seizures in patients with undifferentiated paroxysmal spells. However, our results suggest that the specificity and sensitivity of fibromyalgia as a marker for psychogenic non-epileptic seizures in a mixed general neurological population of patients is less than previously described.

  14. DOM Based XSS Detecting Method Based on Phantomjs

    Science.gov (United States)

    Dong, Ri-Zhan; Ling, Jie; Liu, Yi

    Because malicious code does not appear in html source code, DOM based XSS cannot be detected by traditional methods. By analyzing the causes of DOM based XSS, this paper proposes a detection method of DOM based XSS based on phantomjs. This paper uses function hijacking to detect dangerous operation and achieves a prototype system. Comparing with existing tools shows that the system improves the detection rate and the method is effective to detect DOM based XSS.

  15. Seizures Induced by Music

    Directory of Open Access Journals (Sweden)

    A. O. Ogunyemi

    1993-01-01

    Full Text Available Musicogenic epilepsy is a rare disorder. Much remains to be learned about the electroclinical features. This report describes a patient who has been followed at our institution for 17 years, and was investigated with long-term telemetered simultaneous video-EEG recordings. She began to have seizures at the age of 10 years. She experienced complex partial seizures, often preceded by elementary auditory hallucination and complex auditory illusion. The seizures occurred in relation to singing, listening to music or thinking about music. She also had occasional generalized tonic clonic seizures during sleep. There was no significant antecedent history. The family history was negative for epilepsy. The physical examination was unremarkable. CT and MRI scans of the brain were normal. During long-term simultaneous video-EEG recordings, clinical and electrographic seizure activities were recorded in association with singing and listening to music. Mathematical calculation, copying or viewing geometric patterns and playing the game of chess failed to evoke seizures.

  16. An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks.

    Science.gov (United States)

    Sareen, Sanjay; Sood, Sandeep K; Gupta, Sunil Kumar

    2016-11-01

    Epilepsy is one of the most common neurological disorders which is characterized by the spontaneous and unforeseeable occurrence of seizures. An automatic prediction of seizure can protect the patients from accidents and save their life. In this article, we proposed a mobile-based framework that automatically predict seizures using the information contained in electroencephalography (EEG) signals. The wireless sensor technology is used to capture the EEG signals of patients. The cloud-based services are used to collect and analyze the EEG data from the patient's mobile phone. The features from the EEG signal are extracted using the fast Walsh-Hadamard transform (FWHT). The Higher Order Spectral Analysis (HOSA) is applied to FWHT coefficients in order to select the features set relevant to normal, preictal and ictal states of seizure. We subsequently exploit the selected features as input to a k-means classifier to detect epileptic seizure states in a reasonable time. The performance of the proposed model is tested on Amazon EC2 cloud and compared in terms of execution time and accuracy. The findings show that with selected HOS based features, we were able to achieve a classification accuracy of 94.6 %.

  17. Multi-lane detection based on multiple vanishing points detection

    Science.gov (United States)

    Li, Chuanxiang; Nie, Yiming; Dai, Bin; Wu, Tao

    2015-03-01

    Lane detection plays a significant role in Advanced Driver Assistance Systems (ADAS) for intelligent vehicles. In this paper we present a multi-lane detection method based on multiple vanishing points detection. A new multi-lane model assumes that a single lane, which has two approximately parallel boundaries, may not parallel to others on road plane. Non-parallel lanes associate with different vanishing points. A biological plausibility model is used to detect multiple vanishing points and fit lane model. Experimental results show that the proposed method can detect both parallel lanes and non-parallel lanes.

  18. MMR Vaccination and Febrile Seizures

    DEFF Research Database (Denmark)

    Vestergaard, Mogens; Hviid, Anders; Madsen, Kreesten Meldgaard

    2004-01-01

    CONTEXT: The rate of febrile seizures increases following measles, mumps, and rubella (MMR) vaccination but it is unknown whether the rate varies according to personal or family history of seizures, perinatal factors, or socioeconomic status. Furthermore, little is known about the long-term outcome...... of febrile seizures following vaccination. OBJECTIVES: To estimate incidence rate ratios (RRs) and risk differences of febrile seizures following MMR vaccination within subgroups of children and to evaluate the clinical outcome of febrile seizures following vaccination. DESIGN, SETTING, AND PARTICIPANTS......: Incidence of first febrile seizure, recurrent febrile seizures, and subsequent epilepsy. RESULTS: A total of 439,251 children (82%) received MMR vaccination and 17,986 children developed febrile seizures at least once; 973 of these febrile seizures occurred within 2 weeks of MMR vaccination. The RR...

  19. Intravenous Carbamazepine for Adults With Seizures.

    Science.gov (United States)

    Vickery, P Brittany; Tillery, Erika E; DeFalco, Alicia Potter

    2018-03-01

    To review the pharmacology, pharmacokinetics, efficacy, safety, dosage and administration, potential drug-drug interactions, and place in therapy of the intravenous (IV) formulation of carbamazepine (Carnexiv) for the treatment of seizures in adult patients. A comprehensive PubMed and EBSCOhost search (1945 to August 2017) was performed utilizing the keywords carbamazepine, Carnexiv, carbamazepine intravenous, IV carbamazepine, seizures, epilepsy, and seizure disorder. Additional data were obtained from literature review citations, manufacturer's product labeling, and Lundbeck website as well as Clinicaltrials.gov and governmental sources. All English-language trials evaluating IV carbamazepine were analyzed for this review. IV carbamazepine is FDA approved as temporary replacement therapy for treatment of adult seizures. Based on a phase I trial and pooled data from 2 open-label bioavailability studies comparing oral with IV dosing, there was no noted indication of loss of seizure control in patients switched to short-term replacement antiepileptic drug therapy with IV carbamazepine. The recommended dose of IV carbamazepine is 70% of the patient's oral dose, given every 6 hours via 30-minute infusions. The adverse effect profile of IV carbamazepine is similar to that of the oral formulation, with the exception of added infusion-site reactions. IV carbamazepine is a reasonable option for adults with generalized tonic-clonic or focal seizures, previously stabilized on oral carbamazepine, who are unable to tolerate oral medications for up to 7 days. Unknown acquisition cost and lack of availability in the United States limit its use currently.

  20. [Seizures in neurofibromatosis. What is the risk?].

    Science.gov (United States)

    Drouet, A

    2011-12-01

    The prevalence and the type of seizures associated with neurofibromatosis 1 (NF1) and 2 (NF2) are not adequately characterized. NF1 has a birth incidence of one in 2500, and NF2 one in 25000. Seizures are an occasional complication in NF1 patients and there is no data for NF2 patients. Central nervous system tumors are always suspected, since NF1 and NF2 are caused by mutations in tumor suppressor gene controlling cell proliferation and differentiation. The aim of this article is to provide a synthetic overview about epilepsy associated with NF1 and NF2 based on published studies. In NF1, the type of seizures and their response to therapy are reported, the heterogeneity of etiology is also discussed. For NF2 patients, no specific data are available; the current knowledge comes from series of NF2 patients for which seizures has revealed the disease or from isolated case reports of tumors associated with seizures. Cryptogenic epilepsy without anatomic defect is likely to be related to NF1, while seizures seem to be secondary to leptomeningeal tumors (meningioma, meningioangiomatosis) in NF2 patients. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

  1. Potential fire detection based on Kalman-driven change detection

    CSIR Research Space (South Africa)

    Van Den Bergh, F

    2009-07-01

    Full Text Available A new active fire event detection algorithm for data collected with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, based on the extended Kalman filter, is introduced. Instead of using the observed temperatures of the spatial...

  2. Chiari 1 Malformation in a Child with Febrile Seizures, Parasomnias, and Sleep Apnea Syndrome

    Directory of Open Access Journals (Sweden)

    Marco Zaffanello

    2017-01-01

    Full Text Available Introduction. The type I is the most common Chiari malformation in children. In this condition, the lower part of the cerebellum, but not the brain stem, extends into the foramen magnum at the base of the skull leading to disturbances in cerebrospinal fluid circulation and to direct compression of nervous tissue. Case report. We describe a 4-year-old Caucasian female child with febrile seizures, headache, parasomnias, and a delay of speech. The child underwent a magnetic resonance imaging to investigate these neurological signs, disclosing a Chiari malformation type 1. The polysomnography showed a mild-moderate sleep-disordered breathing, increased number of central sleep apneas, and generalized spike waves at sleep onset. Conclusions. Seizures have been seldom described in CM1 patients. The main reasons for performing MRI in this case were frequent seizures, a delay of speech, and headache, leading to an unexpected diagnosis of CM1. Polysomnography detected a discrete SDB.

  3. Epileptic seizures in patients with a posterior circulation infarct

    Directory of Open Access Journals (Sweden)

    Yüksel Kaplan

    2014-08-01

    Full Text Available OBJECTIVE: The aim of this study was to investigate the frequency of seizures and the clinical features of patients with seizures related to a posterior circulation infarct (POCI. METHODS: We reviewed all ischemic stroke patients admitted to our clinic between January 2011 and January 2012. The patients’ database information was retrospectively analyzed. Fifty-five patients with a POCI were included in the study. We reviewed all patients with epileptic seizures related to a POCI. Age, gender, recurrent stroke, risk factors, etiology, radiographic localization, the seizure type and onset time, and the electroencephalographic findings of patients were evaluated. We excluded all patients who had precipitating conditions during seizures such as taking drugs, acid-base disturbances, electrolyte imbalance, and history of epilepsy. RESULTS: Seizures were observed in four patients (3 male, 1 female with a POCI related epileptic seizures (7.2%. The etiology of strokes was cardiac-embolic in 3 patients and vertebral artery dissection in 1 patient. Seizures occurred in 2 patients as presenting finding, in 1 patient within 7 days, and 1 patient within 28 days. Primary generalized tonic-clonic seizures occurred in 3 patients and simple partial seizures with secondary generalization in 1 patient. Three patients had cerebellum infarction at the left hemisphere. One patient had lateral medullary infarction at the right side. The electroencephalographic findings of patients were normal. CONCLUSION: Studies involving patients with seizures related to a POCI are novel and few in number. Three patients with seizure had cerebellum infarction. The cerebellum in these patients may contribute via different mechanisms over seizure activity.

  4. Seizure metaphors differ in patients' accounts of epileptic and psychogenic nonepileptic seizures.

    Science.gov (United States)

    Plug, Leendert; Sharrack, Basil; Reuber, Markus

    2009-05-01

    To increase understanding of the subjective symptomatology of seizure experiences and improve differential diagnosis by studying the seizure metaphors used by patients with (psychogenic) nonepileptic seizures (NES) and epilepsy. Twenty-one unselected patients taking part in this study were admitted for 48 h of video-EEG (electroenceophalography) observation because of uncertainty about the diagnosis. Eight were proven to have epilepsy, 13 to have psychogenic nonepileptic seizures (PNES). During their admission, patients were interviewed by a neurologist. A linguist blinded to the medical diagnosis identified and categorized all seizure metaphors in verbatim transcripts. Between-group comparisons and logistic regression analysis were carried out. Of 382 metaphors identified, 80.8% conceptualized seizures as an agent/force, event/situation, or space/place. Most patients used metaphors from all categories, but patients with epilepsy and PNES showed preferences for different metaphoric concepts (differences p = 0.009 to p = 0.039). Patients with epilepsy preferred metaphors depicting the seizure as an agent/force or event/situation. PNES patients more often used metaphors of space/place. Logistic regression analyses predicted the diagnosis of PNES or epilepsy correctly in 85.7% of cases (based on different metaphor types in the each category) or 81.0% (based on all metaphor tokens). Patients with epilepsy and PNES have different preferences in the metaphoric conceptualization of their seizures. Epileptic seizures are described as a more external, self-directed entity than PNES, which are depicted as a state or place patients go through. The differentiating value of metaphoric conceptualizations suggests that metaphor preference could form the basis of future diagnostic questionnaires or other diagnostic tools.

  5. Psychogenic non-epileptic seizures: our video-EEG experience.

    Science.gov (United States)

    Nežádal, Tomáš; Hovorka, Jiří; Herman, Erik; Němcová, Iveta; Bajaček, Michal; Stichová, Eva

    2011-09-01

    The aim of our study was to assess the number of psychogenic non-epileptic seizures (PNES) in our patients with a refractory seizure disorder, to determine the 'typical' PNES semiology using video-EEG monitoring and describe other PNES parameters. We evaluated prospectively 596 patients with pharmacoresistant seizures. All these patients underwent continuous video-EEG monitoring. In consenting patients, we used suggestive seizure provocation. We assessed seizure semiology, interictal EEG, brain MRI, psychiatric co-morbidities, personality profiles, and seizure outcome. In the sample of 596 monitored patients, we detected 111 (19.3%) patients with PNES. Of the 111 patients with PNES, 86.5% had spontaneous and 76.5% had provoked seizures. The five most typical symptoms were: initially closed eyelids (67.6%), rapid tremor (47.7%), asynchronous limb movement (37.8%), preictal pseudosleep (33.3%), and side-to-side head movement (32.4%). Interictal EEG was rated as abnormal in 46.2% and with epileptiform abnormality in 9%. Brain MRI was abnormal in 32 (28.8%) patients. Personality disorders (46.8%), anxiety (39.6%), and depression (12.6%) were the most frequent additional psychiatric co-morbidities. PNES outcome after at least 2 years is reported; 22.5% patients was seizure-free; one-third had markedly reduced seizure frequency. We have not seen any negative impact of the provocative testing on the seizure outcome. Video-EEG monitoring with suggestive seizure provocation supported by clinical psychiatric and psychological evaluation significantly contributes to the correct PNES diagnosis, while interictal EEG and brain MRI are frequently abnormal. Symptoms typical for PNES, as opposed to epileptic seizures, could be distinguished.

  6. Forecasting seizures in dogs with naturally occurring epilepsy.

    Directory of Open Access Journals (Sweden)

    J Jeffry Howbert

    Full Text Available Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG. The iEEG spectral power in six frequency bands: delta (0.1-4 Hz, theta (4-8 Hz, alpha (8-12 Hz, beta (12-30 Hz, low-gamma (30-70 Hz, and high-gamma (70-180 Hz, were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring.

  7. Population-based screening versus case detection.

    Directory of Open Access Journals (Sweden)

    Thomas Ravi

    2002-01-01

    Full Text Available India has a large burden of blindness and population-based screening is a strategy commonly employed to detect disease and prevent morbidity. However, not all diseases are amenable to screening. This communication examines the issue of "population-based screening" versus "case detection" in the Indian scenario. Using the example of glaucoma, it demonstrates that given the poor infrastructure, for a "rare" disease, case detection is more effective than population-based screening.

  8. Fast monitoring of epileptic seizures using recurrence time statistics of electroencephalography

    Directory of Open Access Journals (Sweden)

    Jianbo eGao

    2013-10-01

    Full Text Available Epilepsy is one of the most common disorders of the brain. Currently, determination of epileptic seizures often involves tedious, time-consuming visual inspection of electroencephalography (EEG data by medical experts. To better monitor seizures and make medications more effective, we propose a recurrence time based approach to characterize brain electrical activity. Recurrence times have a number of distinguished properties that make it very effective for forwarning epileptic seizures as well as studying propagation of seizures: 1 recurrence times amount to periods of periodic signals, 2 recurrence times are closely related to information dimension, Lyapunov exponent, and Kolmogorov entropy of chaotic signals, 3 recurrence times embody Shannon and Renyi entropies of random fields, and 4 recurrence times can readily detect bifurcation-like transitions in dynamical systems. In particular, property 4 dictates that unlike many other nonlinear methods, recurrence time method does not require the EEG data be chaotic and/or stationary. Moreover, the method only contains a few parameters that are largely signal-independent, and hence, is very easy to use. The method is also very fast—it is fast enough to on-line process multi-channel EEG data with a typical PC. Therefore, it has the potential to be an excellent candidate for real-time monitoring of epileptic seizures in a clinical setting.

  9. Fast monitoring of epileptic seizures using recurrence time statistics of electroencephalography.

    Science.gov (United States)

    Gao, Jianbo; Hu, Jing

    2013-01-01

    Epilepsy is a relatively common brain disorder which may be very debilitating. Currently, determination of epileptic seizures often involves tedious, time-consuming visual inspection of electroencephalography (EEG) data by medical experts. To better monitor seizures and make medications more effective, we propose a recurrence time based approach to characterize brain electrical activity. Recurrence times have a number of distinguished properties that make it very effective for forewarning epileptic seizures as well as studying propagation of seizures: (1) recurrence times amount to periods of periodic signals, (2) recurrence times are closely related to information dimension, Lyapunov exponent, and Kolmogorov entropy of chaotic signals, (3) recurrence times embody Shannon and Renyi entropies of random fields, and (4) recurrence times can readily detect bifurcation-like transitions in dynamical systems. In particular, property (4) dictates that unlike many other non-linear methods, recurrence time method does not require the EEG data be chaotic and/or stationary. Moreover, the method only contains a few parameters that are largely signal-independent, and hence, is very easy to use. The method is also very fast-it is fast enough to on-line process multi-channel EEG data with a typical PC. Therefore, it has the potential to be an excellent candidate for real-time monitoring of epileptic seizures in a clinical setting.

  10. Histamine H1 antagonists and clinical characteristics of febrile seizures

    Directory of Open Access Journals (Sweden)

    Zolaly MA

    2012-03-01

    Full Text Available Mohammed A ZolalyDepartment of Pediatrics, College of Medicine, Taibah University, Al-Madinah Al-Munawarah, Kingdom of Saudi ArabiaBackground: The purpose of this study was to determine whether seizure susceptibility due to antihistamines is provoked in patients with febrile seizures.Methods: The current descriptive study was carried out from April 2009 to February 2011 in 250 infants and children who visited the Madinah Maternity and Children's Hospital as a result of febrile convulsions. They were divided into two groups according to administration of antihistamines at the onset of fever.Results: Detailed clinical manifestations were compared between patients with and without administration of antihistamines. The time from fever detection to seizure onset was significantly shorter in the antihistamine group than that in the nonantihistamine group, and the duration of seizures was significantly longer in the antihistamine group than in the nonantihistamine group. No significant difference was found in time from fever detection to seizure onset or seizure duration between patients who received a first-generation antihistamine and those who received a second-generation antihistamine.Conclusion: Due to their central nervous system effects, H1 antagonists should not be administered to patients with febrile seizures and epilepsy. Caution should be exercised regarding the use of histamine H1 antagonists in young infants, because these drugs could potentially disturb the anticonvulsive central histaminergic system.Keywords: antihistamine, nonantihistamine, histamine H1 antagonist, febrile seizures

  11. Radar-based hail detection

    Czech Academy of Sciences Publication Activity Database

    Skripniková, Kateřina; Řezáčová, Daniela

    2014-01-01

    Roč. 144, č. 1 (2014), s. 175-185 ISSN 0169-8095 R&D Projects: GA ČR(CZ) GAP209/11/2045; GA MŠk LD11044 Institutional support: RVO:68378289 Keywords : hail detection * weather radar * hail damage risk Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.844, year: 2014 http://www.sciencedirect.com/science/article/pii/S0169809513001804

  12. Terminology of psychogenic nonepileptic seizures.

    Science.gov (United States)

    Brigo, Francesco; Igwe, Stanley C; Ausserer, Harald; Nardone, Raffaele; Tezzon, Frediano; Bongiovanni, Luigi Giuseppe; Tinazzi, Michele; Trinka, Eugen

    2015-03-01

    Several different terms have been used to describe "psychogenic nonepileptic seizures" (PNES) in the literature. In this study, we evaluated the most common English terms used to describe PNES on Google and in PubMed using multiple search terms (https://www.google.com and http://www.ncbi.nlm.nih.gov/pubmed). The information prevalence of the five terms most frequently used to refer to PNES in PubMed were: psychogenic non(-)epileptic seizure(s), followed by pseudo(-)seizure(s), non(-)epileptic seizure(s), psychogenic seizure(s), and non(-)epileptic event(s). The five most frequently adopted terms to describe PNES in Google were: psychogenic non(-)epileptic seizure(s), followed by non(-)epileptic event(s), psychogenic attack(s), non(-)epileptic attack(s), and psychogenic non(-)epileptic attack(s). The broad spectrum of synonyms used to refer to PNES in the medical literature reflects a lack of internationally accepted, uniform terminology for PNES. In addition to "seizure(s)," lay people use the word "attack(s)" to describe PNES. Although considered obsolete, some terms, e.g., pseudoseizure(s), are still used in the recent medical literature. Adopting a uniform terminology to describe PNES could facilitate communication between epileptologists, physicians without specific expertise in epilepsy, and patients. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

  13. Seizures Complicating Bacterial Meningitis

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2004-09-01

    Full Text Available The clinical data of 116 patients, 1 month to <5 years of age, admitted for bacterial meningitis, and grouped according to those with and without seizures during hospitalization, were compared in a study at Buddhist Dalin Tzu Chi General Hospital, Chang Gung Memorial Hospital and other centers in Taiwan.

  14. Audiovisual laughter detection based on temporal features

    NARCIS (Netherlands)

    Petridis, Stavros; Nijholt, Antinus; Nijholt, A.; Pantic, M.; Pantic, Maja; Poel, Mannes; Poel, M.; Hondorp, G.H.W.

    2008-01-01

    Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audiovisual approach to distinguishing laughter from speech based on temporal features and we show that the integration of audio and visual information leads to improved

  15. Impaired peri-nidal cerebrovascular reserve in seizure patients with brain arteriovenous malformations

    NARCIS (Netherlands)

    Fierstra, Jorn; Conklin, John; Krings, Timo; Slessarev, Marat; Han, Jay S.; Fisher, Joseph A.; terBrugge, Karel; Wallace, M. Christopher; Tymianski, Michael; Mikulis, David J.

    Epileptic seizures are a common presentation in patients with newly diagnosed brain arteriovenous malformations, but the pathophysiological mechanisms causing the seizures remain poorly understood. We used magnetic resonance imaging-based quantitative cerebrovascular reactivity mapping and

  16. Semiologic classification of psychogenic non epileptic seizures (PNES) based on video EEG analysis: do we need new classification systems?

    Science.gov (United States)

    Wadwekar, Vaibhav; Nair, Pradeep Pankajakshan; Murgai, Aditya; Thirunavukkarasu, Sibi; Thazhath, Harichandrakumar Kottyen

    2014-03-01

    Different studies have described useful signs to diagnose psychogenic non-epileptic seizure (PNES). A few authors have tried to describe the semiologic groups among PNES patients; each group consisting of combination of features. But there is no uniformity of nomenclature among these studies. Our aim was to find out whether the objective classification system proposed by Hubsch et al. was useful and adequate to classify PNES patient population from South India. We retrospectively analyzed medical records and video EEG monitoring data of patients, recorded during 3 year period from June 2010 to July 2013. We observed the semiologic features of each PNES episode and tried to group them strictly adhering to Hubsch et al. classification. Minor modifications were made to include patients who were left unclassified. A total of 65 patients were diagnosed to have PNES during this period, out of which 11 patients were excluded due to inadequate data. We could classify 42(77.77%) patients without modifying the defining criteria of the Hubsch et al. groups. With minor modification we could classify 94.96% patients. The modified groups with patient distribution are as follows: Class 1--dystonic attacks with primitive gestural activities [3(5.6%)]. Class 2 – paucikinetic attacks with or without preserved responsiveness [5(9.3%)]. Class 3--pseudosyncope with or without hyperventilation [21(38.9%)]. Class 4--hyperkinetic prolonged attacks with hyperventilation, involvement of limbs and/or trunk [14(25.9%)]. Class 5--axial dystonic attacks [8(14.8%)]. Class 6--unclassified type [3(5.6%)]. This study demonstrates that the Hubsch's classification with minor modifications is useful and adequate to classify PNES patients from South India. Copyright © 2013 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  17. Automated differentiation between epileptic and non-epileptic convulsive seizures

    DEFF Research Database (Denmark)

    Beniczky, Sándor; Conradsen, Isa; Moldovan, Mihai

    2015-01-01

    Our objective was the clinical validation of an automated algorithm based on surface electromyography (EMG) for differentiation between convulsive epileptic and psychogenic nonepileptic seizures (PNESs). Forty-four consecutive episodes with convulsive events were automatically analyzed with the a......%) and 18 PNESs (95%). The overall diagnostic accuracy was 95%. This algorithm is useful for distinguishing between epileptic and psychogenic convulsive seizures....

  18. Community-Based Intrusion Detection

    OpenAIRE

    Weigert, Stefan

    2017-01-01

    Today, virtually every company world-wide is connected to the Internet. This wide-spread connectivity has given rise to sophisticated, targeted, Internet-based attacks. For example, between 2012 and 2013 security researchers counted an average of about 74 targeted attacks per day. These attacks are motivated by economical, financial, or political interests and commonly referred to as “Advanced Persistent Threat (APT)” attacks. Unfortunately, many of these attacks are successful and the advers...

  19. Daytime Water Detection Based on Sky Reflections

    Science.gov (United States)

    Rankin, Arturo; Matthies, Larry; Bellutta, Paolo

    2011-01-01

    A water body s surface can be modeled as a horizontal mirror. Water detection based on sky reflections and color variation are complementary. A reflection coefficient model suggests sky reflections dominate the color of water at ranges > 12 meters. Water detection based on sky reflections: (1) geometrically locates the pixel in the sky that is reflecting on a candidate water pixel on the ground (2) predicts if the ground pixel is water based on color similarity and local terrain features. Water detection has been integrated on XUVs.

  20. Spike detection II: automatic, perception-based detection and clustering.

    Science.gov (United States)

    Wilson, S B; Turner, C A; Emerson, R G; Scheuer, M L

    1999-03-01

    We developed perception-based spike detection and clustering algorithms. The detection algorithm employs a novel, multiple monotonic neural network (MMNN). It is tested on two short-duration EEG databases containing 2400 spikes from 50 epilepsy patients and 10 control subjects. Previous studies are compared for database difficulty and reliability and algorithm accuracy. Automatic grouping of spikes via hierarchical clustering (using topology and morphology) is visually compared with hand marked grouping on a single record. The MMNN algorithm is found to operate close to the ability of a human expert while alleviating problems related to overtraining. The hierarchical and hand marked spike groupings are found to be strikingly similar. An automatic detection algorithm need not be as accurate as a human expert to be clinically useful. A user interface that allows the neurologist to quickly delete artifacts and determine whether there are multiple spike generators is sufficient.

  1. Power Consumption Based Android Malware Detection

    Directory of Open Access Journals (Sweden)

    Hongyu Yang

    2016-01-01

    Full Text Available In order to solve the problem that Android platform’s sand-box mechanism prevents security protection software from accessing effective information to detect malware, this paper proposes a malicious software detection method based on power consumption. Firstly, the mobile battery consumption status information was obtained, and the Gaussian mixture model (GMM was built by using Mel frequency cepstral coefficients (MFCC. Then, the GMM was used to analyze power consumption; malicious software can be classified and detected through classification processing. Experiment results demonstrate that the function of an application and its power consumption have a close relationship, and our method can detect some typical malicious application software accurately.

  2. Apriori-based network intrusion detection system

    International Nuclear Information System (INIS)

    Wang Wenjin; Liu Junrong; Liu Baoxu

    2012-01-01

    With the development of network communication technology, more and more social activities run by Internet. In the meantime, the network information security is getting increasingly serious. Intrusion Detection System (IDS) has greatly improved the general security level of whole network. But there are still many problem exists in current IDS, e.g. high leak rate detection/false alarm rates and feature library need frequently upgrade. This paper presents an association-rule based IDS. This system can detect unknown attack by generate rules from training data. Experiment in last chapter proved the system has great accuracy on unknown attack detection. (authors)

  3. Histamine H1 antagonists and clinical characteristics of febrile seizures.

    Science.gov (United States)

    Zolaly, Mohammed A

    2012-01-01

    The purpose of this study was to determine whether seizure susceptibility due to antihistamines is provoked in patients with febrile seizures. The current descriptive study was carried out from April 2009 to February 2011 in 250 infants and children who visited the Madinah Maternity and Children's Hospital as a result of febrile convulsions. They were divided into two groups according to administration of antihistamines at the onset of fever. Detailed clinical manifestations were compared between patients with and without administration of antihistamines. The time from fever detection to seizure onset was significantly shorter in the antihistamine group than that in the nonantihistamine group, and the duration of seizures was significantly longer in the antihistamine group than in the nonantihistamine group. No significant difference was found in time from fever detection to seizure onset or seizure duration between patients who received a first-generation antihistamine and those who received a second-generation antihistamine. Due to their central nervous system effects, H1 antagonists should not be administered to patients with febrile seizures and epilepsy. Caution should be exercised regarding the use of histamine H1 antagonists in young infants, because these drugs could potentially disturb the anticonvulsive central histaminergic system.

  4. Association between Estrus and Onset of Seizures in Dogs with Idiopathic Epilepsy

    OpenAIRE

    Van Meervenne, S.A.E.; Volk, H.A.; Van Ham, L.M.L.

    2014-01-01

    Background Catamenial epilepsy in humans is defined as changes in seizure frequency over the course of the menstrual cycle. Three hormonally based patterns of seizure exacerbation have been determined. Objectives The aim of this study was to evaluate whether there is an association between onset of seizures and the estrous cycle in intact bitches with presumptive idiopathic epilepsy and whether a pattern to the onset of seizures could be recognized. Animals Forty?five intact female dogs from ...

  5. The Long-term Risk of Epilepsy after Febrile Seizures in susceptible subgroups

    DEFF Research Database (Denmark)

    Vestergaard, Mogens; Pedersen, Carsten Bøcker; Sidenius, Per Christian

    2007-01-01

    A family history of seizures, preexisting brain damage, or birth complications may modify the long-term risk of epilepsy after febrile seizures. The authors evaluated the association between febrile seizures and epilepsy in a population-based cohort of 1.54 million persons born in Denmark (1978-2......, or low Apgar scores at 5 minutes....

  6. Vision Based Obstacle Detection in Uav Imaging

    Science.gov (United States)

    Badrloo, S.; Varshosaz, M.

    2017-08-01

    Detecting and preventing incidence with obstacles is crucial in UAV navigation and control. Most of the common obstacle detection techniques are currently sensor-based. Small UAVs are not able to carry obstacle detection sensors such as radar; therefore, vision-based methods are considered, which can be divided into stereo-based and mono-based techniques. Mono-based methods are classified into two groups: Foreground-background separation, and brain-inspired methods. Brain-inspired methods are highly efficient in obstacle detection; hence, this research aims to detect obstacles using brain-inspired techniques, which try to enlarge the obstacle by approaching it. A recent research in this field, has concentrated on matching the SIFT points along with, SIFT size-ratio factor and area-ratio of convex hulls in two consecutive frames to detect obstacles. This method is not able to distinguish between near and far obstacles or the obstacles in complex environment, and is sensitive to wrong matched points. In order to solve the above mentioned problems, this research calculates the dist-ratio of matched points. Then, each and every point is investigated for Distinguishing between far and close obstacles. The results demonstrated the high efficiency of the proposed method in complex environments.

  7. Recent advances in biosensor based endotoxin detection.

    Science.gov (United States)

    Das, A P; Kumar, P S; Swain, S

    2014-01-15

    Endotoxins also referred to as pyrogens are chemically lipopolysaccharides habitually found in food, environment and clinical products of bacterial origin and are unavoidable ubiquitous microbiological contaminants. Pernicious issues of its contamination result in high mortality and severe morbidities. Standard traditional techniques are slow and cumbersome, highlighting the pressing need for evoking agile endotoxin detection system. The early and prompt detection of endotoxin assumes prime importance in health care, pharmacological and biomedical sectors. The unparalleled recognition abilities of LAL biosensors perched with remarkable sensitivity, high stability and reproducibility have bestowed it with persistent reliability and their possible fabrication for commercial applicability. This review paper entails an overview of various trends in current techniques available and other possible alternatives in biosensor based endotoxin detection together with its classification, epidemiological aspects, thrust areas demanding endotoxin control, commercially available detection sensors and a revolutionary unprecedented approach narrating the influence of omics for endotoxin detection. Copyright © 2013 Elsevier B.V. All rights reserved.

  8. An SNMP based failure detection service

    OpenAIRE

    Wiesmann, Matthias; Urban, Peter; Defago, Xavier

    2006-01-01

    In this paper, we present the SNMP-FD system. This system is a novel failure detection service entirely based on the SNMP standard. The advantage of this approach is better interoperability, and the possibility to rely on different sources of information for failure detection, including network equipment. This, in turn, gives us more precise failure information. This paper presents the architecture of the SNMP-FD system and discusses its advantages, both from the system engineering and intero...

  9. Plagiarism Detection Based on SCAM Algorithm

    DEFF Research Database (Denmark)

    Anzelmi, Daniele; Carlone, Domenico; Rizzello, Fabio

    2011-01-01

    Plagiarism is a complex problem and considered one of the biggest in publishing of scientific, engineering and other types of documents. Plagiarism has also increased with the widespread use of the Internet as large amount of digital data is available. Plagiarism is not just direct copy but also...... paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. Plagiarism detection techniques are applied by making a distinction between natural and programming languages. Our proposed detection process is based on natural language...... document. Our plagiarism detection system, like many Information Retrieval systems, is evaluated with metrics of precision and recall....

  10. Quantum Endpoint Detection Based on QRDA

    Science.gov (United States)

    Wang, Jian; Wang, Han; Song, Yan

    2017-10-01

    Speech recognition technology is widely used in many applications for man - machine interaction. To face more and more speech data, the computation of speech processing needs new approaches. The quantum computation is one of emerging computation technology and has been seen as useful computation model. So we focus on the basic operation of speech recognition processing, the voice activity detection, to present quantum endpoint detection algorithm. In order to achieve this algorithm, the n-bits quantum comparator circuit is given firstly. Then based on QRDA(Quantum Representation of Digital Audio), a quantum endpoint detection algorithm is presented. These quantum circuits could efficient process the audio data in quantum computer.

  11. Magnetic resonance imaging in complex partial seizures

    International Nuclear Information System (INIS)

    Furune, Sunao; Negoro, Tamiko; Maehara, Mitsuo; Nomura, Kazushi; Miura, Kiyokuni; Takahashi, Izumi; Watanabe, Kazuyoshi

    1989-01-01

    Magnetic resonance imaging (MRI) and computed tomography (CT) were performed on 45 patients with intractable complex partial seizures. MRI was performed with a superconducting whole-body scanner operating at 0.5 tesla (T) and 1.5 T. In patients with temporal lobe epilepsy, 8 of 24 patients had abnormal CT, but 16 or 24 patients showed abnormal MRI. 1.5 T MRI detected more abnormality than 0.5 T MRI when CT was normal. In patients with frontal lobe epilepsy, 5 of 7 patients had normal CT and MRI. In 2 other patients, MRI demonstrated an arachnoid cyst and increased signal intensity area on the T2-weighted images which were not detected by CT. In patients with occipital lobe epilepsy, 5 of 6 patients show abnormal CT and MRI. In patients with tuberous sclerosis, MRI revealed some increased signal intensity areas on the T2-weighted images in the occipital and temporal lobe, which were not detected by CT. Most surface EEG foci corresponded with the side of MRI abnormality. These data indicate that MRI is more informative than CT in complex partial seizures. MRI is the imaging technique of choice in the diagnosis of complex partial seizures. (author)

  12. Preventing and treating posttraumatic seizures: the human experience.

    Science.gov (United States)

    Temkin, Nancy R

    2009-02-01

    Posttraumatic epilepsy presents an ideal target for prevention efforts. Traumatic brain injury (TBI) is common, characteristics that put people at high risk such as penetrating injury or subdural hematoma or provoked seizures are easily identified, and the latency between the injury and the onset of epileptic seizures is frequently short. Several drugs have been tested for their ability to prevent provoked seizures and epilepsy after TBI. We describe the design of those studies and their results. Phenytoin and carbamazepine significantly reduce the incidence of provoked seizures. Phenobarbital and the combination of phenobarbital and phenytoin also look promising for reducing provoked seizures, but small sample sizes in the studies evaluating these drugs do not allow definitive conclusions. None of the drugs studied (phenytoin, phenobarbital, their combination, carbamazepine, valproate, or magnesium) have shown reliable evidence that they prevent, or even suppress, epileptic seizures after TBI. For most of the regimens tested (the phenytoin/phenobarbital combination being the exception), the best estimate of effect is under a 25% reduction in posttraumatic seizures, well less than the 50% reduction most studies were designed to detect. The evaluation of the tested drugs has serious limitations, however, and antiepileptic drugs (AEDs) developed since 1980 and other compounds have barely been tested at all. Better understanding the process of epileptogenesis, testing treatments that demonstrate antiepileptogenic effects in the laboratory, and performing thorough preclinical and phase II evaluations before attempting definitive trials should greatly improve the chance of identifying ways to prevent posttraumatic epilepsy, providing the ultimate cure for this condition.

  13. Time dependencies in the occurrences of epileptic seizures.

    Science.gov (United States)

    Iasemidis, L D; Olson, L D; Savit, R S; Sackellares, J C

    1994-01-01

    A new method of analysis, developed within the framework of nonlinear dynamics, is applied to patient recorded time series of the occurrence of epileptic seizures. These data exhibit broad band spectra and generally have no obvious structure. The goal is to detect hidden internal dependencies in the data without making any restrictive assumptions, such as linearity, about the structure of the underlying system. The basis of our approach is a conditional probabilistic analysis in a phase space reconstructed from the original data. The data, recorded from patients with intractable epilepsy over a period of 1-3 years, consist of the times of occurrences of hundreds of partial complex seizures. Although the epileptic events appear to occur independently, we show that the epileptic process is not consistent with the rules of a homogeneous Poisson process or generally with a random (IID) process. More specifically, our analysis reveals dependencies of the occurrence of seizures on the occurrence of preceding seizures. These dependencies can be detected in the interseizure interval data sets as well as in the rate of seizures per time period. We modeled patient's inaccuracy in recording seizure events by the addition of uniform white noise and found that the detected dependencies are persistent after addition of noise with standard deviation as great as 1/3 of the standard deviation of the original data set. A linear autoregressive analysis fails to capture these dependencies or produces spurious ones in most of the cases.

  14. Seizure Freedom in Children With Pathology-Confirmed Focal Cortical Dysplasia.

    Science.gov (United States)

    Mrelashvili, Anna; Witte, Robert J; Wirrell, Elaine C; Nickels, Katherine C; Wong-Kisiel, Lily C

    2015-12-01

    We evaluated the temporal course of seizure outcome in children with pathology-confirmed focal cortical dysplasia and explored predictors of sustained seizure freedom. We performed a single-center retrospective study of children ≤ 18 years who underwent resective surgery from January 1, 2000 through December 31, 2012 and had pathology-proven focal cortical dysplasia. Surgical outcome was classified as seizure freedom (Engel class I) or seizure recurrence (Engel classes II-IV). Fisher exact and nonparametric Wilcoxon ranksum tests were used, as appropriate. Survival analysis was based on seizure-free outcome. Patients were censored at the time of seizure recurrence or seizure freedom at last follow-up. Thirty-eight patients were identified (median age at surgery, 6.5 years; median duration of epilepsy, 3.3 years). Median time to last follow-up was 13.5 months (interquartile range, 7-41 months). Twenty patients (53%) were seizure free and 26 patients (68%) attained seizure freedom for a minimum of 3 months. Median time to seizure recurrence was 38 months (95% confidence interval, 6-109 months), and the cumulative seizure-free rate was 60% at 12 months (95% confidence interval, 43%-77%). Clinical features associated with seizure freedom at last follow-up included older age at seizure onset (P = .02), older age at surgery (P = .04), absent to mild intellectual disability before surgery (P = .05), and seizure freedom for a minimum of 3 months (P freedom included older age at seizure onset, older age at surgery, absent or mild intellectual disability at baseline, and seizure freedom for a minimum of 3 months. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. A brief history of typical absence seizures - Petit mal revisited.

    Science.gov (United States)

    Brigo, Francesco; Trinka, Eugen; Lattanzi, Simona; Bragazzi, Nicola Luigi; Nardone, Raffaele; Martini, Mariano

    2018-03-01

    In this article, we have traced back the history of typical absence seizures, from their initial clinical description to the more recent nosological position. The first description of absence seizures was made by Poupart in 1705 and Tissot in 1770. In 1824, Calmeil introduced the term "absences", and in 1838, Esquirol for the first time used the term petit mal. Reynolds instead used the term "epilepsia mitior" (milder epilepsy) and provided a comprehensive description of absence seizures (1861). In 1854, Delasiauve ranked absences as the seizure type with lower severity and introduced the concept of idiopathic epilepsy. Otto Binswanger (1899) discussed the role of cortex in the pathophysiology of "abortive seizures", whereas William Gowers (1901) emphasized the importance of a detailed clinical history to identify nonmotor seizures or very mild motor phenomena which otherwise may go unnoticed or considered not epileptic. At the beginning of the 20th Century, the term pyknolepsy was introduced, but initially was not universally considered as a type of epilepsy; it was definitely recognized as an epileptic entity only in 1945, based on electroencephalogram (EEG) recordings. Hans Berger, the inventor of the EEG, made also the first EEG recording of an atypical absence (his results were published only in 1933), whereas the characteristic EEG pattern was reported by neurophysiologists of the Harvard Medical School in 1935. The discovery of EEG made it also possible to differentiate absence seizures from so called "psychomotor" seizures occurring in temporal lobe epilepsy. Penfield and Jasper (1938) considered absences as expression of "centrencephalic epilepsy". Typical absences seizures are now classified by the International League Against Epilepsy among generalized nonmotor (absence) seizures. Copyright © 2018 Elsevier Inc. All rights reserved.

  16. On event-based optical flow detection

    Directory of Open Access Journals (Sweden)

    Tobias eBrosch

    2015-04-01

    Full Text Available Event-based sensing, i.e. the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER. Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency. A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations.

  17. Improved biosensor-based detection system

    DEFF Research Database (Denmark)

    2015-01-01

    Described is a new biosensor-based detection system for effector compounds, useful for in vivo applications in e.g. screening and selecting of cells which produce a small molecule effector compound or which take up a small molecule effector compound from its environment. The detection system...... comprises a protein or RNA-based biosensor for the effector compound which indirectly regulates the expression of a reporter gene via two hybrid proteins, providing for fewer false signals or less 'noise', tuning of sensitivity or other advantages over conventional systems where the biosensor directly...

  18. Image denoising based on noise detection

    Science.gov (United States)

    Jiang, Yuanxiang; Yuan, Rui; Sun, Yuqiu; Tian, Jinwen

    2018-03-01

    Because of the noise points in the images, any operation of denoising would change the original information of non-noise pixel. A noise detection algorithm based on fractional calculus was proposed to denoise in this paper. Convolution of the image was made to gain direction gradient masks firstly. Then, the mean gray was calculated to obtain the gradient detection maps. Logical product was made to acquire noise position image next. Comparisons in the visual effect and evaluation parameters after processing, the results of experiment showed that the denoising algorithms based on noise were better than that of traditional methods in both subjective and objective aspects.

  19. Detection of epileptogenic cortical malformations with surface-based MRI morphometry.

    Directory of Open Access Journals (Sweden)

    Thomas Thesen

    2011-02-01

    Full Text Available Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity with few false positives (96% specificity, successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from

  20. Detection of epileptogenic cortical malformations with surface-based MRI morphometry.

    Science.gov (United States)

    Thesen, Thomas; Quinn, Brian T; Carlson, Chad; Devinsky, Orrin; DuBois, Jonathan; McDonald, Carrie R; French, Jacqueline; Leventer, Richard; Felsovalyi, Olga; Wang, Xiuyuan; Halgren, Eric; Kuzniecky, Ruben

    2011-02-04

    Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery.

  1. Recurrent seizures after lidocaine ingestion.

    Science.gov (United States)

    Aminiahidashti, Hamed; Laali, Abolghasem; Nosrati, Nazanin; Jahani, Fatemeh

    2015-01-01

    Lidocaine has a concentration-dependent effect on seizures. Concentrations above 15 μg/mL frequently result in seizures in laboratory animals and human. We report a case of central nervous system (CNS) lidocaine toxicity and recurrent seizure after erroneous ingestion of lidocaine solution. A 4-year-old boy presented to the Emergency Department of Imam Hospital of Sari in December 2013 due to tonic-clonic generalized seizures approximately 30 min ago. 3 h before seizure, his mother gave him 2 spoons (amount 20-25 cc) lidocaine hydrochloride 2% solution instead of pediatric gripe by mistake. Seizure with generalized tonic-clonic occurred 3 times in home. Neurological examination was essentially unremarkable except for the depressed level of consciousness. Personal and medical history was unremarkable. There was no evidence of intracranial ischemic or hemorrhagic lesions in computed tomography scan. There were no further seizures, the condition of the patient remained stable, and he was discharged 2 days after admission. The use of viscous lidocaine may result in cardiovascular and CNS toxicity, particularly in children. Conservative management is the best option for treatment of lidocaine induced seizure.

  2. Nonepileptic Seizures: An Updated Review

    Science.gov (United States)

    Perez, David L.; LaFrance, W. Curt

    2016-01-01

    Psychogenic nonepileptic seizures are a Functional Neurological Disorder/ Conversion Disorder subtype, which are neurobehavioral conditions at the interface of Neurology and Psychiatry. Significant advancements over the past decade have been made in the diagnosis, management and neurobiological understanding of PNES. This article reviews published PNES research focusing on semiologic features that distinguish PNES from epileptic seizures, consensus diagnostic criteria, the intersection of PNES and other comorbidities, neurobiological studies, evidence-based treatment interventions and outcome studies. Epidemiology and health care utilization studies highlight a continued unmet medical need in the comprehensive care of PNES. Consensus guidelines for diagnostic certainty are based on clinical history, semiology of witnessed typical event(s), and EEG findings. While certain semiologic features may aid the diagnosis of PNES, the gold standard remains capturing a typical event on video electroencephalography (EEG) showing the absence of epileptiform activity with history and semiology consistent with PNES. Medical-neurologic and psychiatric comorbidities are prevalent in PNES and should be assessed in diagnostic evaluations, and integrated into treatment interventions and prognostic considerations. Several studies, including a pilot multicenter, randomized clinical trial, have now demonstrated that a cognitive behavioral therapy informed psychotherapy is an efficacious treatment for PNES, and additional efforts are necessary to evaluate the utility of pharmacologic and other psychotherapy treatments. Neuroimaging studies, while requiring replication, suggest that PNES may occur in the context of alterations within and across sensorimotor, emotion regulation/processing, cognitive control and multimodal integration brain systems. Future research could investigate similarities and differences between PNES and other somatic symptom disorders. PMID:26996600

  3. Epilepsy: accuracy of patient seizure counts.

    Science.gov (United States)

    Hoppe, Christian; Poepel, Annkathrin; Elger, Christian E

    2007-11-01

    To evaluate the effects of a daily patient reminder on seizure documentation accuracy. Randomized controlled trial. Monitoring unit of an academic department of epileptology. Patients Consecutive sample of 91 adult inpatients with focal epilepsies undergoing video-electroencephalographic monitoring. Intervention While all patients were asked to document seizures at the beginning of the monitoring period, patients from the experimental group were reminded each day to document seizures. Main Outcome Measure Documentation accuracy (percentage of documented seizures). A total of 582 partial seizures were recorded. Patients failed to document 55.5% of all recorded seizures, 73.2% of complex partial seizures, 26.2% of simple partial seizures, 41.7% of secondarily generalized tonic-clonic seizures, 85.8% of all seizures during sleeping, and 32.0% of all seizures during the awake state. The group medians of individual documentation accuracies for overall seizures, simple partial seizures, complex partial seizures, and secondarily generalized tonic-clonic seizures were 33.3%, 66.7%, 0%, and 83.3%, respectively. Neither the patient reminder nor cognitive performance affected documentation accuracy. A left-sided electroencephalographic focus or lesion, but not the site (frontal or temporal), contributed to documentation failure. Patient seizure counts do not provide valid information. Documentation failures result from postictal seizure unawareness, which cannot be avoided by reminders. Unchanged documentation accuracy is a prerequisite for the use of patient seizure counts in clinical trials and has to be demonstrated in a subsample of patients undergoing electroencephalographic monitoring.

  4. [Reflex seizures, cinema and television].

    Science.gov (United States)

    Olivares-Romero, Jesús

    2015-12-16

    In movies and television series are few references to seizures or reflex epilepsy even though in real life are an important subgroup of total epileptic syndromes. It has performed a search on the topic, identified 25 films in which they appear reflex seizures. Most seizures observed are tonic-clonic and visual stimuli are the most numerous, corresponding all with flashing lights. The emotions are the main stimuli in higher level processes. In most cases it is not possible to know if a character suffers a reflex epilepsy or suffer reflex seizures in the context of another epileptic syndrome. The main conclusion is that, in the movies, the reflex seizures are merely a visual reinforcing and anecdotal element without significant influence on the plot.

  5. Cardiac asystole in partial seizures.

    Science.gov (United States)

    Scott, C A; Fish, D R

    2000-06-01

    Literature review shows many anecdotal case reports of cardiac asystole in ictal recordings of partial seizures. We have reviewed our data from the last five years, of patients who are being assessed for epilepsy surgery and found 2 out of more than 1,500 complex partial seizures, recorded in 589 consecutive patients, showing a significant period of asystole (13 and 15 seconds). Our previous studies of cardiac and respiratory parameters during partial seizures showed that a central apnoea occurred in 39%. It is probable that sudden death during seizures is due to the interaction of both cardiac and respiratory irregularities. Although rare (occurrence cardiac asystole occurring in an epilepsy monitoring unit highlights the need for resuscitation equipment to be readily available and for trained nursing staff. Furthermore, it is important to recognize that the semiology of seizures may be affected by the consequences of secondary cardiac asystole.

  6. Tornado Detection Based on Seismic Signal.

    Science.gov (United States)

    Tatom, Frank B.; Knupp, Kevin R.; Vitton, Stanley J.

    1995-02-01

    At the present time the only generally accepted method for detecting when a tornado is on the ground is human observation. Based on theoretical considerations combined with eyewitness testimony, there is strong reason to believe that a tornado in contact with the ground transfers a significant amount of energy into the ground. The amount of energy transferred depends upon the intensity of the tornado and the characteristics of the surface. Some portion of this energy takes the form of seismic waves, both body and surface waves. Surface waves (Rayleigh and possibly Love) represent the most likely type of seismic signal to be detected. Based on the existence of such a signal, a seismic tornado detector appears conceptually possible. The major concerns for designing such a detector are range of detection and discrimination between the tornadic signal and other types of surface waves generated by ground transportation equipment, high winds, or other nontornadic sources.

  7. Water Detection Based on Object Reflections

    Science.gov (United States)

    Rankin, Arturo L.; Matthies, Larry H.

    2012-01-01

    Water bodies are challenging terrain hazards for terrestrial unmanned ground vehicles (UGVs) for several reasons. Traversing through deep water bodies could cause costly damage to the electronics of UGVs. Additionally, a UGV that is either broken down due to water damage or becomes stuck in a water body during an autonomous operation will require rescue, potentially drawing critical resources away from the primary operation and increasing the operation cost. Thus, robust water detection is a critical perception requirement for UGV autonomous navigation. One of the properties useful for detecting still water bodies is that their surface acts as a horizontal mirror at high incidence angles. Still water bodies in wide-open areas can be detected by geometrically locating the exact pixels in the sky that are reflecting on candidate water pixels on the ground, predicting if ground pixels are water based on color similarity to the sky and local terrain features. But in cluttered areas where reflections of objects in the background dominate the appearance of the surface of still water bodies, detection based on sky reflections is of marginal value. Specifically, this software attempts to solve the problem of detecting still water bodies on cross-country terrain in cluttered areas at low cost.

  8. Community detection based on network communicability

    Science.gov (United States)

    Estrada, Ernesto

    2011-03-01

    We propose a new method for detecting communities based on the concept of communicability between nodes in a complex network. This method, designated as N-ComBa K-means, uses a normalized version of the adjacency matrix to build the communicability matrix and then applies K-means clustering to find the communities in a graph. We analyze how this method performs for some pathological cases found in the analysis of the detection limit of communities and propose some possible solutions on the basis of the analysis of the ratio of local to global densities in graphs. We use four different quality criteria for detecting the best clustering and compare the new approach with the Girvan-Newman algorithm for the analysis of two "classical" networks: karate club and bottlenose dolphins. Finally, we analyze the more challenging case of homogeneous networks with community structure, for which the Girvan-Newman completely fails in detecting any clustering. The N-ComBa K-means approach performs very well in these situations and we applied it to detect the community structure in an international trade network of miscellaneous manufactures of metal having these characteristics. Some final remarks about the general philosophy of community detection are also discussed.

  9. Wavelet-frame-based microcalcification detection

    Science.gov (United States)

    Chang, Charles C.; Wu, Hsien-Hsun S.; Liu, Jyh-Charn S.; Chui, Charles K.

    1997-10-01

    As the leading cause of death for adult women under 54 years of age in the United States, breast cancer accounts for 29% of all cancers in women. Early diagnosis of breast cancer is the most effective approach to reduce death rate. The rapid climbing of the health care cost further reiterates the importance of cost-effective, accurate screening tools for breast cancer. This paper proposes a wavelet frame based computer algorithm for screening of microcalcifications on digitized mammographical imagery. Despite its simplicity, the discrete wavelet transform (DWT) of compactly supported wavelets has been effectively used for detection of various types of signals. However, the shifting variant property of DWT makes it very unstable for detection of minute microcalcifications. Although increasing the sampling rate will improve the detection probability, this approach will drastically increase the size of mammographical images. The wavelet frame transform can be easily derived from the DWT algorithm by eliminating its down sampling step. The subtle difference between DWT and WF in down sampling is shown to be critical to the accuracy of microcalcifications detection. Without any down sampling, local image information at different scales is preserved. By joint thresholding of wavelet coefficients at different scales, one can accurately pin point suspected microcalcifications. A simple partitioning technique enables the detection algorithm to process image blocks independently. Four different partitioning techniques have been compared, and the method of repeating the end value on each partition boundary has the least significant impact on the detection accuracy.

  10. DIFFERENTIAL SEARCH ALGORITHM BASED EDGE DETECTION

    Directory of Open Access Journals (Sweden)

    M. A. Gunen

    2016-06-01

    Full Text Available In this paper, a new method has been presented for the extraction of edge information by using Differential Search Optimization Algorithm. The proposed method is based on using a new heuristic image thresholding method for edge detection. The success of the proposed method has been examined on fusion of two remote sensed images. The applicability of the proposed method on edge detection and image fusion problems have been analysed in detail and the empirical results exposed that the proposed method is useful for solving the mentioned problems.

  11. Microcomputer-based video motion detection system

    International Nuclear Information System (INIS)

    Howington, L.C.

    1979-01-01

    This system was developed to enhance the volumetric intrusion detection capability of the Oak Ridge Y-12 Plant's security program. Not only does the system exhibit an extended range of detection over present infrared, microwave, and ultrasonic devices, it also provides an instantaneous assessment capability by providing the operator with a closed-circuit television (CCTV) image of the alarm scene as soon as motion is detected. The system consists of a custom-built, microcomputer-based, video processor which analyzes the signals received from a network of video cameras. The operator can view the camera images as they are displayed on a CCTV monitor while alarm scenes are displayed on a second monitor. Motion is detected by digitizing and comparing successive video frames and making an alarm decision based on the degree of mismatch. The software-based nature of the microcomputer lends a great deal of flexibility and adaptability in making the alarm decision. Alarm decision variables which are easily adjusted through software are the percent change in gray level required to label a pixel (picture element) as suspect, the number of suspect pixels required to generate an alarm, the pixel pattern to be sampled from the image, and the rate at which a new reference frame is taken. The system is currently being evaluated in a warehouse for potential application in several areas of the Plant. This paper discusses the hardware and software design of the system as well as problems encountered in its implementation and results obtained

  12. Cocaine-Associated Seizures and Incidence of Status Epilepticus

    Directory of Open Access Journals (Sweden)

    Majlesi, Nima DO

    2010-05-01

    Full Text Available Objectives: Acute complications from cocaine abuse are commonly treated in the emergency department (ED; one of the most consequential is status epilepticus. The incidence of this complication is not clearly defined in the prior literature on cocaine-associated sequelae. We evaluated the incidence of status epilepticus in patients with seizures secondary to suspected cocaine use.Methods: We performed a retrospective multi-center study of patients with seizures resulting from cocaine use. We identified study subjects at 15 hospitals by record review and conducted a computer-assisted records search to identify patients with seizures for each institution over a four-year period. We selected subjects from this group on the basis of cocaine use and determined the occurrence of status epilepticus among them. Data were collected on each subject using a standardized data collection form.Results: We evaluated 43 patients in the ED for cocaine-associated seizures. Their age range was 17 to 54, with a mean age was 31 years; 53% were male. Of 43 patients, 42 experienced a single tonic-clonic seizure and one developed status epilepticus. All patients had either a history of cocaine use or positive urine drug screen for cocaine.Conclusion: Despite reported cases of status epilepticus with cocaine-induced seizures, the incidence of this complication was unclear based on prior literature. This study shows that most cocaine-associated seizures are self-limited. [West J Emerg Med. 2010; 11(2:157-160.

  13. Febrile seizures. Update and controversies.

    Science.gov (United States)

    Jan, Mohammed M; Girvin, John P

    2004-10-01

    Febrile seizures are the most common seizure disorder in children younger than 5 years of age. Most febrile seizures are brief, do not require any specific treatment or workup, and have benign prognoses. Generalists and pediatricians are frequently faced with anxious parents and are required to make rational decisions regarding the need to investigate and treat such a child. They subsequently need to provide further prognostic information and counseling to the families. The aim of this article is to provide an updated overview of febrile seizures and review the most recent diagnostic and therapeutic recommendations. Despite the progress in the understanding of this benign syndrome, a wide variation in physician evaluation and management persists. However, there is recent evidence that pediatricians are becoming more selective in admitting and investigating children with febrile seizures. Admitted children frequently had complex seizures, status epilepticus, or were ill looking. Considering the full scope of febrile seizures, the yield of investigations that might alter management remains low and does not justify extensive work-up or prolonged hospitalization.

  14. Frequency Based Fault Detection in Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2014-01-01

    In order to obtain lower cost of energy for wind turbines fault detection and accommodation is important. Expensive condition monitoring systems are often used to monitor the condition of rotating and vibrating system parts. One example is the gearbox in a wind turbine. This system is operated...... in parallel to the control system, using different computers and additional often expensive sensors. In this paper a simple filter based algorithm is proposed to detect changes in a resonance frequency in a system, exemplified with faults resulting in changes in the resonance frequency in the wind turbine...... turbine fault detection and fault tolerant control benchmark model, in which one of the included faults results in a change in the gear box resonance frequency. This evaluation shows the potential of the proposed scheme to monitor the condition of wind turbine gear boxes in the existing control system....

  15. Water Detection Based on Color Variation

    Science.gov (United States)

    Rankin, Arturo L.

    2012-01-01

    This software has been designed to detect water bodies that are out in the open on cross-country terrain at close range (out to 30 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). This detector exploits the fact that the color variation across water bodies is generally larger and more uniform than that of other naturally occurring types of terrain, such as soil and vegetation. Non-traversable water bodies, such as large puddles, ponds, and lakes, are detected based on color variation, image intensity variance, image intensity gradient, size, and shape. At ranges beyond 20 meters, water bodies out in the open can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. But at closer range, the color coming out of a water body dominates sky reflections, and the water cue from sky reflections is of marginal use. Since there may be times during UGV autonomous navigation when a water body does not come into a perception system s field of view until it is at close range, the ability to detect water bodies at close range is critical. Factors that influence the perceived color of a water body at close range are the amount and type of sediment in the water, the water s depth, and the angle of incidence to the water body. Developing a single model of the mixture ratio of light reflected off the water surface (to the camera) to light coming out of the water body (to the camera) for all water bodies would be fairly difficult. Instead, this software detects close water bodies based on local terrain features and the natural, uniform change in color that occurs across the surface from the leading edge to the trailing edge.

  16. Semiology of hypermotor (hyperkinetic) seizures.

    Science.gov (United States)

    Alqadi, Khalid; Sankaraneni, Ram; Thome, Ursula; Kotagal, Prakash

    2016-01-01

    Hypermotor seizures (HMSs) consist of complex movements involving proximal segments of the limbs and trunk that appear violent and inappropriate for the situation. We analyzed hypermotor seizure videos in seizure-free patients (Engel class I) following resective epilepsy surgery. After completion of video analysis, we reviewed EEG and neuroimaging data. Search of our epilepsy surgery database yielded 116 patients classified as having hypermotor seizures between 1996 and 2013. From this subset, 17/31 (55%) patients had been seizure-free for >6months (mean follow-up: 3.3years). Mean seizure duration was 35s (range: 6-91s), of which the HM phase lasted a mean of 22s (range: 3-53s). In 16 patients (95%), hypermotor activity was seen at or within 10s of clinical seizure onset. Type I semiology occurred in 6 patients, type II semiology in 10 patients, and 1 patient exhibited features of both. Type I and type II semiologies were noted in patients who had frontal lobe as well as extrafrontal resections. Nonversive head and body turning occurred in 10 patients (ranging from 90° to 270°) which was ipsilateral to the side of resection in all patients and seen both in frontal and extrafrontal resections. Six out of eleven patients with abnormal MRI and 4/6 patients with nonlesional MRI underwent invasive EEG evaluation. Eight patients (47%) had frontal lobe resection, 4/17 (23%) patients had temporal lobe resection, and one patient each had parietal lobe, insular, temporoparietooccipital, or motor sparing resection; 1 patient had functional hemispherectomy. Hypermotor semiology typically occurs at or within 10s after seizure onset. Ipsilateral head/body turning appears to be of lateralizing value whereas asymmetry of limb movement was not lateralizing. Hypermotor semiology is most often seen in frontal lobe epilepsy but may occur in seizures arising from other locations. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Ionizing particle detection based on phononic crystals

    Energy Technology Data Exchange (ETDEWEB)

    Aly, Arafa H., E-mail: arafa16@yahoo.com, E-mail: arafa.hussien@science.bsu.edu.eg; Mehaney, Ahmed; Eissa, Mostafa F. [Physics Department, Faculty of Science, Beni-Suef University, Beni-Suef (Egypt)

    2015-08-14

    Most conventional radiation detectors are based on electronic or photon collections. In this work, we introduce a new and novel type of ionizing particle detector based on phonon collection. Helium ion radiation treats tumors with better precision. There are nine known isotopes of helium, but only helium-3 and helium-4 are stable. Helium-4 is formed in fusion reactor technology and in enormous quantities during Big Bang nucleo-synthesis. In this study, we introduce a technique for helium-4 ion detection (sensing) based on the innovative properties of the new composite materials known as phononic crystals (PnCs). PnCs can provide an easy and cheap technique for ion detection compared with conventional methods. PnC structures commonly consist of a periodic array of two or more materials with different elastic properties. The two materials are polymethyl-methacrylate and polyethylene polymers. The calculations showed that the energies lost to target phonons are maximized at 1 keV helium-4 ion energy. There is a correlation between the total phonon energies and the transmittance of PnC structures. The maximum transmission for phonons due to the passage of helium-4 ions was found in the case of making polyethylene as a first layer in the PnC structure. Therefore, the concept of ion detection based on PnC structure is achievable.

  18. Skeleton-Based Abnormal Gait Detection

    Directory of Open Access Journals (Sweden)

    Trong-Nguyen Nguyen

    2016-10-01

    Full Text Available Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%.

  19. An FPGA-Based People Detection System

    Directory of Open Access Journals (Sweden)

    James J. Clark

    2005-05-01

    Full Text Available This paper presents an FPGA-based system for detecting people from video. The system is designed to use JPEG-compressed frames from a network camera. Unlike previous approaches that use techniques such as background subtraction and motion detection, we use a machine-learning-based approach to train an accurate detector. We address the hardware design challenges involved in implementing such a detector, along with JPEG decompression, on an FPGA. We also present an algorithm that efficiently combines JPEG decompression with the detection process. This algorithm carries out the inverse DCT step of JPEG decompression only partially. Therefore, it is computationally more efficient and simpler to implement, and it takes up less space on the chip than the full inverse DCT algorithm. The system is demonstrated on an automated video surveillance application and the performance of both hardware and software implementations is analyzed. The results show that the system can detect people accurately at a rate of about 2.5 frames per second on a Virtex-II 2V1000 using a MicroBlaze processor running at 75 MHz, communicating with dedicated hardware over FSL links.

  20. Minimum Electric Field Exposure for Seizure Induction with Electroconvulsive Therapy and Magnetic Seizure Therapy.

    Science.gov (United States)

    Lee, Won H; Lisanby, Sarah H; Laine, Andrew F; Peterchev, Angel V

    2017-05-01

    Lowering and individualizing the current amplitude in electroconvulsive therapy (ECT) has been proposed as a means to produce stimulation closer to the neural activation threshold and more focal seizure induction, which could potentially reduce cognitive side effects. However, the effect of current amplitude on the electric field (E-field) in the brain has not been previously linked to the current amplitude threshold for seizure induction. We coupled MRI-based E-field models with amplitude titrations of motor threshold (MT) and seizure threshold (ST) in four nonhuman primates (NHPs) to determine the strength, distribution, and focality of stimulation in the brain for four ECT electrode configurations (bilateral, bifrontal, right-unilateral, and frontomedial) and magnetic seizure therapy (MST) with cap coil on vertex. At the amplitude-titrated ST, the stimulated brain subvolume (23-63%) was significantly less than for conventional ECT with high, fixed current (94-99%). The focality of amplitude-titrated right-unilateral ECT (25%) was comparable to cap coil MST (23%), demonstrating that ECT with a low current amplitude and focal electrode placement can induce seizures with E-field as focal as MST, although these electrode and coil configurations affect differently specific brain regions. Individualizing the current amplitude reduced interindividual variation in the stimulation focality by 40-53% for ECT and 26% for MST, supporting amplitude individualization as a means of dosing especially for ECT. There was an overall significant correlation between the measured amplitude-titrated ST and the prediction of the E-field models, supporting a potential role of these models in dosing of ECT and MST. These findings may guide the development of seizure therapy dosing paradigms with improved risk/benefit ratio.

  1. Tensor-based spatiotemporal saliency detection

    Science.gov (United States)

    Dou, Hao; Li, Bin; Deng, Qianqian; Zhang, LiRui; Pan, Zhihong; Tian, Jinwen

    2018-03-01

    This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos. First, we construct the tensor representation of video frames. Then, the spatiotemporal saliency can be directly computed by the tensor distance between different tensors, which can preserve the complete temporal and spatial structure information of object in the spatiotemporal domain. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods.

  2. Frequency-based Vehicle Idling Detection

    OpenAIRE

    Kai-Chao Yang; Chih-Ting Kuo; Chun-Yu Chen; Chih-Chyau Yang; Chien-Ming Wu; Chun-Ming Huang

    2014-01-01

    Continuous increases in fuel prices and environmental awareness have raised the importance of reducing vehicle emissions, with many national governments passing anti-idling laws. To reduce air pollution and fuel consumption, we propose a frequency-based vehicle idling detection method to remind drivers to turn off the engine vehicle idling exceeds a certain time threshold. The method is implemented in existing handheld devices without any modification to the car or engine, making the solution...

  3. Autonomic epileptic seizures, autonomic effects of seizures, and SUDEP.

    Science.gov (United States)

    Moseley, Brian; Bateman, Lisa; Millichap, John J; Wirrell, Elaine; Panayiotopoulos, Chrysostomos P

    2013-03-01

    Many generalized tonic-clonic seizures are accompanied by profound autonomic changes. However, autonomic seizures and autonomic status epilepticus can also be seen with specific electroclinical syndromes (Panayiotopoulos syndrome), etiologies, and localizations. Such autonomic symptoms may impact cardiorespiratory function. While it is likely that several factors contribute to SUDEP, further study of both ictal respiratory and cardiac changes and underlying neuroanatomical mechanisms involved in autonomic seizure semiology are likely to provide important data to improve our understanding of the pathophysiology of this devastating condition. This paper will review the association between autonomic symptoms and epileptic seizures and will highlight the work of three young investigators. Drs. Lisa Bateman and Brian Moseley will review their work on cardiorespiratory effects of recorded seizures and how this assists in our understanding of SUDEP. Dr. John Millichap will review autonomic seizures and autonomic dysfunctions related to childhood epilepsy and will discuss the importance of expanded research efforts in this field. Copyright © 2012 Elsevier Inc. All rights reserved.

  4. The best time for EEG recording in febrile seizure.

    Science.gov (United States)

    Karimzadeh, Parvaneh; Rezayi, Alireza; Togha, Mansoureh; Ahmadabadi, Farzad; Derakhshanfar, Hojjat; Azargashb, Eznollah; Khodaei, Fatemeh

    2014-01-01

    Some studies suggest that detection of epileptic discharge is unusual during the first postictal week of febrile seizure and others believe that EEGs carried out on the day of the seizure are abnormal in as many as 88% of the patients. In this study, we intend to compare early and late EEG abnormalities in febrile seizure. EEG was recorded during daytime sleep, 24-48 hours (early EEG) and 2 weeks (late EEG) after the seizure in 36 children with febrile seizure (FS), aged between 3 months and 6 years. EEGs that showed generalized or focal spikes, sharp, spike wave complex, and slowing were considered as abnormal EEG. Abnormalities of the first EEG were compared with those of second EEG. The most common abnormal epileptiform discharges recorded in the early EEG were slow waves (27.6%) and sharp waves in late EEG (36%). Distribution of abnormalities in early and late EEG showed no significant statistical difference. The early and late EEG recording had the same results in patient with febrile seizure.

  5. Monitor for status epilepticus seizures

    Science.gov (United States)

    Johnson, Mark; Simkins, Thomas

    1994-01-01

    This paper describes the sensor technology and associated electronics of a monitor designed to detect the onset of a seizure disorder called status epilepticus. It is a condition that affects approximately 3-5 percent of those individuals suffering from epilepsy. This form of epilepsy does not follow the typical cycle of start-peak-end. The convulsions continue until medically interrupted and are life threatening. The mortality rate is high without prompt medical treatment at a suitable facility. The paper describes the details of a monitor design that provides an inexpensive solution to the needs of those responsible for the care of individuals afflicted with this disorder. The monitor has been designed as a cooperative research and development effort involving the United States Army Armament Research, Development, and Engineering Center's Benet Laboratories (Benet) and the Cerebral Palsy Center for the Disabled (Center), in association with the Department of Neurology at Albany Medical College (AMC). Benet has delivered a working prototype of the device for field testing, in collaboration with Albany Medical College. The Center has identified several children in need of special monitoring and has agreed to pursue commercialization of the device.

  6. Fluorescence quenching based alkaline phosphatase activity detection.

    Science.gov (United States)

    Mei, Yaqi; Hu, Qiong; Zhou, Baojing; Zhang, Yonghui; He, Minhui; Xu, Ting; Li, Feng; Kong, Jinming

    2018-01-01

    Simple and fast detection of alkaline phosphatase (ALP) activity is of great importance for diagnostic and analytical applications. In this work, we report a turn-off approach for the real-time detection of ALP activity on the basis of the charge transfer induced fluorescence quenching of the Cu(BCDS) 2 2- (BCDS = bathocuproine disulfonate) probe. Initially, ALP can enzymatically hydrolyze the substrate ascorbic acid 2-phosphate to release ascorbic acid (AA). Subsequently, the AA-mediated reduction of the Cu(BCDS) 2 2- probe, which displays an intense photoluminescence band at the wavelength of 402nm, leads to the static quenching of fluorescence of the probe as a result of charge transfer. The underlying mechanism of the fluorescence quenching was demonstrated by quantum mechanical calculations. The Cu(BCDS) 2 2- probe features a large Stokes shift (86nm) and is highly immune to photo bleaching. In addition, this approach is free of elaborately designed fluorescent probes and allows the detection of ALP activity in a real-time manner. Under optimal conditions, it provides a fast and sensitive detection of ALP activity within the dynamic range of 0-220mUmL -1 , with a detection limit down to 0.27mUmL -1 . Results demonstrate that it is highly selective, and applicable to the screening of ALP inhibitors in drug discovery. More importantly, it shows a good analytical performance for the direct detection of the endogenous ALP levels of undiluted human serum and even whole blood samples. Therefore, the proposed charge transfer based approach has great potential in diagnostic and analytical applications. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Transient pseudohypoparathyroidism and neonatal seizure.

    Science.gov (United States)

    Manzar, S

    2001-04-01

    The case of a neonate is presented who had late onset seizure associated with hypocalcemia, hyperphosphatemia, and raised parathyroid hormone. The infant did not have any stigmata of pseudohypoparathyroidism. The hypocalcemia was initially resistant to calcium therapy, but responded to vitamin D analog therapy. The diagnosis of 'transient neonatal pseudohypoparathyroidism' was entertained, as the infant remained stable and seizure-free with normal serum biochemistry during 8 months of follow-up.

  8. Mental development of tuberous sclerosis with regard to epileptic seizures and CT findings

    International Nuclear Information System (INIS)

    Katafuchi, Yukihiko; Ishihara, Osamu; Matsuishi, Toyojiro; Shiotsuki, Yuko; Yamaguchi, Yoichiro; Imuta, Fusae

    1985-01-01

    The relation of mental development to epileptic seizures and CT findings was examined in 17 patients with tuberous sclerosis. Epileptic seizures occurred in 16 of the 17 patients. The earlier it occurred, the higher the incidence of mental retardation was. There was no constant correlation between mental development and the type of epileptic seizures or the attainment of inhibition of seizures. In two patients in whom calcification spreading to the cerebral cortex and subcortical region was detected on CT, in addition to calcified tubercles around the cerebral ventricle, an intelligence quotient was significantly lower than in the other patients. (Namekawa, K.)

  9. QRS detection based ECG quality assessment

    International Nuclear Information System (INIS)

    Hayn, Dieter; Jammerbund, Bernhard; Schreier, Günter

    2012-01-01

    Although immediate feedback concerning ECG signal quality during recording is useful, up to now not much literature describing quality measures is available. We have implemented and evaluated four ECG quality measures. Empty lead criterion (A), spike detection criterion (B) and lead crossing point criterion (C) were calculated from basic signal properties. Measure D quantified the robustness of QRS detection when applied to the signal. An advanced Matlab-based algorithm combining all four measures and a simplified algorithm for Android platforms, excluding measure D, were developed. Both algorithms were evaluated by taking part in the Computing in Cardiology Challenge 2011. Each measure's accuracy and computing time was evaluated separately. During the challenge, the advanced algorithm correctly classified 93.3% of the ECGs in the training-set and 91.6 % in the test-set. Scores for the simplified algorithm were 0.834 in event 2 and 0.873 in event 3. Computing time for measure D was almost five times higher than for other measures. Required accuracy levels depend on the application and are related to computing time. While our simplified algorithm may be accurate for real-time feedback during ECG self-recordings, QRS detection based measures can further increase the performance if sufficient computing power is available. (paper)

  10. Maternal thyroid dysfunction and risk of seizure in the child

    DEFF Research Database (Denmark)

    Andersen, Stine Linding; Laurberg, Peter; Wu, Chunsen

    2013-01-01

    Thyroid hormones are essential for brain development, and maternal thyroid disease may affect child neurocognitive development. Some types of seizures may also depend upon early exposure of the developing central nervous system, and we hypothesized that maternal thyroid dysfunction could increase...... the risk of seizure in the child. In a Danish population-based study we included 1,699,693 liveborn singletons, and from the Danish National Hospital Register we obtained information on maternal diagnosis of hyper- or hypothyroidism and neonatal seizure, febrile seizure, and epilepsy in the child. Maternal...... diagnosis of thyroid dysfunction before or after birth of the child was registered in two percent of the singleton births. In adjusted analyses, maternal hyperthyroidism and hypothyroidism first time diagnosed after birth of the child were associated with a significant increased risk of epilepsy...

  11. Reset Tree-Based Optical Fault Detection

    Directory of Open Access Journals (Sweden)

    Howon Kim

    2013-05-01

    Full Text Available In this paper, we present a new reset tree-based scheme to protect cryptographic hardware against optical fault injection attacks. As one of the most powerful invasive attacks on cryptographic hardware, optical fault attacks cause semiconductors to misbehave by injecting high-energy light into a decapped integrated circuit. The contaminated result from the affected chip is then used to reveal secret information, such as a key, from the cryptographic hardware. Since the advent of such attacks, various countermeasures have been proposed. Although most of these countermeasures are strong, there is still the possibility of attack. In this paper, we present a novel optical fault detection scheme that utilizes the buffers on a circuit’s reset signal tree as a fault detection sensor. To evaluate our proposal, we model radiation-induced currents into circuit components and perform a SPICE simulation. The proposed scheme is expected to be used as a supplemental security tool.

  12. Early-Onset Convulsive Seizures Induced by Brain Hypoxia-Ischemia in Aging Mice: Effects of Anticonvulsive Treatments.

    Directory of Open Access Journals (Sweden)

    Justin Wang

    Full Text Available Aging is associated with an increased risk of seizures/epilepsy. Stroke (ischemic or hemorrhagic and cardiac arrest related brain injury are two major causative factors for seizure development in this patient population. With either etiology, seizures are a poor prognostic factor. In spite of this, the underlying pathophysiology of seizure development is not well understood. In addition, a standardized treatment regimen with anticonvulsants and outcome assessments following treatment has yet to be established for these post-ischemic seizures. Previous studies have modeled post-ischemic seizures in adult rodents, but similar studies in aging/aged animals, a group that mirrors a higher risk elderly population, remain sparse. Our study therefore aimed to investigate early-onset seizures in aging animals using a hypoxia-ischemia (HI model. Male C57 black mice 18-20-month-old underwent a unilateral occlusion of the common carotid artery followed by a systemic hypoxic episode (8% O2 for 30 min. Early-onset seizures were detected using combined behavioral and electroencephalographic (EEG monitoring. Brain injury was assessed histologically at different times post HI. Convulsive seizures were observed in 65% of aging mice post-HI but not in control aging mice following either sham surgery or hypoxia alone. These seizures typically occurred within hours of HI and behaviorally consisted of jumping, fast running, barrel-rolling, and/or falling (loss of the righting reflex with limb spasms. No evident discharges during any convulsive seizures were seen on cortical-hippocampal EEG recordings. Seizure development was closely associated with acute mortality and severe brain injury on brain histological analysis. Intra-peritoneal injections of lorazepam and fosphenytoin suppressed seizures and improved survival but only when applied prior to seizure onset and not after. These findings together suggest that seizures are a major contributing factor to acute

  13. Neonatal seizures associated with cerebral lesions shown by magnetic resonance imaging

    DEFF Research Database (Denmark)

    Leth, H; Toft, P.B.; Herning, Gudrun Margrethe

    1997-01-01

    AIM: To determine the diagnostic potential of magnetic resonance imaging (MRI) in neonatal seizures; to elucidate the aetiology, timing, and prognosis of the cerebral lesions detected. METHODS: Thirty one term neonates with clinical seizures underwent ultrasonography between days 1-7 (mean 2.5 days...

  14. Global contrast based salient region detection

    KAUST Repository

    Cheng, Ming-Ming

    2011-08-25

    Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, and adaptive compression. We propose a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence. The proposed algorithm is simple, efficient, and yields full resolution saliency maps. Our algorithm consistently outperformed existing saliency detection methods, yielding higher precision and better recall rates, when evaluated using one of the largest publicly available data sets. We also demonstrate how the extracted saliency map can be used to create high quality segmentation masks for subsequent image processing.

  15. DNA & Protein detection based on microbead agglutination

    KAUST Repository

    Kodzius, Rimantas

    2012-06-06

    We report a simple and rapid room temperature assay for point-of-care (POC) testing that is based on specific agglutination. Agglutination tests are based on aggregation of microparticles in the presence of a specific analyte thus enabling the macroscopic observation. Agglutination-based tests are most often used to explore the antibody-antigen reactions. Agglutination has been used for mode protein assays using a biotin/streptavidin two-component system, as well as a hybridization based two-component assay; however, as our work shows, two-component systems are prone to self-termination of the linking analyte and thus have a lower sensitivity. Three component systems have also been used with DNA hybridization, as in our work; however, their assay requires 48 hours for incubation, while our assay is performed in 5 minutes making it a real candidate for POC testing. We demonstrate three assays: a two-component biotin/streptavidin assay, a three-component hybridization assay using single stranded DNA (ssDNA) molecules and a stepped three-component hybridization assay. The comparison of these three assays shows our simple stepped three-component agglutination assay to be rapid at room temperature and more sensitive than the two-component version by an order of magnitude. An agglutination assay was also performed in a PDMS microfluidic chip where agglutinated beads were trapped by filter columns for easy observation. We developed a rapid (5 minute) room temperature assay, which is based on microbead agglutination. Our three-component assay solves the linker self-termination issue allowing an order of magnitude increase in sensitivity over two–component assays. Our stepped version of the three-component assay solves the issue with probe site saturation thus enabling a wider range of detection. Detection of the agglutinated beads with the naked eye by trapping in microfluidic channels has been shown.

  16. Scene change detection based on multimodal integration

    Science.gov (United States)

    Zhu, Yingying; Zhou, Dongru

    2003-09-01

    Scene change detection is an essential step to automatic and content-based video indexing, retrieval and browsing. In this paper, a robust scene change detection and classification approach is presented, which analyzes audio, visual and textual sources and accounts for their inter-relations and coincidence to semantically identify and classify video scenes. Audio analysis focuses on the segmentation of audio stream into four types of semantic data such as silence, speech, music and environmental sound. Further processing on speech segments aims at locating speaker changes. Video analysis partitions visual stream into shots. Text analysis can provide a supplemental source of clues for scene classification and indexing information. We integrate the video and audio analysis results to identify video scenes and use the text information detected by the video OCR technology or derived from transcripts available to refine scene classification. Results from single source segmentation are in some cases suboptimal. By combining visual, aural features adn the accessorial text information, the scence extraction accuracy is enhanced, and more semantic segmentations are developed. Experimental results are proven to rather promising.

  17. Waveguide-Based Biosensors for Pathogen Detection

    Directory of Open Access Journals (Sweden)

    Nile Hartman

    2009-07-01

    Full Text Available Optical phenomena such as fluorescence, phosphorescence, polarization, interference and non-linearity have been extensively used for biosensing applications. Optical waveguides (both planar and fiber-optic are comprised of a material with high permittivity/high refractive index surrounded on all sides by materials with lower refractive indices, such as a substrate and the media to be sensed. This arrangement allows coupled light to propagate through the high refractive index waveguide by total internal reflection and generates an electromagnetic wave—the evanescent field—whose amplitude decreases exponentially as the distance from the surface increases. Excitation of fluorophores within the evanescent wave allows for sensitive detection while minimizing background fluorescence from complex, “dirty” biological samples. In this review, we will describe the basic principles, advantages and disadvantages of planar optical waveguide-based biodetection technologies. This discussion will include already commercialized technologies (e.g., Corning’s EPIC® Ô, SRU Biosystems’ BIND™, Zeptosense®, etc. and new technologies that are under research and development. We will also review differing assay approaches for the detection of various biomolecules, as well as the thin-film coatings that are often required for waveguide functionalization and effective detection. Finally, we will discuss reverse-symmetry waveguides, resonant waveguide grating sensors and metal-clad leaky waveguides as alternative signal transducers in optical biosensing.

  18. Localization value of seizure semiology analyzed by the conditional inference tree method.

    Science.gov (United States)

    Kim, Dong Wook; Jung, Ki-Young; Chu, Kon; Park, So-Hee; Lee, Seo-Young; Lee, Sang Kun

    2015-09-01

    Although accurate interpretation of seizures is important for the management of patients with epilepsy, studies on the localizing value of seizure semiology and the reliability of the semiology descriptions are scarce. The objective of our study is to investigate the accuracy of video-recorded seizure semiology in the classification and localization of epileptic seizures. We also evaluated the reliability of the semiology descriptions provided by the patients or their caregivers. Video-recorded clinical seizures from 831 consecutive patients (391 females; 31.7 ± 11.6 years) were analyzed retrospectively. Epileptic seizures were classified as generalized and partial seizures, and patients with partial seizures were further divided into five ictal onset areas. In order to analyze the diagnostic value of individual semiologic features for clinical diagnosis, we used the conditional inference tree method. Generalized and partial seizures were differentiated with high accuracy (97.1%), but the accuracy of localization among the five ictal onset areas was relatively low (56.1%), which was largely attributed to the difficulty in the discrimination between mesial and lateral temporal onset seizures. Lateralization of the ictal onset area in partial seizures was possible in 427 (55.1%) patients based on video analysis, nevertheless it was possible in only 158 (20.4%) patients based on historical semiology descriptions. The results of our study suggest that careful observation of seizure semiology may be useful for the differentiation of ictal onset areas. However, the semiologic differentiation between mesial and lateral temporal onset seizures is difficult, and historical semiologic descriptions should be interpreted carefully because of their low reliability. Copyright © 2015 Elsevier B.V. All rights reserved.

  19. The prognosis of acute symptomatic seizures after ischaemic stroke.

    Science.gov (United States)

    Leung, Thomas; Leung, Howan; Soo, Yannie O Y; Mok, Vincent C T; Wong, K S

    2017-01-01

    Acute symptomatic seizure (AS) after ischaemic stroke is defined as a seizure occurring ≤7 days of the stroke. There remains a lack of information on the prognosis of AS after ischaemic stroke and how it should be treated. We prospectively recruited patients after their incidents of ischaemic stroke from a population-based stroke registry. Stroke aetiology was defined according to Trial-of-ORG-10172 in acute-stroke treatment (TOAST). Patients were examined for any transient complete-occlusion with recanalisation (TCOR) and haemorrhagic transformation. The seizure outcomes were (1) acute clustering of seizures ≤7 days, (2) seizure recurrence associated with stroke recurrence beyond the 7-day period and (3) unprovoked seizure (US) >7 days. 104 patients (mean age 65 years/55% female) with AS after ischaemic stroke were identified (mean follow-up 6.17 years). Comparison of the group of patients with AS and those without seizures showed that patients with AS had significantly less large-vessel and small-vessel disease but more cardioembolisms (pstroke beyond 7 days was 13.5% at 2 years, 16.4% at 4 years and 18% at 8 years. Presence of >2 cardiovascular risk factors (pischaemic stroke may appear as acute clustering. Afterwards, seizures may occur as often with a recurrent stroke as without one within 4.2 years. We recommend the use of antiepileptic agents for up to 4 years if the underlying stroke aetiology cannot be fully treated. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  20. Vagus nerve stimulation magnet activation for seizures: a critical review.

    Science.gov (United States)

    Fisher, R S; Eggleston, K S; Wright, C W

    2015-01-01

    Some patients receiving VNS Therapy report benefit from manually activating the generator with a handheld magnet at the time of a seizure. A review of 20 studies comprising 859 subjects identified patients who reported on-demand magnet mode stimulation to be beneficial. Benefit was reported in a weighted average of 45% of patients (range 0-89%) using the magnet, with seizure cessation claimed in a weighted average of 28% (range 15-67%). In addition to seizure termination, patients sometimes reported decreased intensity or duration of seizures or the post-ictal period. One study reported an isolated instance of worsening with magnet stimulation (Arch Pediatr Adolesc Med, 157, 2003 and 560). All of the reviewed studies assessed adjunctive magnet use. No studies were designed to provide Level I evidence of efficacy of magnet-induced stimulation. Retrospective analysis of one pivotal randomized trial of VNS therapy showed significantly more seizures terminated or improved in the active stimulation group vs the control group. Prospective, controlled studies would be required to isolate the effect and benefit of magnet mode stimulation and to document that the magnet-induced stimulation is the proximate cause of seizure reduction. Manual application of the magnet to initiate stimulation is not always practical because many patients are immobilized or unaware of their seizures, asleep or not in reach of the magnet. Algorithms based on changes in heart rate at or near the onset of the seizure provide a methodology for automated responsive stimulation. Because literature indicates additional benefits from on-demand magnet mode stimulation, a potential role exists for automatic activation of stimulation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  1. Seizure Prediction: Science Fiction or Soon to Become Reality?

    Science.gov (United States)

    Freestone, Dean R; Karoly, Philippa J; Peterson, Andre D H; Kuhlmann, Levin; Lai, Alan; Goodarzy, Farhad; Cook, Mark J

    2015-11-01

    This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.

  2. Prevalence and predictors of subclinical seizures during scalp video-EEG monitoring in patients with epilepsy.

    Science.gov (United States)

    Jin, Bo; Wang, Shan; Yang, Linglin; Shen, Chunhong; Ding, Yao; Guo, Yi; Wang, Zhongjin; Zhu, Junming; Wang, Shuang; Ding, Meiping

    2017-08-01

    This study first aimed to establish the prevalence and predictors of subclinical seizures in patients with epilepsy undergoing video electroencephalographic monitoring, then to evaluate the relationship of sleep/wake and circadian pattern with subclinical seizures. We retrospectively reviewed the charts of 742 consecutive patients admitted to our epilepsy center between July 2012 and October 2014. Demographic, electro-clinical data and neuroimage were collected. A total of 148 subclinical seizures were detected in 39 patients (5.3%) during video electroencephalographic monitoring. The mean duration of subclinical seizures was 47.18 s (range, 5-311). Pharmacoresistant epilepsy, abnormal MRI and the presence of interictal epileptiform discharges were independently associated with subclinical seizures in multivariate logistic regression analysis. Subclinical seizures helped localizing the presumed epileptogenic zone in 24 (61.5%) patients, and suggested multifocal epilepsy in five (12.8%). In addition, subclinical seizures occurred more frequently in sleep and night than wakefulness and daytime, respectively, and they were more likely seen between 21:00-03:00 h, and less likely seen between 09:00-12:00 h. Thirty patients (76.9%) had their first subclinical seizures within the first 24 h of monitoring while only 7.7% of patients had their first subclinical seizures detected within 20 min. Subclinical seizures are not uncommon in patients with epilepsy, particularly in those with pharmacoresistant epilepsy, abnormal MRI or interictal epileptiform discharges. Subclinical seizures occur in specific circadian patterns and in specific sleep/wake distributions. A 20-min VEEG monitoring might not be long enough to allow for their detection.

  3. Knowledge and Attitude on Febrile Seizure among Mothers with Under-Five Children

    Directory of Open Access Journals (Sweden)

    Jihan Alifa Syahida

    2016-12-01

    Full Text Available Background: Febrile seizures frequently occur in children under 5 years old and usually create fear and anxiety among parents. Poor understanding of febrile seizure among parents contributes to mismanagement of seizure. The objective of this study was to identify the knowledge and attitude on febrile seizure among mothers of under five children. Methods: This descriptive community-based survey comprised of 96 mothers with under 5 children who were chosen through randomization. This survey was, conducted in Hegarmanah Village, Jatinangor, West Java, Indonesia in October 2013. Data were collected using a questionnaire and analyzed using frequency analysis. Results: Fifty nine respondents (61% considered that high fever in their children will result in seizure and 63 mothers (65% stated that this condition was a life-threatening situation which could lead to brain damage (50% and paralysis (50%. There were some respondents who would manage seizure by shaking (27% or holding the child tightly during seizure (22% and putting spoon into the children mouth (59%. Sixty respondents (62.5% prevented febrile seizure by giving them coffee. Conclusions: Knowledge and attitude regarding febrile seizure is good, but the knowledge and attitude towards the outcome and what to do during febrile seizures occasion are still poor.

  4. Seizure-susceptible brain regions in glioblastoma: identification of patients at risk.

    Science.gov (United States)

    Cayuela, N; Simó, M; Majós, C; Rifà-Ros, X; Gállego Pérez-Larraya, J; Ripollés, P; Vidal, N; Miró, J; Gil, F; Gil-Gil, M; Plans, G; Graus, F; Bruna, J

    2018-02-01

    The main aim of this study was to identify which patients with glioblastoma multiforme (GBM) have a higher risk of presenting seizures during follow-up. Patients with newly diagnosed GBM were reviewed (n = 306) and classified as patients with (Group 1) and without (Group 2) seizures at onset. Group 2 was split into patients with seizures during follow-up (Group 2A) and patients who never had seizures (Group 2B). The anatomical location of GBM was identified and compared by voxel-based lesion symptom mapping (discovery set). Seizure-susceptible brain regions obtained were assessed visually and automatically in external GBM validation series (n = 85). In patients with GBM who had no seizures at onset, an increased risk of presenting seizures during follow-up was identified in the superior frontal and inferior occipital lobe, as well as in inferoposterior regions of the temporal lobe. Conversely, those patients with GBM located in medial and inferoanterior temporal areas had a significantly lower risk of suffering from seizures during follow-up. Additionally, the seizure-susceptible brain region maps obtained classified patients in the validation set with high positive and negative predictive values. Tumor location is a useful marker to identify patients with GBM who are at risk of suffering from seizures during follow-up. These results may help to support the use of antiepileptic prophylaxis in a selected GBM population and to improve stratification in antiepileptic clinical trials. © 2017 EAN.

  5. Attribute and topology based change detection in a constellation of previously detected objects

    Science.gov (United States)

    Paglieroni, David W.; Beer, Reginald N.

    2016-01-19

    A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

  6. Low-Power, 8-Channel EEG Recorder and Seizure Detector ASIC for a Subdermal Implantable System.

    Science.gov (United States)

    Do Valle, Bruno G; Cash, Sydney S; Sodini, Charles G

    2016-12-01

    EEG remains the mainstay test for the diagnosis and treatment of patients with epilepsy. Unfortunately, ambulatory EEG systems are far from ideal for patients who have infrequent seizures. These systems only last up to 3 days and if a seizure is not captured during the recordings, a definite diagnosis of the patient's condition cannot be given. This work aims to address this need by proposing a subdermal implantable, eight-channel EEG recorder and seizure detector that has two modes of operation: diagnosis and seizure counting. In the diagnosis mode, EEG is continuously recorded until a number of seizures are recorded. In the seizure counting mode, the system uses a low-power algorithm to track the number of seizures a patient has, providing doctors with a reliable count to help determine medication efficacy or other clinical endpoint. An ASIC that implements the EEG recording and seizure detection algorithm was designed and fabricated in a 0.18 μm CMOS process. The ASIC includes eight EEG channels and is designed to minimize the system's power and size. The result is a power-efficient analog front end that requires 2.75 μW per channel in diagnosis mode and 0.84 μW per channel in seizure counting mode. Both modes have an input referred noise of approximately 1.1 μVrms.

  7. A buffer overflow detection based on inequalities solution

    International Nuclear Information System (INIS)

    Xu Guoai; Zhang Miao; Yang Yixian

    2007-01-01

    A new buffer overflow detection model based on Inequalities Solution was designed, which is based on analyzing disadvantage of the old buffer overflow detection technique and successfully converting buffer overflow detection to Inequalities Solution. The new model can conquer the disadvantage of the old technique and improve efficiency of buffer overflow detection. (authors)

  8. Explosive Blast Neuropathology and Seizures

    Directory of Open Access Journals (Sweden)

    S. Krisztian eKovacs

    2014-04-01

    Full Text Available Traumatic brain injury (TBI due to explosive blast exposure is a leading combat casualty. It is also implicated as a key contributor to war related mental health diseases. A clinically important consequence of all types of TBI is a high risk for development of seizures and epilepsy. Seizures have been reported in patients who have suffered blast injuries in the Global War on Terror but the exact prevalence is unknown. The occurrence of seizures supports the contention that explosive blast leads to both cellular and structural brain pathology. Unfortunately, the exact mechanism by which explosions cause brain injury is unclear, which complicates development of meaningful therapies and mitigation strategies. To help improve understanding, detailed neuropathological analysis is needed. For this, histopathological techniques are extremely valuable and indispensable. In the following we will review the pathological results, including those from immunohistochemical and special staining approaches, from recent preclinical explosive blast studies.

  9. Varying seizure semiology according to age.

    Science.gov (United States)

    Nordli, Douglas R

    2013-01-01

    The clinical manifestations of seizures change in a predictable fashion with advancing age. For focal seizures these changes can be summarized into domains similar to those used in developmental models. These include fine motor, communication, and gross motor manifestations. Instead of socialization the fourth domain for seizure semiology concerns synchronization. Focal seizures in the very young tend to be simpler with fewer fine motor manifestations. Auras are uncommon, even in young children with some linguistic skill and it is often difficult to discern alteration of consciousness. Infantile focal seizures can present with spasms or even diffuse tonic seizures. In terms of synchronization, orderly secondary generalization is rarely seen so that primary generalized clonic seizures are rarely recorded in infants. Amongst so-called "generalized" seizures spasms are most often seen in the first year of life. Absence seizures, myoclonic-astatic and generalized tonic-clonic seizures are all usually not seen until after age 2 years. A full description of the clinical details of seizures is probably the most important part of the epilepsy history. A detailed knowledge of seizure semiology can make the history more effective and also in the identification of the correct seizure classification. Copyright © 2013 Elsevier B.V. All rights reserved.

  10. Smell Detection Agent Based Optimization Algorithm

    Science.gov (United States)

    Vinod Chandra, S. S.

    2016-09-01

    In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems.

  11. Detecting Soft Errors in Stencil based Computations

    Energy Technology Data Exchange (ETDEWEB)

    Sharma, V. [Univ. of Utah, Salt Lake City, UT (United States); Gopalkrishnan, G. [Univ. of Utah, Salt Lake City, UT (United States); Bronevetsky, G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-05-06

    Given the growing emphasis on system resilience, it is important to develop software-level error detectors that help trap hardware-level faults with reasonable accuracy while minimizing false alarms as well as the performance overhead introduced. We present a technique that approaches this idea by taking stencil computations as our target, and synthesizing detectors based on machine learning. In particular, we employ linear regression to generate computationally inexpensive models which form the basis for error detection. Our technique has been incorporated into a new open-source library called SORREL. In addition to reporting encouraging experimental results, we demonstrate techniques that help reduce the size of training data. We also discuss the efficacy of various detectors synthesized, as well as our future plans.

  12. Detection of epileptiform activity in EEG signals based on time-frequency and nonlinear analysis

    Directory of Open Access Journals (Sweden)

    Dragoljub eGajic

    2015-03-01

    Full Text Available We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using nonlinear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved.

  13. A case for cannabidiol in Wolf-Hirschhorn syndrome seizure management.

    Science.gov (United States)

    Ho, Karen S; Wassman, E Robert

    2017-02-01

    Complex, and sometimes intractable, seizures affect the quality of life and cognitive development of over 90% of individuals with Wolf-Hirschhorn syndrome (WHS). Fine resolution genotype-phenotype mapping of the WHS locus recently identified a candidate gene whose probable function has led to insights into a mechanism connecting WHS seizures with those of Dravet syndrome, a distinct condition caused by mutations in SCN1A and SCN1B. In addition to this possible molecular mechanistic connection, these disorders' seizures share a strikingly similar constellation of features, including clinical presentation, seizure types, early age of onset, EEG pattern, and responses to specific anti-epileptic drugs. Based in part on these similarities, we suggest that a highly successful Phase III clinical trial of a formulation of cannabidiol for Dravet syndrome seizures may be directly translatable into possible benefits for WHS individuals with challenging seizure patterns. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  14. Single electron based binary multipliers with overflow detection ...

    African Journals Online (AJOL)

    electron based device. Multipliers with overflow detection based on serial and parallel prefix computation algorithm are elaborately discussed analytically and designed. The overflow detection circuits works in parallel with a simplified multiplier to ...

  15. COMPUTED TOMOGRAPHIC EVALUATION OF SEIZURES (EPILEPSY IN PEDIATRIC AGE GROUP

    Directory of Open Access Journals (Sweden)

    Prasad

    2015-10-01

    no age is spared. Generalized Seizures accounted for 63.85% in our local Pediat ric age group. We were able to detect various epileptogenic foci, in 61.4% (40 cases. And with available CT findings we could able to distinguish and diagnose various Pathological lesions, including infections, congenital anomalies, calcified lesions and neoplasm. CECT should be done in evaluation of low and mixed density lesions, CECT helped more in evaluation of low density lesions and revealed ring enhancing lesions. Delayed (5 - 7 min and thin (2mm sections are more useful. Ring entrancing lesions due to infections are the commonest epileptogenic foci in our study accounting for 28%. And CECT is not helped much in evaluation other than low density lesions. As normal neurological examination can’t rule out an epileptogenic focus. Therefore CT brain shou ld be included. INTERPRETATION AND CONCLUSION: Based on our study and on reviewing the literature, we are able to detect epileptogenic foci. CECT is very essential is evaluation of low density lesions. Only on contrast study we can detect and distinguish v arious lesions. Moreover delayed and thin sections are more useful CECT is may not play any role in evaluation of most of the congenital anomalies and calcified lesions. Ring en hancing lesions of infections etiology especially NCC are the commonest epilept ogenic foci in our local paediatric population. Therefore CT is the prime mode of investigation is evaluation of epilepsy in Paediatric

  16. EEG and CT findings of infant partial seizures

    International Nuclear Information System (INIS)

    Kajitani, Takashi; Kumanomido, Yoshiaki; Nakamura, Makoto; Ueoka, Kiyotaka

    1981-01-01

    Examination of EEG and cranial CT were performed in 19 cases of partial seizures with elementary symptomatology (PSES), 6 cases of partial seizures with complex symptomatology (PSCS), and 17 cases of benign focal pilepsy of childhood with Rolandic spikes (BFECRS). The results were as follows. 1) In 16 of 19 cases of PSES (84%), various abnormal CT findings such as localized cerebral atrophy (7 cases), localized cerebral atrophy complicated with porencephaly (4 cases), porencephaly alone (2 cases), and diffuse cerebral atrophy (3 cases) were found. 2) Of 6 cases of PSCS localized cerebral atrophy was found in 3 cases, porencephaly in one case, and localized calcification in one case. Normal CT findings were obtained in one case. 3) In comparison of EEG findings with CT findings in 25 cases of partial seizures CT findings correlated with the basic waves rather than the paroxysmal ones. 4) The fact that CT findings in patients with BFECRS were mostly normal suggests the functional origin of the seizures. 5) CT was valuable in partial seizures for detecting underlying disorders and predicting the prognosis. (Ueda, J.)

  17. Etiology and Outcome of Neonatal Seizures

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2006-04-01

    Full Text Available The prognostic value of seizure etiology, neurologic examination, EEG, and neuroimaging in the neurodevelopmental outcome of 89 term infants with neonatal seizures was determined at the Children’s Hospital and Harvard Medical School, Boston, MA.

  18. Similar semiology of epileptic and psychogenic nonepileptic seizures recorded during stereo-EEG.

    Science.gov (United States)

    Ostrowsky-Coste, Karine; Montavont, Alexandra; Keo-Kosal, Pascale; Guenot, Marc; Chatillon, Claude-Edouard; Ryvlin, Philippe

    2013-12-01

    We report two adolescents with refractory seizure disorders in whom both epileptic and psychogenic nonepileptic seizures (PNES) were recorded with intracerebral EEG. The ictal phenomenology of epileptic seizures (ES) and PNES, consisting of hypermotor attacks in the first patient and left-sided painful episodes in the second patient, proved remarkably similar in both cases, highlighting the difficulties which can arise with the distinction of epileptic seizures and PNES based on ictal phenomenology alone. Copyright © 2013 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  19. Characterization of seizure-like events recorded in vivo in a mouse model of Rett syndrome.

    Science.gov (United States)

    Colic, Sinisa; Wither, Robert G; Zhang, Liang; Eubanks, James H; Bardakjian, Berj L

    2013-10-01

    Rett syndrome is a neurodevelopmental disorder caused by mutations in the X-linked gene encoding methyl-CpG-binding protein 2 (MECP2). Spontaneous recurrent discharge episodes are displayed in Rett-related seizures as in other types of epilepsies. The aim of this paper is to investigate the seizure-like event (SLE) and inter-SLE states in a female MeCP2-deficient mouse model of Rett syndrome and compare them to those found in other spontaneous recurrent epilepsy models. The study was performed on a small population of female MeCP2-deficient mice using telemetric local field potential (LFP) recordings over a 24 h period. Durations of SLEs and inter-SLEs were extracted using a rule-based automated SLE detection system for both daytime and nighttime, as well as high and low power levels of the delta frequency range (0.5-4 Hz) of the recorded LFPs. The results suggest SLE occurrences are not influenced by circadian rhythms, but had a significantly greater association with delta power. Investigating inter-SLE and SLE states by fitting duration histograms to the gamma distribution showed that SLE initiation and termination were associated with random and deterministic mechanisms, respectively. These findings when compared to reported studies on epilepsy suggest that Rett-related seizures share many similarities with absence epilepsy. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  20. Fast automatic analysis of antenatal dexamethasone on micro-seizure activity in the EEG

    International Nuclear Information System (INIS)

    Rastin, S.J.; Unsworth, C.P.; Bennet, L.

    2010-01-01

    Full text: In this work wc develop an automatic scheme for studying the effect of the antenatal Dexamethasone on the EEG activity. To do so an FFT (Fast Fourier Transform) based detector was designed and applied to the EEG recordings obtained from two groups of fetal sheep. Both groups received two injections with a time delay of 24 h between them. However the applied medicine was different for each group (Dex and saline). The detector developed was used to automatically identify and classify micro-seizures that occurred in the frequency bands corresponding to the EEG transients known as slow waves (2.5 14 Hz). For each second of the data recordings the spectrum was computed and the rise of the energy in each predefined frequency band then counted when the energy level exceeded a predefined corresponding threshold level (Where the threshold level was obtained from the long term average of the spectral points at each band). Our results demonstrate that it was possible to automatically count the micro-seizures for the three different bands in a time effective manner. It was found that the number of transients did not strongly depend on the nature of the injected medicine which was consistent with the results manually obtained by an EEG expert. Tn conclusion, the automatic detection scheme presented here would allow for rapid micro-seizure event identification of hours of highly sampled EEG data thus providing a valuable time-saving device.

  1. Fault detection based on microseismic events

    Science.gov (United States)

    Yin, Chen

    2017-09-01

    In unconventional reservoirs, small faults allow the flow of oil and gas as well as act as obstacles to exploration; for, (1) fracturing facilitates fluid migration, (2) reservoir flooding, and (3) triggering of small earthquakes. These small faults are not generally detected because of the low seismic resolution. However, such small faults are very active and release sufficient energy to initiate a large number of microseismic events (MEs) during hydraulic fracturing. In this study, we identified microfractures (MF) from hydraulic fracturing and natural small faults based on microseismicity characteristics, such as the time-space distribution, source mechanism, magnitude, amplitude, and frequency. First, I identified the mechanism of small faults and MF by reservoir stress analysis and calibrated the ME based on the microseismic magnitude. The dynamic characteristics (frequency and amplitude) of MEs triggered by natural faults and MF were analyzed; moreover, the geometry and activity types of natural fault and MF were grouped according to the source mechanism. Finally, the differences among time-space distribution, magnitude, source mechanism, amplitude, and frequency were used to differentiate natural faults and manmade fractures.

  2. Assessing Systems of Care for US Children with Epilepsy/Seizure Disorder

    Science.gov (United States)

    Kenney, Mary Kay; Mann, Marie

    2013-01-01

    Background. The proportion of US children with special health care needs (CSHCN) with epilepsy/seizure disorder who receive care in high-quality health service systems was examined. Methodology. We analyzed data for 40,242 CSHCN from the 2009-2010 National Survey of CSHCN and compared CSHCN with epilepsy/seizure disorder to CSHCN without epilepsy/seizure disorder. Measures included attainment rates for 6 federal quality indicators with comparisons conducted using chi square and logistic regression methods. In addition, CSHCN with epilepsy/seizure disorder were compared to CSHCN without epilepsy/seizure disorder on the basis of 14 unmet health care needs. Results. Lower attainment rates for receiving comprehensive care in a medical home and easily accessible community-based services were found for CSHCN with epilepsy/seizure disorder versus CSHCN without epilepsy/seizure disorder (medical home: 32% versus 43%; accessible community-based services: 50% versus 66%, resp.) in unadjusted analyses. Lower adjusted odds for these indicators as well as greater unmet need for specialists, dentistry, prescriptions, therapies, and mental health care were also found for CSHCN with epilepsy/seizure disorder. Conclusions. Further efforts are needed to improve attainment of high-quality health care services for CSHCN with epilepsy/seizure disorders. PMID:24228175

  3. Cardiac Troponin I elevation after epileptic seizure

    Directory of Open Access Journals (Sweden)

    Sieweke Nicole

    2012-07-01

    Full Text Available Abstract Background Cardiac troponin-I (cTNI is highly specific biomarker to prove myocardial damage, e.g. in acute coronary syndrome (ACS. However, it occurs in other conditions as well. We therefore analysed cTNI increase in patients after generalized convulsive seizure. Methods Consecutive patients admitted with acute generalized convulsive seizure were included in case of cTNI measurement on admission. Among 898 selected cases, 53 patients were referred secondary to our department; in 845 cases cTNI measurements on admission were available. In case of multiple admissions (81 cases, only the first admission entered our analysis. In 17 patients elevated cTNI was determined due to ACS; in one patient a myocarditis was found. 5 patients suffered of relevant renal insufficiency. Finally 741 patients were included in the analysis. A cTNI cut-off level of ≥ 0.1 ng/ml was considered. Factors associated with a cTNI increase were analysed subsequently. Results The mean age of the study population (n = 741 was 47.8 years (SD ± 18.6, 40.9% were female. In 50 patients (6.7% a cTNI elevation of unknown origin was found; no obvious cardiac involvement could be detected in these patients who all remained asymptomatic. A vascular risk profile (including at least hypertension, hypercholesterolemia or diabetes (OR = 3.62; CI: 1.59 to 8.21; p = 0.001 and elevated creatine kinase on admission (OR = 2.36; CI: 1.26 to 4.39; p = 0.002 were independent factors associated with cTNI release. Conclusion cTNI release occurs in patients with generalized convulsive seizure with predominance in patients with vascular risk profile.

  4. Cellular telephone-based radiation detection instrument

    Energy Technology Data Exchange (ETDEWEB)

    Craig, William W [Pittsburg, CA; Labov, Simon E [Berkeley, CA

    2011-06-14

    A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

  5. Migrating Partial Seizures of Infancy

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2013-06-01

    Full Text Available A national surveillance study in conjunction with the British Paediatric Neurology Unit was undertaken to further define the clinical, pathological and molecular genetic features of migrating partial seizures of infancy (MPSI, a rare early infantile epileptic encephalopathy with poor prognosis.

  6. Cortical gene expression: prognostic value for seizure outcome following temporal lobectomy and amygdalohippocampectomy.

    Science.gov (United States)

    Gallek, Matthew J; Skoch, Jesse; Ansay, Tracy; Behbahani, Mandana; Mount, David; Manziello, Ann; Witte, Marlys; Bernas, Michael; Labiner, David M; Weinand, Martin E

    2016-10-01

    Whole genome analyses were performed to test the hypothesis that temporal cortical gene expression differs between epilepsy patients rendered seizure-free versus non-seizure-free following anterior temporal lobectomy with amygdalohippocampectomy (ATL/AH). Twenty four patients underwent ATL/AH to treat medically intractable seizures of temporal lobe origin (mean age 35.5 years, mean follow-up 42.2 months); they were then dichotomized into seizure-free and non-seizure-free groups. Tissue RNA was isolated from the lateral temporal cortex and gene expression analysis was performed. Whole genome data were analyzed for prognostic value for seizure-free outcome following ATL/AH by logistic regression. Genes that could distinguish seizure outcome groups were identified based on providing an accuracy of >0.90 judging by area under the receiver operating characteristic curve, AUC, with a P value of the slope coefficient of <0.05. Four genes and seven RNA probes were with prognostic value for post-operative seizure-free outcome. Gene expression associated with seizure-free outcome included relative down-regulation of zinc finger protein 852 (ZNF852), CUB domain-containing protein 2 (CDCP2), proline-rich transmembrane protein 1 (PRRT1), hypothetical LOC440200 (FLJ41170), RNA probe 8047763, RNA probe 8126238, RNA probe 8113489, RNA probe 8092883, RNA probe 7935228, RNA probe 806293, and RNA probe 8104131. This study describes the predictive value of temporal cortical gene expression for seizure-free outcome after ATL/AH. Four genes and seven RNA probes were found to predict post-operative seizure-free outcome. Future prospective investigation of these genes and probes in human brain tissue and blood could establish new biomarkers predictive of seizure outcome following ATL/AH.

  7. Pretreatment seizure semiology in childhood absence epilepsy.

    Science.gov (United States)

    Kessler, Sudha Kilaru; Shinnar, Shlomo; Cnaan, Avital; Dlugos, Dennis; Conry, Joan; Hirtz, Deborah G; Hu, Fengming; Liu, Chunyan; Mizrahi, Eli M; Moshé, Solomon L; Clark, Peggy; Glauser, Tracy A

    2017-08-15

    To determine seizure semiology in children with newly diagnosed childhood absence epilepsy and to evaluate associations with short-term treatment outcomes. For participants enrolled in a multicenter, randomized, double-blind, comparative-effectiveness trial, semiologic features of pretreatment seizures were analyzed as predictors of treatment outcome at the week 16 to 20 visit. Video of 1,932 electrographic absence seizures from 416 participants was evaluated. Median seizure duration was 10.2 seconds; median time between electrographic seizure onset and clinical manifestation onset was 1.5 seconds. For individual seizures and by participant, the most common semiology features were pause/stare (seizure 95.5%, participant 99.3%), motor automatisms (60.6%, 86.1%), and eye involvement (54.9%, 76.5%). The interrater agreement for motor automatisms and eye involvement was good (72%-84%). Variability of semiology features between seizures even within participants was high. Clustering analyses revealed 4 patterns (involving the presence/absence of eye involvement and motor automatisms superimposed on the nearly ubiquitous pause/stare). Most participants experienced more than one seizure cluster pattern. No individual semiologic feature was individually predictive of short-term outcome. Seizure freedom was half as likely in participants with one or more seizure having the pattern of eye involvement without motor automatisms than in participants without this pattern. Almost all absence seizures are characterized by a pause in activity or staring, but rarely is this the only feature. Semiologic features tend to cluster, resulting in identifiable absence seizure subtypes with significant intraparticipant seizure phenomenologic heterogeneity. One seizure subtype, pause/stare and eye involvement but no motor automatisms, is specifically associated with a worse treatment outcome. © 2017 American Academy of Neurology.

  8. Water Pollution Detection Based on Hypothesis Testing in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xu Luo

    2017-01-01

    Full Text Available Water pollution detection is of great importance in water conservation. In this paper, the water pollution detection problems of the network and of the node in sensor networks are discussed. The detection problems in both cases of the distribution of the monitoring noise being normal and nonnormal are considered. The pollution detection problems are analyzed based on hypothesis testing theory firstly; then, the specific detection algorithms are given. Finally, two implementation examples are given to illustrate how the proposed detection methods are used in the water pollution detection in sensor networks and prove the effectiveness of the proposed detection methods.

  9. Nursing and Focal Dyscognitive Seizures: A Clinical Update When Managing Risk Using Advanced Nursing Skills.

    Science.gov (United States)

    Holland, Christine; Edward, Karen-Leigh; Giandinoto, Jo-Ann

    2017-06-01

    Focal seizures are divided into simple and dyscognitive, with the latter resulting in the alteration of consciousness. In the ictal and postictal stages, patients may present with confusion, delirium, and psychosis, presenting a risk of safety to themselves and others. This article presents 3 case studies where patients have been admitted for visual and electroencephalographic monitoring. Seizure activity is provoked for the diagnosis and development of a management plan. These cases illustrate the unique nursing implications when caring for patients experiencing focal dyscognitive seizures, highlighting the unique circumstances for the neuroscience nurse regarding risk management, safe administration of radioactive isotopes, detection of subtle seizure manifestation, and use of family as experts in patient-centered care. Through a deliberate onset of seizures, neuroscience nurses are placed in nontypical nursing situations, thus managing risk in unpredictable conditions and displaying advanced and distinctive nursing skills.

  10. Nonstationary weak signal detection based on normalization ...

    Indian Academy of Sciences (India)

    ... than the traditional stochastic resonance. The method develops the area of time-varying signal detection with stochastic resonance and presents new strategy for detection and denoising of a time-varying signal. It can be expected to be widely used in the areas of aperiodic signal processing, radar communication,etc ...

  11. Laser-Based Detection Methods for Explosives

    Science.gov (United States)

    2007-09-01

    Photofragmentation-Fragment Detection (SPF-FD) Cabalo and Sausa introduced a technique for detection of explosives with low vapor pressure called SPF-FD (149...1999, 38, 6447. 149. Cabalo , J.; Sausa, R. Appl. Spectrosc. 2003, 57, 1196. 150. Claspy, P. C.; Pao, Y.-H.; Kwong, S.; Nodov, E. IEEE J. Quant

  12. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

    Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  13. Early Seizure Frequency and Aetiology Predict Long-Term Medical Outcome in Childhood-Onset Epilepsy

    Science.gov (United States)

    Sillanpaa, Matti; Schmidt, Dieter

    2009-01-01

    In clinical practice, it is important to predict as soon as possible after diagnosis and starting treatment, which children are destined to develop medically intractable seizures and be at risk of increased mortality. In this study, we determined factors predictive of long-term seizure and mortality outcome in a population-based cohort of 102…

  14. 2015 ACVIM Small Animal Consensus Statement on seizure management in dogs

    DEFF Research Database (Denmark)

    Podell, M.; Volk, H. A.; Berendt, Mette

    2016-01-01

    This report represents a scientific and working clinical consensus statement on seizure management in dogs based on current literature and clinical expertise. The goal was to establish guidelines for a predetermined, concise, and logical sequential approach to chronic seizure management starting ...

  15. Nonlinear analysis of EEG for epileptic seizures

    Energy Technology Data Exchange (ETDEWEB)

    Hively, L.M.; Clapp, N.E.; Daw, C.S.; Lawkins, W.F. [Oak Ridge National Lab., TN (United States); Eisenstadt, M.L. [Knoxville Neurology Clinic, St. Mary`s Medical Center, Knoxville, TN (United States)

    1995-04-01

    We apply chaotic time series analysis (CTSA) to human electroencephalogram (EEG) data. Three epoches were examined: epileptic seizure, non-seizure, and transition from non-seizure to seizure. The CTSA tools were applied to four forms of these data: raw EEG data (e-data), artifact data (f-data) via application of a quadratic zero-phase filter of the raw data, artifact-filtered data (g- data) and that was the residual after subtracting f-data from e-data, and a low-pass-filtered version (h-data) of g-data. Two different seizures were analyzed for the same patient. Several nonlinear measures uniquely indicate an epileptic seizure in both cases, including an abrupt decrease in the time per wave cycle in f-data, an abrupt increase in the Kolmogorov entropy and in the correlation dimension for e-h data, and an abrupt increase in the correlation dimension for e-h data. The transition from normal to seizure state also is characterized by distinctly different trends in the nonlinear measures for each seizure and may be potential seizure predictors for this patient. Surrogate analysis of e-data shows that statistically significant nonlinear structure is present during the non-seizure, transition , and seizure epoches.

  16. INHIBITORY MOTOR SEIZURES: SEMIOLOGY AND THERAPY

    Directory of Open Access Journals (Sweden)

    K. Yu. Мukhin

    2013-01-01

    Full Text Available The article is devoted to rare and unique type of epileptic seizures – inhibitory motor seizures, characterized by the inability to execute a voluntary movement with preserved consciousness. The exact prevalence of this type of seizures is not known, but many cases are unrecognized or non-correctly diagnosed as Todd's paralysis. Therefore practical doctors should know the clinical and electroencephalographic characteristics of this type of seizures andtake them into account in the differential diagnoses . The authors presented a detailed review of the literature, including the historical data, etiology, pathogenesis and proposed mechanisms of formation of inhibitory motor seizures, clinical and EEG characteristics, therapeutic approaches. Antiepileptic drugs of choice used in the treatment of inhibitory motor seizures are valproic acid (preferably depakine chronosphere – original prolonged form of valproate. The authors also presented their observations of patients with inhibitory motor seizures.

  17. Treatment of refractory neonatal seizures with topiramate.

    Science.gov (United States)

    Riesgo, Rudimar; Winckler, Maria Isabel; Ohlweiler, Lygia; Ranzan, Josiane; Becker, Michele; Salvador, Socrates; Magalhaes, Luiza; Ribeiro, Ricardo

    2012-12-01

    The objective of this study is to describe the usefulness of topiramate in refractory neonatal seizures. We reported the clinical off-label use of topiramate in three cases of refractory neonatal seizures of unclear origin with no response to conventional antiepileptic drugs. In all cases, the seizures were completely controlled with adding topiramate. All patients became seizure free during hospitalization and were followed by approximately 1 year after hospital discharge, with monotherapy with topiramate. The clinical off-label use of topiramate in neonatal seizures is still incipient. When searching publications in this matter, only one report was identified. Because of its efficacy for both seizures and neuroprotection, topiramate could be a useful choice in refractory neonatal seizures. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

  18. Electroencephalography after a single unprovoked seizure.

    Science.gov (United States)

    Debicki, Derek B

    2017-07-01

    Electroencephalography (EEG) is an essential diagnostic tool in the evaluation of seizure disorders. In particular, EEG is used as an additional investigation for a single unprovoked seizure. Epileptiform abnormalities are related to seizure disorders and have been shown to predict recurrent unprovoked seizures (i.e., a clinical definition of epilepsy). Thus, the identification of epileptiform abnormalities after a single unprovoked seizure can inform treatment options. The current review addresses the relationship between EEG abnormalities and seizure recurrence. This review also addresses factors that are found to improve the yield of recording epileptiform abnormalities including timing of EEG relative to the new-onset seizure, use of repeat studies, use of sleep deprivation and prolonged recordings. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  19. Traditional and non-traditional treatments for autism spectrum disorder with seizures: an on-line survey

    Directory of Open Access Journals (Sweden)

    Sreenivasula Swapna

    2011-05-01

    factors. For ASD individuals with reported subclinical seizures, other clinical factors were reported to be worsened by AEDs and improved by non-AED traditional seizure and non-traditional treatments. The rate of side effects was reportedly higher for AEDs compared to traditional non-AED treatments. Conclusion Although this survey-based method only provides information regarding parental perceptions of effectiveness, this information may be helpful for selecting seizure treatments in individuals with ASD.

  20. System and Experiences in the Area of Radioactive Material Seizure Assurance

    International Nuclear Information System (INIS)

    Svoboda, K.; Podlaha, K.; Sir, D.

    2005-01-01

    In recent years, a number of radioactive seizures have been increased (i.e. the materials that contain one or more radionuclides and their activities from the point of view of radiation protection are not negligible). This is mainly due to newly installed technical equipment that monitors metal scrap resp. communal waste during its entry to metallurgical plants and iron works resp. incinerators or waste dumps. In the Nuclear research Institute Rez plc. (NRI Rez) was established a working group which provides, among other activities, full system of radioactive material seizure assurance. Part of this service contents also transport, storage, treatment, conditioning and disposal of the seizured radioactive source. This service was firstly established for communal waste dump, but other organizations can take advantage of this service not only for the seizures in communal waste dumps. The system of radioactive material seizure assurance is consisted of the following parts: (1) seizure on stationary detection system; (2) 24 hours emergency service of the working group; (3) event classification, detailed counting a tracking of radioactive source; (4) found radioactive source transport to NRI Rez for storage; (5) radioactive source characterization; (6) seizure evaluation and protocol providing; (7) State Office for Nuclear Safety (SONS) decree about next procedure. Stationary detection system ( detection gate ) is usually installed at the entry to dumps area, metallurgical plants, iron works etc. The detection gate traces changes of vehicle dose rate comparing to the average background by vehicle measurement. If the vehicle dose rate is significantly higher then the average background (usual alarm level is 10-30% above background), the vehicle is postponed by the gate operator and put aside on the determined place. Seizure is announced to the police of the Czech republic and to the SONS. Typical examples of the seizured radionuclide sources are: military, devices coated

  1. Reappraisal of epileptic pain as a rare symptom of seizures.

    Science.gov (United States)

    Kuloğlu Pazarcı, Nevin; Bebek, Nerses; Baykan, Betül; Gürses, Candan; Gökyiğit, Ayşen

    2016-02-01

    To draw attention to epileptic pain which is a rare seizure symptom mostly causing wrong diagnosis and delayed treatment. We present nine patients in whom pain was a prominent initial or early ictal symptom. We reviewed the records of 4736 patients, and found nine patients who had pain as an aura or an early prominent symptom of their seizures. Seizure semiology, EEG, and cranial imaging features were evaluated retrospectively. Age at seizure onset ranged from 6 months to 50 years, and the mean age during the study was 37.7±11.9 years. Pain was predominantly peripherally localized in four patients, whereas cephalic pain was detected in three patients, and abdominal pain was detected in two patients. Electroencephalography (EEG) revealed epileptic abnormalities on the temporoparietal and frontotemporal regions in three patients each. Photosensitive generalized epileptic discharges were detected in one and diffuse background slowing in the remaining two other patients. Electroencephalography abnormalities were lateralized to the contralateral site of the pain in four patients with strictly localized pain. Three patients revealed no abnormality on the cranial MR imaging, whereas others showed different types of abnormality such as heterotopias (n:1), mesial temporal lobe atrophy (n:1), white and gray matter sequela lesions (n:1), diffuse white matter lesion (n:1), chronic encephalomalacia and gliosis (n:1), and perivascular space dilatation (n:1). Epileptic pain is a neglected, but important, semiologic symptom with localization and lateralization value in the patients with somatosensorial seizures of parietal as well as temporal lobe origin. It occurs mainly as peripherally localized, cephalic, or abdominal pain and is usually associated with a symptomatic etiology. Awareness of epileptic pain is important to avoid misdiagnosis and delayed treatment. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Anomica: Fast Support Vector Based Novelty Detection

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper we propose ν-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the...

  3. Unexpected marked seizure improvement in paediatric epilepsy surgery candidates

    DEFF Research Database (Denmark)

    Hoei-Hansen, Christina E; Mathiasen, René; Uldall, Peter

    2017-01-01

    of children withdrawn from an epilepsy surgery programme due to unexpected seizure improvement. METHODS: We retrospectively studied 173 children under 18 years with medical refractory epilepsy referred for epilepsy surgery between 1996 and 2010. Medical records were reviewed in 2012 and 2015. RESULTS...... for these patients was heterotopia (n=1), focal cortical dysplasia (n=3), infarction (n=1) and unknown, with normal MRI (n=1). They all had an IQ in the normal range. Two of the remaining 7 children were operated later. CONCLUSION: Unexpected seizure control may occur during epilepsy surgery evaluation.......PURPOSE: Epilepsy surgery is performed based on the assumption that medical refractory epilepsy will continue. Rarely seizure freedom occurs before surgery is performed, while the patient is being evaluated as an epilepsy surgery candidate. The aim of this study was to describe the number...

  4. Genetic-algorithm-based multiple regression with fuzzy inference system for detection of nocturnal hypoglycemic episodes.

    Science.gov (United States)

    Ling, Steve S H; Nguyen, Hung T

    2011-03-01

    Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity.

  5. Seizure characteristics of epilepsy in childhood after acute encephalopathy with biphasic seizures and late reduced diffusion.

    Science.gov (United States)

    Ito, Yuji; Natsume, Jun; Kidokoro, Hiroyuki; Ishihara, Naoko; Azuma, Yoshiteru; Tsuji, Takeshi; Okumura, Akihisa; Kubota, Tetsuo; Ando, Naoki; Saitoh, Shinji; Miura, Kiyokuni; Negoro, Tamiko; Watanabe, Kazuyoshi; Kojima, Seiji

    2015-08-01

    The aim of this study was to clarify characteristics of post-encephalopathic epilepsy (PEE) in children after acute encephalopathy with biphasic seizures and late reduced diffusion (AESD), paying particular attention to precise diagnosis of seizure types. Among 262 children with acute encephalopathy/encephalitis registered in a database of the Tokai Pediatric Neurology Society between 2005 and 2012, 44 were diagnosed with AESD according to the clinical course and magnetic resonance imaging (MRI) findings and were included in this study. Medical records were reviewed to investigate clinical data, MRI findings, neurologic outcomes, and presence or absence of PEE. Seizure types of PEE were determined by both clinical observation by pediatric neurologists and ictal video-electroencephalography (EEG) recordings. Of the 44 patients after AESD, 10 (23%) had PEE. The period between the onset of encephalopathy and PEE ranged from 2 to 39 months (median 8.5 months). Cognitive impairment was more severe in patients with PEE than in those without. Biphasic seizures and status epilepticus during the acute phase of encephalopathy did not influence the risk of PEE. The most common seizure type of PEE on clinical observation was focal seizures (n = 5), followed by epileptic spasms (n = 4), myoclonic seizures (n = 3), and tonic seizures (n = 2). In six patients with PEE, seizures were induced by sudden unexpected sounds. Seizure types confirmed by ictal video-EEG recordings were epileptic spasms and focal seizures with frontal onset, and all focal seizures were startle seizures induced by sudden acoustic stimulation. Intractable daily seizures remain in six patients with PEE. We demonstrate seizure characteristics of PEE in children after AESD. Epileptic spasms and startle focal seizures are common seizure types. The specific seizure types may be determined by the pattern of diffuse subcortical white matter injury in AESD and age-dependent reorganization of the brain

  6. An Entropy-Based Network Anomaly Detection Method

    Directory of Open Access Journals (Sweden)

    Przemysław Bereziński

    2015-04-01

    Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.

  7. Vision-based vehicle detection and tracking algorithm design

    Science.gov (United States)

    Hwang, Junyeon; Huh, Kunsoo; Lee, Donghwi

    2009-12-01

    The vision-based vehicle detection in front of an ego-vehicle is regarded as promising for driver assistance as well as for autonomous vehicle guidance. The feasibility of vehicle detection in a passenger car requires accurate and robust sensing performance. A multivehicle detection system based on stereo vision has been developed for better accuracy and robustness. This system utilizes morphological filter, feature detector, template matching, and epipolar constraint techniques in order to detect the corresponding pairs of vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles such as the leading vehicle and side-lane vehicles. The position parameters of the vehicles located in front are obtained based on the detection information. The proposed vehicle detection system is implemented on a passenger car, and its performance is verified experimentally.

  8. Fyodor Dostoevsky and his falling sickness: a critical analysis of seizure semiology.

    Science.gov (United States)

    Seneviratne, Udaya

    2010-08-01

    Fyodor Dostoevsky is a great Russian writer who had epilepsy. As a consequence, there are many references to seizure-related phenomena in his work. His epilepsy syndrome has been a focus of debate. The goal of this article is to delineate his epilepsy syndrome based on a semiological description of seizures, which could be considered one of the most reliable pieces of circumstantial evidence available. It was hypothesized that seizure-related descriptions in his books were based on his own personal experience. The semiology of seizures and related phenomena was compiled from Dostoevsky's own work, his letters to family and friends, and reminiscences of his wife and friend. Those descriptions were analyzed in detail to elicit localizing and lateralizing features of seizures. On the basis of this evidence, it was postulated that Dostoevsky had a partial epilepsy syndrome most probably arising from the dominant temporal lobe. Crown Copyright 2010. Published by Elsevier Inc. All rights reserved.

  9. A hypothesis regarding the molecular mechanism underlying dietary soy-induced effects on seizure propensity.

    Directory of Open Access Journals (Sweden)

    Cara Jean Westmark

    2014-09-01

    Full Text Available Numerous neurological disorders including fragile X syndrome, Down syndrome, autism and Alzheimer’s disease are comorbid with epilepsy. We have observed elevated seizure propensity in mouse models of these disorders dependent on diet. Specifically, soy-based diets exacerbate audiogenic-induced seizures in juvenile mice. We have also found potential associations between the consumption of soy-based infant formula and seizure incidence, epilepsy comorbidity and autism diagnostic scores in autistic children by retrospective analyses of medical record data. In total, these data suggest that consumption of high levels of soy protein during postnatal development may affect neuronal excitability. Herein, we present our theory regarding the molecular mechanism underlying soy-induced effects on seizure propensity. We hypothesize that soy phytoestrogens interfere with metabotropic glutamate receptor signaling through an estrogen receptor-dependent mechanism, which results in elevated production of key synaptic proteins and decreased seizure threshold.

  10. Nanopore-Based Target Sequence Detection.

    Directory of Open Access Journals (Sweden)

    Trevor J Morin

    Full Text Available The promise of portable diagnostic devices relies on three basic requirements: comparable sensitivity to established platforms, inexpensive manufacturing and cost of operations, and the ability to survive rugged field conditions. Solid state nanopores can meet all these requirements, but to achieve high manufacturing yields at low costs, assays must be tolerant to fabrication imperfections and to nanopore enlargement during operation. This paper presents a model for molecular engineering techniques that meets these goals with the aim of detecting target sequences within DNA. In contrast to methods that require precise geometries, we demonstrate detection using a range of pore geometries. As a result, our assay model tolerates any pore-forming method and in-situ pore enlargement. Using peptide nucleic acid (PNA probes modified for conjugation with synthetic bulk-adding molecules, pores ranging 15-50 nm in diameter are shown to detect individual PNA-bound DNA. Detection of the CFTRΔF508 gene mutation, a codon deletion responsible for ∼66% of all cystic fibrosis chromosomes, is demonstrated with a 26-36 nm pore size range by using a size-enhanced PNA probe. A mathematical framework for assessing the statistical significance of detection is also presented.

  11. Vaccination, seizures and 'vaccine damage'.

    Science.gov (United States)

    Brown, Natasha J; Berkovic, Samuel F; Scheffer, Ingrid E

    2007-04-01

    Concerns about the safety of vaccination have plagued the community, with reduction in vaccine uptake resulting in increased risk of epidemics. Vaccination has been implicated in the cause of febrile seizures, 'vaccine encephalopathy' and autistic spectrum disorders. Evaluation of alleged associations is complicated by evolution in the vaccination field. This review focuses on the risk of seizures following vaccination and the alleged associations of vaccination with vaccine encephalopathy and also with autism spectrum disorders. Over the last decade the introduction of new vaccines such as the acellular pertussis vaccine has produced a reduction in seizures following vaccination, the outcome of which was benign even with older vaccines. New evidence emerged in 2006 showing that cases of alleged 'vaccine encephalopathy' are due to mutations within a sodium channel gene. The weight of epidemiological evidence does not support a relationship between vaccination and childhood epileptic encephalopathies or autism spectrum disorders. Vaccines are safer than ever before, but the challenge remains to convey this message to society in such a way that produces change in attitudes to vaccination and subsequent increase in vaccine coverage.

  12. Smartphone applications for seizure management.

    Science.gov (United States)

    Pandher, Puneet Singh; Bhullar, Karamdeep Kaur

    2016-06-01

    Technological advancements continue to provide innovative ways of enhancing patient care in medicine. In particular, the growing popularity of smartphone technology has seen the recent emergence of a myriad of healthcare applications (or apps) that promise to help shape the way in which health information is delivered to people worldwide. While limited research already exists on a range of such apps, our study is the first to examine the salient features of smartphone applications as they apply to the area of seizure management. For the purposes of this review, we conducted a search of the official online application stores of the five major smartphone platforms: iPhone, Android, Blackberry, Windows Mobile and Nokia-Symbian. Apps were included if they reported to contain some information or tools relating to seizure management and excluded if they were aimed exclusively at health professionals. A total of 28 applications met these criteria. Overall, we found an increasing number of epilepsy apps available on the smartphone market, but with only a minority offering comprehensive educational information alongside tools such as seizure diaries, medication tracking and/or video recording. © The Author(s) 2014.

  13. Tracking generalized tonic-clonic seizures with a wrist accelerometer linked to an online database.

    Science.gov (United States)

    Velez, Mariel; Fisher, Robert S; Bartlett, Victoria; Le, Scheherazade

    2016-07-01

    Clinical management of epilepsy and current epilepsy therapy trials rely on paper or electronic diaries often with inaccurate self-reported seizure frequency as the primary outcome. This is the first study addressing the feasibility of detecting and recording generalized tonic-clonic seizures (GTCS) through a biosensor linked to an online seizure database. A prospective trial was conducted with video-EEG (vEEG) in an epilepsy monitoring unit. Patients wore a wristwatch accelerometer that detected shaking and transmitted events via Bluetooth® to a bedside electronic tablet and then via Wi-Fi to an online portal. The watch recorded the date, time, audio, duration, frequency and amplitude of events. Events logged by the watch and recorded in a bedside paper diary were measured against vEEG, the "gold standard." Thirty patients were enrolled and 62 seizures were recorded on vEEG: 31 convulsive and 31 non-convulsive. Twelve patients had a total of 31 convulsive seizures, and of those, 10 patients had 13 GTCS. The watch captured 12/13 (92.3%) GTCS. Watch audio recordings were consistent with seizures in 11/12 (91.6%). Data were successfully transferred to the bedside tablet in 11/12 (91.6%), and to the online database in 10/12 (83.3%) GTCS. The watch recorded 81 false positives, of which 42/81 (51%) were cancelled by the patients. Patients and caregivers verbally reported 15/62 seizures (24.2% sensitivity) but no seizures were recorded on paper logs. Automatic detection and recording of GTCS to an online database is feasible and may be more informative than seizure logging in a paper diary. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  14. Adaptive skin detection based on online training

    Science.gov (United States)

    Zhang, Ming; Tang, Liang; Zhou, Jie; Rong, Gang

    2007-11-01

    Skin is a widely used cue for porn image classification. Most conventional methods are off-line training schemes. They usually use a fixed boundary to segment skin regions in the images and are effective only in restricted conditions: e.g. good lightness and unique human race. This paper presents an adaptive online training scheme for skin detection which can handle these tough cases. In our approach, skin detection is considered as a classification problem on Gaussian mixture model. For each image, human face is detected and the face color is used to establish a primary estimation of skin color distribution. Then an adaptive online training algorithm is used to find the real boundary between skin color and background color in current image. Experimental results on 450 images showed that the proposed method is more robust in general situations than the conventional ones.

  15. Acute EEG findings in HIV-infected Zambian adults with new-onset seizure

    Science.gov (United States)

    Elafros, Melissa A.; Sikazwe, Izukanji; Birbeck, Gretchen L.; Kalungwana, Lisa; Potchen, Michael J.; Bositis, Christopher M.; Koralnik, Igor J.; Theodore, William H.

    2015-01-01

    Objective: To describe acute EEG findings in HIV-infected adults with new-onset seizure, assess baseline clinical characteristics associated with EEG abnormalities, and evaluate the relationship between EEG abnormalities and recurrent seizure. Methods: Eighty-one HIV-infected adults with new-onset seizure had EEG recordings during their index admission. Baseline characteristics assessed included HIV stage, seizure semiology, serum and CSF studies, neuroimaging, cognitive function based on the Zambian Mini-Mental State Examination and International HIV Dementia Scale, and psychiatric symptoms using the Shona Symptom Questionnaire. We evaluated the relationship between baseline characteristics and EEG abnormalities. Patients were followed for seizure recurrence, and the association between acute EEG abnormalities and seizure recurrence was assessed. Death was a secondary outcome. Results: Fifty-five patients had abnormal EEGs (68%): 18 (22%) had interictal spikes (12) or a recorded seizure (6). Among baseline clinical characteristics, more advanced HIV disease (p = 0.039) and any imaging abnormality (p = 0.027) were associated with abnormal EEGs. Cortical (p = 0.008) and white matter (p = 0.004) abnormalities were associated with slow posterior dominant rhythm. Patients were followed for a median of 303 days (interquartile range 103–560). Twenty-four (30%) died and 23 (28%) had recurrent seizures. EEG abnormalities were not associated with recurrent seizure. There was a nonsignificant association between seizures recorded during EEG and death (67% vs 26%, p = 0.051). Conclusions: EEG abnormalities are common in this population, particularly in patients with imaging abnormalities and advanced HIV. Acute EEG abnormalities were not associated with recurrent seizure, but high mortality rates during follow-up limited this analysis. PMID:25740861

  16. Mapping cortical haemodynamics during neonatal seizures using diffuse optical tomography: A case study

    Directory of Open Access Journals (Sweden)

    Harsimrat Singh

    2014-01-01

    Full Text Available Seizures in the newborn brain represent a major challenge to neonatal medicine. Neonatal seizures are poorly classified, under-diagnosed, difficult to treat and are associated with poor neurodevelopmental outcome. Video-EEG is the current gold-standard approach for seizure detection and monitoring. Interpreting neonatal EEG requires expertise and the impact of seizures on the developing brain remains poorly understood. In this case study we present the first ever images of the haemodynamic impact of seizures on the human infant brain, obtained using simultaneous diffuse optical tomography (DOT and video-EEG with whole-scalp coverage. Seven discrete periods of ictal electrographic activity were observed during a 60 minute recording of an infant with hypoxic–ischaemic encephalopathy. The resulting DOT images show a remarkably consistent, high-amplitude, biphasic pattern of changes in cortical blood volume and oxygenation in response to each electrographic event. While there is spatial variation across the cortex, the dominant haemodynamic response to seizure activity consists of an initial increase in cortical blood volume prior to a large and extended decrease typically lasting several minutes. This case study demonstrates the wealth of physiologically and clinically relevant information that DOT–EEG techniques can yield. The consistency and scale of the haemodynamic responses observed here also suggest that DOT–EEG has the potential to provide improved detection of neonatal seizures.

  17. Laser spot detection based on reaction diffusion

    Czech Academy of Sciences Publication Activity Database

    Vázquez-Otero, Alejandro; Khikhlukha, Danila; Solano-Altamirano, J. M.; Dormido, R.; Duro, N.

    2016-01-01

    Roč. 16, č. 3 (2016), s. 1-11, č. článku 315. ISSN 1424-8220 R&D Projects: GA MŠk EF15_008/0000162 Grant - others:ELI Beamlines(XE) CZ.02.1.01/0.0/0.0/15_008/0000162 Institutional support: RVO:68378271 Keywords : laser spot detection * laser beam detection * reaction diffusion models * Fitzhugh-Nagumo model * reaction diffusion computation * Turing patterns Subject RIV: BL - Plasma and Gas Discharge Physics OBOR OECD: Fluids and plasma physics (including surface physics) Impact factor: 2.677, year: 2016

  18. The design method and research status of vehicle detection system based on geomagnetic detection principle

    Science.gov (United States)

    Lin, Y. H.; Bai, R.; Qian, Z. H.

    2018-03-01

    Vehicle detection systems are applied to obtain real-time information of vehicles, realize traffic control and reduce traffic pressure. This paper reviews geomagnetic sensors as well as the research status of the vehicle detection system. Presented in the paper are also our work on the vehicle detection system, including detection algorithms and experimental results. It is found that the GMR based vehicle detection system has a detection accuracy up to 98% with a high potential for application in the road traffic control area.

  19. Contour Detection Operators Based on Surround Inhibition

    NARCIS (Netherlands)

    Grigorescu, Cosmin; Petkov, Nicolai; Westenberg, Michel A.

    2003-01-01

    We propose a biologically motivated computational step, called non-classical receptive field (non-CRF) inhibition, to improve contour detection in images of natural scenes. We augment a Gabor energy operator with non-CRF inhibition. The resulting contour operator responds strongly to isolated lines,

  20. Nonstationary weak signal detection based on normalization ...

    Indian Academy of Sciences (India)

    Haibin Zhang

    Time-varying signal; weak signal detection; varying parameters; stochastic resonance. 1. Introduction. In general view, noise ..... the numerical solution for the typical first-order differential equation as Eq. (2). The discrete fourth-rank Runge–Kutta method [27] as follows is applied to solve the equation numerically. x. 0 ¼ dx dt.

  1. Febrile Seizures and Epilepsy: Association With Autism and Other Neurodevelopmental Disorders in the Child and Adolescent Twin Study in Sweden.

    Science.gov (United States)

    Gillberg, Christopher; Lundström, Sebastian; Fernell, Elisabeth; Nilsson, Gill; Neville, Brian

    2017-09-01

    There is a recently well-documented association between childhood epilepsy and earlysymptomaticsyndromeselicitingneurodevelopmentalclinicalexaminations (ESSENCE) including autism spectrum disorder, but the relationship between febrile seizures and ESSENCE is less clear. The Child and Adolescent Twin Study in Sweden (CATSS) is an ongoing population-based study targeting twins born in Sweden since July 1, 1992. Parents of 27,092 twins were interviewed using a validated DSM-IV-based interview for ESSENCE, in connection with the twins' ninth or twelfth birthday. Diagnoses of febrile seizures (n = 492) and epilepsy (n = 282) were based on data from the Swedish National Patient Register. Prevalence of ESSENCE in individuals with febrile seizures and epilepsy was compared with prevalence in the twin population without seizures. The association between febrile seizures and ESSENCE was considered before and after adjustment for epilepsy. Age of diagnosis of febrile seizures and epilepsy was considered as a possible correlate of ESSENCE in febrile seizures and epilepsy. The rate of ESSENCE in febrile seizures and epilepsy was significantly higher than in the total population without seizures (all P < 0.001). After adjusting for epilepsy, a significant association between febrile seizures and autism spectrum disorder, developmental coordination disorder, and intellectual disability remained. Earlier age of onset was associated with all ESSENCE except attention-deficit/hyperactivity disorder in epilepsy but not with ESSENCE in febrile seizures. In a nationally representative sample of twins, there was an increased rate of ESSENCE in childhood epilepsy and in febrile seizures. Febrile seizures alone could occur as a marker for a broader ESSENCE phenotype. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Withdrawal of valproic acid treatment during pregnancy and seizure outcome

    DEFF Research Database (Denmark)

    Tomson, Torbjörn; Battino, Dina; Bonizzoni, Erminio

    2016-01-01

    Based on data from the EURAP observational International registry of antiepileptic drugs (AEDs) and pregnancy, we assessed changes in seizure control and subsequent AED changes in women who underwent attempts to withdraw valproic acid (VPA) during the first trimester of pregnancy. Applying Bayesi...

  3. Feasibility of Seizure Prediction from intracranial EEG Recordings

    DEFF Research Database (Denmark)

    Henriksen, Jonas; Kjær, Troels; Thomsen, Carsten E.

    2009-01-01

    Purpose: The current project evaluated the feasibility of providing an algorithm that could warn a patient of a forthcoming seizure based on iEEG recordings. Method: The mean phase coherence (MPC) feature (Mormann F et al. Phys Nonlinear Phenom 2000;3-4:358-369.) was implemented and tested in a r...

  4. Memory detection 2.0: the first web-based memory detection test.

    Science.gov (United States)

    Kleinberg, Bennett; Verschuere, Bruno

    2015-01-01

    There is accumulating evidence that reaction times (RTs) can be used to detect recognition of critical (e.g., crime) information. A limitation of this research base is its reliance upon small samples (average n = 24), and indications of publication bias. To advance RT-based memory detection, we report upon the development of the first web-based memory detection test. Participants in this research (Study1: n = 255; Study2: n = 262) tried to hide 2 high salient (birthday, country of origin) and 2 low salient (favourite colour, favourite animal) autobiographical details. RTs allowed to detect concealed autobiographical information, and this, as predicted, more successfully so than error rates, and for high salient than for low salient items. While much remains to be learned, memory detection 2.0 seems to offer an interesting new platform to efficiently and validly conduct RT-based memory detection research.

  5. Memory detection 2.0: the first web-based memory detection test.

    Directory of Open Access Journals (Sweden)

    Bennett Kleinberg

    Full Text Available There is accumulating evidence that reaction times (RTs can be used to detect recognition of critical (e.g., crime information. A limitation of this research base is its reliance upon small samples (average n = 24, and indications of publication bias. To advance RT-based memory detection, we report upon the development of the first web-based memory detection test. Participants in this research (Study1: n = 255; Study2: n = 262 tried to hide 2 high salient (birthday, country of origin and 2 low salient (favourite colour, favourite animal autobiographical details. RTs allowed to detect concealed autobiographical information, and this, as predicted, more successfully so than error rates, and for high salient than for low salient items. While much remains to be learned, memory detection 2.0 seems to offer an interesting new platform to efficiently and validly conduct RT-based memory detection research.

  6. Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rupinder Singh

    2017-01-01

    Full Text Available In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack. For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.

  7. Marked Seizure Reduction after MCT Supplementation

    Directory of Open Access Journals (Sweden)

    Raed Azzam

    2013-01-01

    Full Text Available We report the case of a 43-year-old man with history of nonsurgical partial epilepsy who previously failed multiple trials of antiepileptic drugs. Medium-chain triglycerides (MCT were added to his regular diet in the form of pure oil. Subsequently, his seizure frequency was markedly reduced from multiple daily seizures to one seizure every four days. His seizures recurred after transient discontinuation of MCT over a period of ten days. His seizure improvement was achieved at a dose of four tablespoons of MCT twice daily with no reported side effects. He developed significant diarrhea and flatulence at higher doses. We conclude that MCT oil supplementation to regular diet may provide better seizure control in some patients. MCT oil supplementation may be a more tolerable alternative to the standard ketogenic diet.

  8. Role of neuroimaging in first seizure diagnosis.

    Science.gov (United States)

    Crocker, Candice E; Pohlmann-Eden, Bernhard; Schmidt, Matthias H

    2017-07-01

    The primary goal of neuroimaging in a first, unprovoked seizure is to identify a lesion that can explain the seizure. Secondarily, neuroimaging may be used to predict seizure recurrence and assist with the diagnosis of epilepsy. However, the events leading from a first seizure to epilepsy, with or without an identifiable epileptogenic lesion, are not well understood, and it is not always clear which lesions are epileptogenic as opposed to incidental. Much neuroimaging research to date has focused on findings in chronic epilepsy, rather than first seizure. Dedicated epilepsy imaging with high quality MRI protocols maximizes the likelihood of a diagnosis. However, a significant proportion of patients are MRI-negative, prompting researchers in the field to continue the search for better imaging strategies. Here we describe the role of neuroimaging in the assessment of a first seizure, the current state of the art and possible future directions. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  9. Anomaly-based Network Intrusion Detection Methods

    Directory of Open Access Journals (Sweden)

    Pavel Nevlud

    2013-01-01

    Full Text Available The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks.

  10. Cable-Based Water Leak Detection Technology

    OpenAIRE

    ECT Team, Purdue

    2007-01-01

    Water leaks can be considered as a serious problem from many sources such as water supply and return chains, air conditioning units, cold-water chillers, clogged drains, damaged skylights or windows, or even construction errors. The new water leak detection technologies can provide significant advantages in cost, reliability, and easy adoption have continued since the traditional technology mainly focusing on a spot detector revealed several limitations.

  11. Carbon-Based Electrodes for Parabens Detection

    OpenAIRE

    Aniela Pop; Ianina Birsan; Corina Orha; Rodica Pode; Florica Manea

    2016-01-01

    Carbon nanofiber-epoxy composite electrode has been investigated through voltammetric and amperometric techniques in order to detect parabens from aqueous solutions. The occurrence into environment as emerging pollutants of these preservative compounds has been extensively studied in the last decades, and consequently, a rapid and reliable method for their quantitative quantification is required. In this study, methylparaben (MP) and propylparaben (PP) were chosen as representatives for parab...

  12. Parkinson's disease detection based on dysphonia measurements

    Science.gov (United States)

    Lahmiri, Salim

    2017-04-01

    Assessing dysphonic symptoms is a noninvasive and effective approach to detect Parkinson's disease (PD) in patients. The main purpose of this study is to investigate the effect of different dysphonia measurements on PD detection by support vector machine (SVM). Seven categories of dysphonia measurements are considered. Experimental results from ten-fold cross-validation technique demonstrate that vocal fundamental frequency statistics yield the highest accuracy of 88 % ± 0.04. When all dysphonia measurements are employed, the SVM classifier achieves 94 % ± 0.03 accuracy. A refinement of the original patterns space by removing dysphonia measurements with similar variation across healthy and PD subjects allows achieving 97.03 % ± 0.03 accuracy. The latter performance is larger than what is reported in the literature on the same dataset with ten-fold cross-validation technique. Finally, it was found that measures of ratio of noise to tonal components in the voice are the most suitable dysphonic symptoms to detect PD subjects as they achieve 99.64 % ± 0.01 specificity. This finding is highly promising for understanding PD symptoms.

  13. Daytime Water Detection Based on Color Variation

    Science.gov (United States)

    Rankin, Arturo L.; Matthies, Larry H.

    2010-01-01

    Robust water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation. This is particularly true in wide open areas where water can collect in naturally occurring terrain depressions during periods of heavy precipitation and form large water bodies (such as ponds). At far range, reflections of the sky provide a strong cue for water. But at close range, the color coming out of a water body dominates sky reflections and the water cue from sky reflections is of marginal use. We model this behavior by using water body intensity data from multiple frames of RGB imagery to estimate the total reflection coefficient contribution from surface reflections and the combination of all other factors. Then we describe an algorithm that uses one of the color cameras in a forward- looking, UGV-mounted stereo-vision perception system to detect water bodies in wide open areas. This detector exploits the knowledge that the change in saturation-to-brightness ratio across a water body from the leading to trailing edge is uniform and distinct from other terrain types. In test sequences approaching a pond under clear, overcast, and cloudy sky conditions, the true positive and false negative water detection rates were (95.76%, 96.71%, 98.77%) and (0.45%, 0.60%, 0.62%), respectively. This software has been integrated on an experimental unmanned vehicle and field tested at Ft. Indiantown Gap, PA.

  14. Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

    Directory of Open Access Journals (Sweden)

    Andres M. Alvarez-Meza

    2017-10-01

    Full Text Available We introduce Enhanced Kernel-based Relevance Analysis (EKRA that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand.

  15. Memory detection 2.0: The first web-based memory detection test

    NARCIS (Netherlands)

    Kleinberg, B.; Verschuere, B.

    2015-01-01

    There is accumulating evidence that reaction times (RTs) can be used to detect recognition of critical (e.g., crime) information. A limitation of this research base is its reliance upon small samples (average n = 24), and indications of publication bias. To advance RT-based memory detection, we

  16. Epileptic Seizures from Abnormal Networks: Why Some Seizures Defy Predictability

    Science.gov (United States)

    2011-12-12

    P. Kudela), gbergey@jhmi.edu (G.K. Bergey ), pfranasz@gmail.com (P.J. Franaszczuk). 1 Tel.: +1 443 287 4561; fax: +1 443 287 6423. 2 Tel.: +1 443 287...cle can be found, in the online version, at doi:10.1016/j.eplepsyres.2011.11.006. References Afra, P., Jouny, C.C., Bergey , G.K., 2008. Duration of...complex partial seizures: an intracranial EEG study. Epilepsia 49 (4), 677—684. Anderson, W.S., Kudela, P., Cho, R.J., Bergey , G.K., Franaszczuk, P., 2007

  17. An Immunity-Based Anomaly Detection System with Sensor Agents

    Directory of Open Access Journals (Sweden)

    Yoshiteru Ishida

    2009-11-01

    Full Text Available This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user’s command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems.

  18. An immunity-based anomaly detection system with sensor agents.

    Science.gov (United States)

    Okamoto, Takeshi; Ishida, Yoshiteru

    2009-01-01

    This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user's command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems.

  19. Automatic hearing loss detection system based on auditory brainstem response

    International Nuclear Information System (INIS)

    Aldonate, J; Mercuri, C; Reta, J; Biurrun, J; Bonell, C; Gentiletti, G; Escobar, S; Acevedo, R

    2007-01-01

    Hearing loss is one of the pathologies with the highest prevalence in newborns. If it is not detected in time, it can affect the nervous system and cause problems in speech, language and cognitive development. The recommended methods for early detection are based on otoacoustic emissions (OAE) and/or auditory brainstem response (ABR). In this work, the design and implementation of an automated system based on ABR to detect hearing loss in newborns is presented. Preliminary evaluation in adults was satisfactory

  20. DNA based methods used for characterization and detection of food ...

    African Journals Online (AJOL)

    Detection of food borne pathogen is of outmost importance in the food industries and related agencies. For the last few decades conventional methods were used to detect food borne pathogens based on phenotypic characters. At the advent of complementary base pairing and amplification of DNA, the diagnosis of food ...

  1. Poseidon: A 2-tier Anomaly-based Intrusion Detection System

    NARCIS (Netherlands)

    Bolzoni, D.; Zambon, Emmanuele; Etalle, Sandro; Hartel, Pieter H.

    2005-01-01

    We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection

  2. Prenuptial seizures: a report of five cases.

    Science.gov (United States)

    McConnell, H; Valeriano, J; Brillman, J

    1995-01-01

    The cases of 5 patients with seizures occurring the day of or shortly before their weddings are presented. Major life events may precipitate or exacerbate epileptic or nonepileptic seizures as a result of 1) missed medications, 2) sleep deprivation, 3) alcohol or concomitant medications, 4) hyperventilation, or 5) the emotional state directly or stress indirectly. Seizures occurring at times of psychological stress may be either neurological or psychiatric in origin. The physician treating patients with a new onset or exacerbation of seizures around a major life event must consider all of these factors in the evaluation.

  3. Flumazenil and seizures: analysis of 43 cases.

    Science.gov (United States)

    Spivey, W H

    1992-01-01

    Flumazenil is a new drug indicated for the reversal of the sedative effects of benzodiazepines mediated at the benzodiazepine-receptor site. Worldwide sources to date have disclosed 43 cases of seizures related, at least temporally, to the intravenous administration of flumazenil. There was no apparent relationship between the dose of flumazenil and the development of seizures, which occurred at doses ranging from 0.2 to 10.0 mg. The seizures were not considered to be a toxic effect of flumazenil, but many of them probably were due to an unmasking of the anticonvulsant effect of the previously used benzodiazepine or to a severe benzodiazepine-withdrawal syndrome. Eighteen (42%) of the patients had ingested overdoses of cyclic antidepressants, which were considered responsible for the seizures. In addition to patients with concurrent cyclic antidepressant poisoning, high-risk populations include patients who have been treated with benzodiazepines for a seizure disorder or an acute convulsive episode, patients with concurrent major sedative-hypnotic drug withdrawal, patients who have recently been treated with repeated doses of parenteral benzodiazepines, and overdose patients with myoclonic jerking or seizure activity before flumazenil administration. To minimize the likelihood of a seizure, it is recommended that flumazenil not be administered to patients who have used benzodiazepines for the treatment of seizure disorders or to patients who have ingested drugs (eg, cyclic antidepressants, cocaine, lithium, methylxanthines, isoniazid, propoxyphene, monoamine oxidase inhibitors, buproprion HCl, and cyclosporine) that place them at risk for the development of seizures.

  4. [THE PROPAGATION AND SEMIOLOGY OF FOCAL EPILEPTIC SEIZURES. CASES CONNECTED TO THE INSULA. THEORETICAL CONSIDERATIONS].

    Science.gov (United States)

    Balogh, Attila; Balogh, Attila

    2016-01-30

    The developing of diagnostical examinations in epileptology provides new challenges in seizure semiology. On the analysis of seizures it is important to examine the mechanisms of their propagation. The brain connectivity (based on the neuroimaging), the shadowing of the movement of excessive neuronal activity (based on computerized EEG and MEG methods), the cognition of the physiological and pathological brain networks are the footstone of the epileptic seizure propagation. The investigators prove, by means of case demonstrations of the role of the network nodes and the role of the epileptic hubs in the seizure symptomatology. The preoperative, intra and postoperative data are analised of three insular and one parietal epileptic patients in point of view of their seizure symptomes. Complex neuroimaging, noninvasive and invasive electrophysiology, intensive long-term video-EEG monitoring, computerized EEG analysis, fuctional mapping, intraoperative corticography were used. The etiology were confirmed with hystology. It is observed that on seizure semiology our patients plays the insula a double role. In some cases, it is the focus of insular seizures with their symptoms difficult to identify. However, in the majority of cases and as a consequence of its rich neural connections, the insula has a peculiar property in the evolution of the symptomatogenic features of seizures. This observations are developing new relationships between the mechanism of seizure propagation and its semiological consequences. On epileptological point of view there are brain structures which has peculiar role in the "designe" of propagation of the epileptic excitement. The numerous new methods in neuroimaging and neurophysiology allowed the connectomical examination of the epileptic networks. The role of the epileptic diathesis is approachable with the metholdology of the brain connectivity. Theoretically the node of the epileptic network consist of the potential pathes where the localised

  5. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi

    2015-12-07

    Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.

  6. A Vehicle Detection Algorithm Based on Deep Belief Network

    Directory of Open Access Journals (Sweden)

    Hai Wang

    2014-01-01

    Full Text Available Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.

  7. Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states.

    Science.gov (United States)

    Hussain, Lal; Aziz, Wajid; Alowibdi, Jalal S; Habib, Nazneen; Rafique, Muhammad; Saeed, Sharjil; Kazmi, Syed Zaki Hassan

    2017-03-23

    Epilepsy is a neuronal disorder for which the electrical discharge in the brain is synchronized, abnormal and excessive. To detect the epileptic seizures and to analyse brain activities during different mental states, various methods in non-linear dynamics have been proposed. This study is an attempt to quantify the complexity of control and epileptic subject with and without seizure as well as to distinguish eye-open (EO) and eye-closed (EC) conditions using threshold-based symbolic entropy. The threshold-dependent symbolic entropy was applied to distinguish the healthy and epileptic subjects with seizure and seizure-free intervals (i.e. interictal and ictal) as well as to distinguish EO and EC conditions. The original time series data was converted into symbol sequences using quantization level, and word series of symbol sequences was generated using a word length of three or more. Then, normalized corrected Shannon entropy (NCSE) was computed to quantify the complexity. The NCSE values were not following the normal distribution, and the non-parametric Mann-Whitney-Wilcoxon (MWW) test was used to find significant differences among various groups at 0.05 significance level. The values of NCSE were presented in a form of topographic maps to show significant brain regions during EC and EO conditions. The results of the study were compared to those of the multiscale entropy (MSE). The results indicated that the dynamics of healthy subjects are more complex compared to epileptic subjects (during seizure and seizure-free intervals) in both EO and EC conditions. The comparison of the dynamics of epileptic subjects revealed that seizure-free intervals are more complex than seizure intervals. The dynamics of healthy subjects during EO conditions are more complex compared to those during EC conditions. Further, the results clearly demonstrated that threshold-dependent symbolic entropy outperform MSE in distinguishing different physiological and pathological conditions. The

  8. Pilomotor seizures: an autonomic semiology of limbic encephalitis?

    Science.gov (United States)

    Rocamora, Rodrigo; Becerra, Juan L; Fossas, Pilar; Gomez, María; Vivanco-Hidalgo, Rosa M; Mauri, José A; Molins, Albert

    2014-09-01

    Ictal piloerection is an infrequent seizure semiology that is commonly overlooked as an ictal epileptic manifestation. Piloerection is considered to be principally caused by temporal lobe activity although frontal and hypothalamic seizure origins have been reported. The described etiology has shown a wide variety of structural causes such as mesial temporal sclerosis, tumors, posttraumatic, cavernomas and cryptogenic epilepsies. We retrospectively reviewed the incidence of ictal piloerection in the clinical records of patients who underwent video-EEG monitoring (VEEGM) between 2007 and 2013 in a multicenter cooperative study. All patients presented refractory epilepsies and were evaluated with a protocol that included brain MRI, neuropsychology and VEEGM. A total of 766 patients were evaluated in four tertiary centers in Spain. Five patients showed piloerection as principal seizure semiology (prevalence 0.65%). The mean age at seizure onset was 39.6 years and the average epilepsy duration was 5.2 years (range 2-14) before diagnosis. Four patients were additionally examined with FDG-PET and/or SPECT-SISCOM. All presented temporal lobe epilepsy (TLE), three right-sided and two left-sided. A typical unilateral hippocampal sclerosis was described in 3 cases. The etiology detected in all cases was limbic encephalitis. Three had LGI1, one anti-Hu, and another Ma2 antibodies. Our series describes a so far not well-recognized autoimmune association of pilomotor seizures to limbic encephalitis. This etiology should be ruled out through a comprehensive diagnostic work-up even in cases of long-lasting TLE with typical hippocampal atrophy on MRI. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  9. Muon Detection Based on a Hadronic Calorimeter

    CERN Document Server

    Ciodaro, T; Abreu, R; Achenbach, R; Adragna, P; Aharrouche, M; Aielli, G; Al-Shabibi, A; Aleksandrov, I; Alexandrov, E; Aloisio, A; Alviggi, M G; Amorim, A; Amram, N; Andrei, V; Anduaga, X; Angelaszek, D; Anjos, N; Annovi, A; Antonelli, S; Anulli, F; Apolle, R; Aracena, I; Ask, S; Åsman, B; Avolio, G; Baak, M; Backes, M; Backlund, S; Badescu, E; Baines, J; Ballestrero, S; Banerjee, S; Bansil, H S; Barnett, B M; Bartoldus, R; Bartsch, V; Batraneanu, S; Battaglia, A; Bauss, B; Beauchemin, P; Beck, H P; Bee, C; Begel, M; Behera, P K; Bell, P; Bell, W H; Bellagamba, L; Bellomo, M; Ben Ami, S; Bendel, M; Benhammou, Y; Benslama, K; Berge, D; Bernius, C; Berry, T; Bianco, M; Biglietti, M; Blair, R E; Bogaerts, A; Bohm, C; Boisvert, V; Bold, T; Bondioli, M; Borer, C; Boscherini, D; Bosman, M; Bossini, E; Boveia, A; Bracinik, J; Brandt, A G; Brawn, I P; Brelier, B; Brenner, R; Bressler, S; Brock, R; Brooks, W K; Brown, G; Brunet, S; Bruni, A; Bruni, G; Bucci, F; Buda, S; Burckhart-Chromek, D; Buscher, V; Buttinger, W; Calvet, S; Camarri, P; Campanelli, M; Canale, V; Canelli, F; Capasso, L; Caprini, M; Caracinha, D; Caramarcu, C; Cardarelli, R; Carlino, G; Casadei, D; Casado, M P; Cattani, G; Cerri, A; Cerrito, L; Chapleau, B; Childers, J T; Chiodini, G; Christidi, I; Ciapetti, G; Cimino, D; Ciobotaru, M; Coccaro, A; Cogan, J; Collins, N J; Conde Muino, P; Conidi, C; Conventi, F; Corradi, M; Corso-Radu, A; Coura Torres, R; Cranmer, K; Crescioli, F; Crone, G; Crupi, R; Cuenca Almenar, C; Cummings, J T; Curtis, C J; Czyczula, Z; Dam, M; Damazio, D; Dao, V; Darlea, G L; Davis, A O; De Asmundis, R; De Pedis, D; De Santo, A; de Seixas, J M; Degenhardt, J; Della Pietra, M; Della Volpe, D; Demers, S; Demirkoz, B; Di Ciaccio, A; Di Mattia, A; Di Nardo, R; Di Simone, A; Diaz, M A; Dietzsch, T A; Dionisi, C; Dobson, E; Dobson, M; dos Anjos, A; Dotti, A; Dova, M T; Drake, G; Dufour, M-A; Dumitru, I; Eckweiler, S; Ehrenfeld, W; Eifert, T; Eisenhandler, E; Ellis, K V; Ellis, N; Emeliyanov, D; Enoque Ferreira de Lima, D; Ermoline, Y; Ernst, J; Etzion, E; Falciano, S; Farrington, S; Farthouat, P; Faulkner , P J W; Fedorko, W; Fellmann, D; Feng, E; Ferrag, S; Ferrari, R; Ferrer, M L; Fiorini, L; Fischer, G; Flowerdew, M J; Fonseca Martin, T; Francis, D; Fratina, S; French, S T; Front, D; Fukunaga, C; Gadomski, S; Garelli, N; Garitaonandia Elejabarrieta, H; Gaudio, G; Gee, C N P; George, S; Giagu, S; Giannetti, P; Gillman, A R; Giorgi, M; Giunta, M; Giusti, P; Goebel, M; Gonçalo, R; Gonzalez Silva, L; Göringer, C; Gorini, B; Gorini, E; Grabowska-Bold, I; Green, B; Groll, M; Guida, A; Guler, H; Haas, S; Hadavand, H; Hadley, D R; Haller, J; Hamilton, A; Hanke, P; Hansen, J R; Hasegawa, S; Hasegawa, Y; Hauser, R; Hayakawa, T; Hayden, D; Head, S; Heim, S; Hellman, S; Henke, M; Hershenhorn, A; Hidvégi, A; Hillert, S; Hillier, S J; Hirayama, S; Hod, N; Hoffmann, D; Hong, T M; Hryn'ova, T; Huston, J; Iacobucci, G; Igonkina, O; Ikeno, M; Ilchenko, Y; Ishikawa, A; Ishino, M; Iwasaki, H; Izzo, V; Jez, P; Jimenez Otero, S; Johansen, M; Johns, K; Jones, G; Joos, M; Kadlecik, P; Kajomovitz, E; Kanaya, N; Kanega, F; Kanno, T; Kapliy, A; Kaushik, V; Kawagoe, K; Kawamoto, T; Kazarov, A; Kehoe, R; Kessoku, K; Khomich, A; Khoriauli, G; Kieft, G; Kirk, J; Klemetti, M; Klofver, P; Klous, S; Kluge, E-E; Kobayashi, T; Koeneke, K; Koletsou, I; Koll, J D; Kolos, S; Kono, T; Konoplich, R; Konstantinidis, N; Korcyl, K; Kordas, K; Kotov, V; Kowalewski, R V; Krasznahorkay, A; Kraus, J; Kreisel, A; Kubota, T; Kugel, A; Kunkle, J; Kurashige, H; Kuze, M; Kwee, R; Laforge, B; Landon, M; Lane, J; Lankford, A J; Laranjeira Lima, S M; Larner, A; Leahu, L; Lehmann Miotto, G; Lei, X; Lellouch, D; Levinson, L; Li, S; Liberti, B; Lilley, J N; Linnemann, J T; Lipeles, E; Lohse, T; Losada, M; Lowe, A; Luci, C; Luminari, L; Lundberg, J; Lupu, N; Machado Miguéns, J; Mackeprang, R; Maettig, S; Magnoni, L; Maiani, C; Maltrana, D; Mangeard, P-S; Männer, R; Mapelli, L; Marchese, F; Marino, C; Martin, B; Martin, B T; Martin, T; Martyniuk, A; Marzano, F; Masik, J; Mastrandrea, P; Matsushita, T; McCarn, A; Mechnich, J; Medinnis, M; Meier, K; Melachrinos, C; Mendoza Nava, L M; Merola, L; Messina, A; Meyer, C P; Middleton, R P; Mikenberg, G; Mills, C M; Mincer, A; Mineev, M; Misiejuk, A; Moa, T; Moenig, K; Monk, J; Monticelli, F; Mora Herrera, C; Morettini, P; Morris, J D; Müller, F; Munwes, Y; Murillo Garcia, R; Nagano, K; Nagasaka, Y; Navarro, G A; Negri, A; Nelson, S; Nemethy, P; Neubauer, M S; Neusiedl, A; Newman, P; Nisati, A; Nomoto, H; Nozaki, M; Nozicka, M; Nurse, E; Ochando, C; Ochi, A; Oda, S; Oh, A; Ohm, C; Okumura, Y; Olivito, D; Omachi, C; Osculati, B; Oshita, H; Ospanov, R; Owen, M A; Özcan, V E; Ozone, K; Padilla, C; Panes, B; Panikashvili, N; Paramonov, A; Parodi, F; Pasqualucci, E; Pastore, F; Patricelli, S; Pauly, T; Perera, V J O; Perez, E; Petcu, M; Petersen, B A; Petersen, J; Petrolo, E; Phan, A; Piegaia, R; Pilkington, A; Pinder, A; Poddar, S; Polini, A; Pope, B G; Potter, C T; Primavera, M; Prokoshin, F; Ptacek, E; Qian, W; Quinonez, F; Rajagopalan, S; Ramos Dos Santos Neves, R; Reinherz-Aronis, E; Reinsch, A; Renkel, P; Rescigno, M; Rieke, S; Riu, I; Robertson, S H; Robinson, M; Rodriguez, D; Roich, A; Romeo, G; Romero, R; Roos, L; Ruiz Martinez, A; Ryabov, Y; Ryan, P; Saavedra, A; Safai Tehrani, F; Sakamoto, H; Salamanna, G; Salamon, A; Saland, J; Salnikov, A; Salvatore, F; Sankey, D P C; Santamarina, C; Santonico, R; Sarkisyan-Grinbaum, E; Sasaki, O; Savu, D; Scannicchio, D A; Schäfer, U; Scharf, V L; Scheirich, D; Schiavi, C; Schlereth, J; Schmitt, K; Schroder, C; Schroer, N; Schultz-Coulon, H-C; Schwienhorst, R; Sekhniaidze, G; Sfyrla, A; Shamim, M; Sherman, D; Shimojima, M; Shochet, M; Shooltz, D; Sidoti, A; Silbert, O; Silverstein, S; Sinev, N; Siragusa, G; Sivoklokov, S; Sjoen, R; Sjölin, J; Slagle, K; Sloper, J E; Smith, B C; Soffer, A; Soloviev, I; Spagnolo, S; Spiwoks, R; Staley, R J; Stamen, R; Stancu, S; Steinberg, P; Stelzer, J; Stockton, M C; Straessner, A; Strauss, E A; Strom, D; Su, D; Sugaya, Y; Sugimoto, T; Sushkov, S; Sutton, M R; Suzuki, Y; Taffard, A; Taiblum, N; Takahashi, Y; Takeda, H; Takeshita, T; Tamsett, M; Tan, C L A; Tanaka, S; Tapprogge, S; Tarem, S; Tarem, Z; Taylor, C; Teixeira-Dias, P; Thomas, J P; Thompson, P D; Thomson, M A; Tokushuku, K; Tollefson, K; Tomoto, M; Topfel, C; Torrence, E; Touchard, F; Traynor, D; Tremblet, L; Tricoli, A; Tripiana, M; Triplett, N; True, P; Tsiakiris, M; Tsuno, S; Tuggle, J; Ünel, G; Urquijo, P; Urrejola, P; Usai, G; Vachon, B; Vallecorsa, S; Valsan, L; Vandelli, W; Vari, R; Vaz Gil Lopes, L; Veneziano, S; Ventura, A; Venturi, N; Vercesi, V; Vermeulen, J C; Volpi, G; Vorwerk, V; Wagner, P; Wang, M; Warburton, A; Watkins, P M; Watson, A T; Watson, M; Weber, P; Weidberg, A R; Wengler, T; Werner, P; Werth, M; Wessels, M; White, M; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Winklmeier, F; Woods, K S; Wu, S-L; Wu, X; Xaplanteris Karampatsos, L; Xella, S; Yakovlev, A; Yamazaki, Y; Yang, U; Yasu, Y; Yuan, L; Zaitsev, A; Zanello, L; Zhang, H; Zhang, J; Zhao, L; Zobernig, H; zur Nedden, M

    2010-01-01

    The ATLAS Tile hadronic calorimeter (TileCal) provides highly-segmented energy measurements of incoming particles. The information from TileCal's last segmentation layer can assist in muon tagging and it is being considered for a near future upgrade of the level-one trigger, mainly for rejecting triggers due to cavern background at the barrel region. A muon receiver for the TileCal muon signals is being designed in order to interface with the ATLAS level-one trigger. This paper addresses the preliminary studies concerning the muon discrimination capability for the muon receiver. Monte Carlo simulations for single muons from the interaction point were used to study the effectiveness of hadronic calorimeter information on muon detection.

  10. Muon Detection Based on a Hadronic Calorimeter

    CERN Document Server

    Ciodaro, Thiago; Abreu, R; Achenbach, R; Adragna, P; Aharrouche, M; Aielli, G; Al-Shabibi, A; Aleksandrov, I; Alexandrov, E; Aloisio, A; Alviggi, M G; Amorim, A; Amram, N; Andrei, V; Anduaga, X; Angelaszek, D; Anjos, N; Annovi, A; Antonelli, S; Anulli, F; Apolle, R; Aracena, I; Ask, S; Åsman, B; Avolio, G; Baak, M; Backes, M; Backlund, S; Badescu, E; Baines, J; Ballestrero, S; Banerjee, S; Bansil, H S; Barnett, B M; Bartoldus, R; Bartsch, V; Batraneanu, S; Battaglia, A; Bauss, B; Beauchemin, P; Beck, H P; Bee, C; Begel, M; Behera, P K; Bell, P; Bell, W H; Bellagamba, L; Bellomo, M; Ben Ami, S; Bendel, M; Benhammou, Y; Benslama, K; Berge, D; Bernius, C; Berry, T; Bianco, M; Biglietti, M; Blair, R E; Bogaerts, A; Bohm, C; Boisvert, V; Bold, T; Bondioli, M; Borer, C; Boscherini, D; Bosman, M; Bossini, E; Boveia, A; Bracinik, J; Brandt, A G; Brawn, I P; Brelier, B; Brenner, R; Bressler, S; Brock, R; Brooks, W K; Brown, G; Brunet, S; Bruni, A; Bruni, G; Bucci, F; Buda, S; Burckhart-Chromek, D; Buscher, V; Buttinger, W; Calvet, S; Camarri, P; Campanelli, M; Canale, V; Canelli, F; Capasso, L; Caprini, M; Caracinha, D; Caramarcu, C; Cardarelli, R; Carlino, G; Casadei, D; Casado, M P; Cattani, G; Cerri, A; Cerrito, L; Chapleau, B; Childers, J T; Chiodini, G; Christidi, I; Ciapetti, G; Cimino, D; Ciobotaru, M; Coccaro, A; Cogan, J; Collins, N J; Conde Muino, P; Conidi, C; Conventi, F; Corradi, M; Corso-Radu, A; Coura Torres, R; Cranmer, K; Crescioli, F; Crone, G; Crupi, R; Cuenca Almenar, C; Cummings, J T; Curtis, C J; Czyczula, Z; Dam, M; Damazio, D; Dao, V; Darlea, G L; Davis, A O; De Asmundis, R; De Pedis, D; De Santo, A; de Seixas, J M; Degenhardt, J; Della Pietra, M; Della Volpe, D; Demers, S; Demirkoz, B; Di Ciaccio, A; Di Mattia, A; Di Nardo, R; Di Simone, A; Diaz, M A; Dietzsch, T A; Dionisi, C; Dobson, E; Dobson, M; dos Anjos, A; Dotti, A; Dova, M T; Drake, G; Dufour, M-A; Dumitru, I; Eckweiler, S; Ehrenfeld, W; Eifert, T; Eisenhandler, E; Ellis, K V; Ellis, N; Emeliyanov, D; Enoque Ferreira de Lima, D; Ermoline, Y; Ernst, J; Etzion, E; Falciano, S; Farrington, S; Farthouat, P; Faulkner, P J W; Fedorko, W; Fellmann, D; Feng, E; Ferrag, S; Ferrari, R; Ferrer, M L; Fiorini, L; Fischer, G; Flowerdew, M J; Fonseca Martin, T; Francis, D; Fratina, S; French, S T; Front, D; Fukunaga, C; Gadomski, S; Garelli, N; Garitaonandia Elejabarrieta, H; Gaudio, G; Gee, C N P; George, S; Giagu, S; Giannetti, P; Gillman, A R; Giorgi, M; Giunta, M; Giusti, P; Goebel, M; Gonçalo, R; Gonzalez Silva, L; Göringer, C; Gorini, B; Gorini, E; Grabowska-Bold, I; Green, B; Groll, M; Guida, A; Guler, H; Haas, S; Hadavand, H; Hadley, D R; Haller, J; Hamilton, A; Hanke, P; Hansen, J R; Hasegawa, S; Hasegawa, Y; Hauser, R; Hayakawa, T; Hayden, D; Head, S; Heim, S; Hellman, S; Henke, M; Hershenhorn, A; Hidvégi, A; Hillert, S; Hillier, S J; Hirayama, S; Hod, N; Hoffmann, D; Hong, T M; Hryn'ova, T; Huston, J; Iacobucci, G; Igonkina, O; Ikeno, M; Ilchenko, Y; Ishikawa, A; Ishino, M; Iwasaki, H; Izzo, V; Jez, P; Jimenez Otero, S; Johansen, M; Johns, K; Jones, G; Joos, M; Kadlecik, P; Kajomovitz, E; Kanaya, N; Kanega, F; Kanno, T; Kapliy, A; Kaushik, V; Kawagoe, K; Kawamoto, T; Kazarov, A; Kehoe, R; Kessoku, K; Khomich, A; Khoriauli, G; Kieft, G; Kirk, J; Klemetti, M; Klofver, P; Klous, S; Kluge, E-E; Kobayashi, T; Koeneke, K; Koletsou, I; Koll, J D; Kolos, S; Kono, T; Konoplich, R; Konstantinidis, N; Korcyl, K; Kordas, K; Kotov, V; Kowalewski, R V; Krasznahorkay, A; Kraus, J; Kreisel, A; Kubota, T; Kugel, A; Kunkle, J; Kurashige, H; Kuze, M; Kwee, R; Laforge, B; Landon, M; Lane, J; Lankford, A J; Laranjeira Lima, S M; Larner, A; Leahu, L; Lehmann Miotto, G; Lei, X; Lellouch, D; Levinson, L; Li, S; Liberti, B; Lilley, J N; Linnemann, J T; Lipeles, E; Lohse, T; Losada, M; Lowe, A; Luci, C; Luminari, L; Lundberg, J; Lupu, N; Machado Miguéns, J; Mackeprang, R; Maettig, S; Magnoni, L; Maiani, C; Maltrana, D; Mangeard, P-S; Männer, R; Mapelli, L; Marchese, F; Marino, C; Martin, B; Martin, B T; Martin, T; Martyniuk, A; Marzano, F; Masik, J; Mastrandrea, P; Matsushita, T; McCarn, A; Mechnich, J; Medinnis, M; Meier, K; Melachrinos, C; Mendoza Nava, L M; Merola, L; Messina, A; Meyer, C P; Middleton, R P; Mikenberg, G; Mills, C M; Mincer, A; Mineev, M; Misiejuk, A; Moa, T; Moenig, K; Monk, J; Monticelli, F; Mora Herrera, C; Morettini, P; Morris, J D; Müller, F; Munwes, Y; Murillo Garcia, R; Nagano, K; Nagasaka, Y; Navarro, G A; Negri, A; Nelson, S; Nemethy, P; Neubauer, M S; Neusiedl, A; Newman, P; Nisati, A; Nomoto, H; Nozaki, M; Nozicka, M; Nurse, E; Ochando, C; Ochi, A; Oda, S; Oh, A; Ohm, C; Okumura, Y; Olivito, D; Omachi, C; Osculati, B; Oshita, H; Ospanov, R; Owen, M A; Özcan, V E; Ozone, K; Padilla, C; Panes, B; Panikashvili, N; Paramonov, A; Parodi, F; Pasqualucci, E; Pastore, F; Patricelli, S; Pauly, T; Perera, V J O; Perez, E; Petcu, M; Petersen, B A; Petersen, J; Petrolo, E; Phan, A; Piegaia, R; Pilkington, A; Pinder, A; Poddar, S; Polini, A; Pope, B G; Potter, C T; Primavera, M; Prokoshin, F; Ptacek, E; Qian, W; Quinonez, F; Rajagopalan, S; Ramos Dos Santos Neves, R; Reinherz-Aronis, E; Reinsch, A; Renkel, P; Rescigno, M; Rieke, S; Riu, I; Robertson, S H; Robinson, M; Rodriguez, D; Roich, A; Romeo, G; Romero, R; Roos, L; Ruiz Martinez, A; Ryabov, Y; Ryan, P; Saavedra, A; Safai Tehrani, F; Sakamoto, H; Salamanna, G; Salamon, A; Saland, J; Salnikov, A; Salvatore, F; Sankey, D P C; Santamarina, C; Santonico, R; Sarkisyan-Grinbaum, E; Sasaki, O; Savu, D; Scannicchio, D A; Schäfer, U; Scharf, V L; Scheirich, D; Schiavi, C; Schlereth, J; Schmitt, K; Schroder, C; Schroer, N; Schultz-Coulon, H-C; Schwienhorst, R; Sekhniaidze, G; Sfyrla, A; Shamim, M; Sherman, D; Shimojima, M; Shochet, M; Shooltz, D; Sidoti, A; Silbert, O; Silverstein, S; Sinev, N; Siragusa, G; Sivoklokov, S; Sjoen, R; Sjölin, J; Slagle, K; Sloper, J E; Smith, B C; Soffer, A; Soloviev, I; Spagnolo, S; Spiwoks, R; Staley, R J; Stamen, R; Stancu, S; Steinberg, P; Stelzer, J; Stockton, M C; Straessner, A; Strauss, E A; Strom, D; Su, D; Sugaya, Y; Sugimoto, T; Sushkov, S; Sutton, M R; Suzuki, Y; Taffard, A; Taiblum, N; Takahashi, Y; Takeda, H; Takeshita, T; Tamsett, M; Tan, C L A; Tanaka, S; Tapprogge, S; Tarem, S; Tarem, Z; Taylor, C; Teixeira-Dias, P; Thomas, J P; Thompson, P D; Thomson, M A; Tokushuku, K; Tollefson, K; Tomoto, M; Topfel, C; Torrence, E; Touchard, F; Traynor, D; Tremblet, L; Tricoli, A; Tripiana, M; Triplett, N; True, P; Tsiakiris, M; Tsuno, S; Tuggle, J; Ünel, G; Urquijo, P; Urrejola, P; Usai, G; Vachon, B; Vallecorsa, S; Valsan, L; Vandelli, W; Vari, R; Vaz Gil Lopes, L; Veneziano, S; Ventura, A; Venturi, N; Vercesi, V; Vermeulen, J C; Volpi, G; Vorwerk, V; Wagner, P; Wang, M; Warburton, A; Watkins, P M; Watson, A T; Watson, M; Weber, P; Weidberg, A R; Wengler, T; Werner, P; Werth, M; Wessels, M; White, M; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Winklmeier, F; Woods, K S; Wu, S-L; Wu, X; Xaplanteris Karampatsos, L; Xella, S; Yakovlev, A; Yamazaki, Y; Yang, U; Yasu, Y; Yuan, L; Zaitsev, A; Zanello, L; Zhang, H; Zhang, J; Zhao, L; Zobernig, H; zur Nedden, M

    2010-01-01

    The TileCal hadronic calorimeter provides a muon signal which can be used to assist in muon tagging at the ATLAS level-one trigger. Originally, the muon signal was conceived to be combined with the RPC trigger in order to reduce unforeseen high trigger rates due to cavern background. Nevertheless, the combined trigger cannot significantly deteriorate the muon detection performance at the barrel region. This paper presents preliminary studies concerning the impact in muon identification at the ATLAS level-one trigger, through the use of Monte Carlo simulations with single muons with 40 GeV/c momentum. Further, different trigger scenarios were proposed, together with an approach for matching both TileCal and RPC geometries.

  11. Humidity detection using chitosan film based sensor

    Science.gov (United States)

    Nasution, T. I.; Nainggolan, I.; Dalimunthe, D.; Balyan, M.; Cuana, R.; Khanifah, S.

    2018-02-01

    A humidity sensor made of the natural polymer chitosan has been successfully fabricated in the film form by a solution casting method. Humidity testing was performed by placing a chitosan film sensor in a cooling machine room, model KT-2000 Ahu. The testing results showed that the output voltage values of chitosan film sensor increased with the increase in humidity percentage. For the increase in humidity percentage from 30 to 90% showed that the output voltage of chitosan film sensor increased from 32.19 to 138.75 mV. It was also found that the sensor evidenced good repeatability and stability during the testing. Therefore, chitosan has a great potential to be used as new sensing material for the humidity detection of which was cheaper and environmentally friendly.

  12. Robust face detection based on components and their topology

    Science.gov (United States)

    Goldmann, Lutz; Mönich, Ullrich; Sikora, Thomas

    2006-01-01

    This paper presents a novel approach for automatic and robust object detection. It utilizes a component-based approach that combines techniques from both statistical and structural pattern recognition domain. While the component detection relies on Haar-like features and an AdaBoost trained classifier cascade, the topology verification is based on graph matching techniques. The system was applied to face detection and the experiments show its outstanding performance in comparison to other face detection approaches. Especially in the presence of partial occlusions, uneven illumination and out-of-plane rotations it yields higher robustness.

  13. Effect of age and anticonvulsants on seizure threshold during bilateral electroconvulsive therapy with brief-pulse stimulus: A chart-based analysis

    Science.gov (United States)

    Nitturkar, Abhishek R.; Sinha, Preeti; Bagewadi, Virupakshappa I.; Thirthalli, Jagadisha

    2016-01-01

    Background: Efficacy and adverse effects of electroconvulsive therapy (ECT) depend on the extent to which the electrical stimulus exceeds patients' seizure thresholds (STs). Titration method of estimating ST is recommended. Age and co-prescribed anticonvulsants (ACs) are known to affect ST. Literature on ST in bilateral ECT (BLECT) is sparse. Objective: To explore the clinical and demographic determinants of ST in a clinically representative sample of patients prescribed with BLECT. Materials and Methods: ECT records of 640 patients who received BLECT in 2011 in an academic psychiatric setting were studied. Demographic, clinical, pharmacological, and ECT details were analyzed. As per the standard practice, during the 1st ECT session, ST was determined by titration method, starting with 30 milli-Coulombs (mC) and increasing by 30 mC and thence in steps of 60 mC. Increase in ST over up to 6th session of ECT was noted. Receiver operating characteristic curve was used to find age cut-off with high specificity for ST ≥120 mC. The associations of ST and increase in ST with the age cut-off and other clinical factors were assessed using Chi-square test and logistic regression analysis. Results: The mean age was 30.98 years (+11.23 years) and mean ST at 1st ECT session was 130.36 mC (+51.96 mC). There was significantly high positive correlation (r = 0.37, P < 0.001) between age and ST. Cut-off age of 45 years had high specificity: Only 4.6% of those older than 45 years had ST <120 mC. Higher proportion of patients on AC had ST ≥120 mC. These associations were seen even after controlling for potential confounds of each other using logistic regression analysis. The results were similar for increase in ST over the course of ECT. Sex, diagnosis, use of antipsychotics, antidepressants, lithium, and benzodiazepines (BZPs) had no effect on ST or its increase. Conclusions: For BLECT using brief-pulse stimulus, ST depends on age and use of AC. For patients above the age of 45

  14. Segment-based change detection for polarimetric SAR data

    DEFF Research Database (Denmark)

    Skriver, Henning; Nielsen, Allan Aasbjerg; Conradsen, Knut

    2006-01-01

    single-channel SAR images but multi-channel algorithms have also been described. Different approaches have been used for image segmentation. Edge detection combined with region growing is one approach, where segments are created by growing regions from a previously edge detected and edge thinned image....... This method relies primarily on a robust edge detector, which preferably provides a constant false alarm rate. For single-channel SAR images this is fulfilled by the ratio edge detector, and for polarimetric SAR data, an edge detector based on the above mentioned test statistic fulfils this. Another approach......, wetlands, lakes, and urban areas. Also, other test sites over for instance urban areas have been used to assess the improvement by the segment-based change detection method. In the paper, results from pixel-based change detection, i.e. without segmentation, and from segment-based change detection, where...

  15. Seizures

    Science.gov (United States)

    ... the attack, such as: Fear or anxiety Nausea Vertigo (feeling as if you are spinning or moving) ... body due to liver or kidney failure Very high blood pressure ( malignant hypertension ) Venomous bites and stings ( snake bite ) ...

  16. Seizures in E200K familial and sporadic Creutzfeldt-Jakob disease.

    Science.gov (United States)

    Appel, S; Chapman, J; Cohen, O S; Rosenmann, H; Nitsan, Z; Blatt, I

    2015-03-01

    Although seizures (other than myoclonus) are frequently reported in Creutzfeldt-Jakob disease (CJD), their frequency, clinical manifestations, and effect on the disease course is unknown. To characterize the frequency of seizures in E200K familial and sporadic CJD, to describe its semiology, EEG and MRI findings. In this retrospective study, we reviewed all patients with CJD who were seen in the Sheba Medical Center between the years 2003-2012 and underwent clinical evaluation, genetic testing, EEG and MRI studies. The diagnosis of seizures was carried out based on documentation of episodes consistent with seizures or episode of unresponsiveness correlated with ictal activity in EEG. Sixty-four probable patients with CJD were included in the study, 57 (89%) with E200K familial (fCJD) and 7 (11%) with sporadic (sCJD). Seizures occurred in 8 patients: 3 of 7 (43%) in patients with sCJD compared to 5/57 (9%) in patients with E200K fCJD (P = 0.04, chi-square test). Two of E200K fCJD patients with seizures had other non-prion etiologies for seizures (brain metastasis, known history of temporal lobe epilepsy which started 44 years before the diagnosis of CJD). Seizures occurred late in the course of the disease with an average of 12 days between the onset of seizures and death. Seizures in E200K fCJD were infrequent and occurred late in the disease course. This difference suggests that E200K fCJD represents a separate subtype of the disease with distinct clinical characteristics. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  17. The role of the basal ganglia in the control of seizure.

    Science.gov (United States)

    Vuong, J; Devergnas, Annaelle

    2018-03-01

    Epilepsy is a network disorder and each type of seizure involves distinct cortical and subcortical network, differently implicated in the control and propagation of the ictal activity. The role of the basal ganglia has been revealed in several cases of focal and generalized seizures. Here, we review the data that show the implication of the basal ganglia in absence, temporal lobe, and neocortical seizures in animal models (rodent, cat, and non-human primate) and in human. Based on these results and the advancement of deep brain stimulation for Parkinson's disease, basal ganglia neuromodulation has been tested with some success that can be equally seen as promising or disappointing. The effect of deep brain stimulation can be considered promising with a 76% in seizure reduction in temporal lobe epilepsy patients, but also disappointing, since only few patients have become seizure free and the antiepileptic effects have been highly variable among patients. This variability could probably be explained by the heterogeneity among the patients included in these clinical studies. To illustrate the importance of specific network identification, electrophysiological activity of the putamen and caudate nucleus has been recorded during penicillin-induced pre-frontal and motor seizures in one monkey. While an increase of the firing rate was found in putamen and caudate nucleus during pre-frontal seizures, only the activity of the putamen cells was increased during motor seizures. These preliminary results demonstrate the implication of the basal ganglia in two types of neocortical seizures and the necessity of studying the network to identify the important nodes implicated in the propagation and control of each type of seizure.

  18. Research on moving object detection based on frog's eyes

    Science.gov (United States)

    Fu, Hongwei; Li, Dongguang; Zhang, Xinyuan

    2008-12-01

    On the basis of object's information processing mechanism with frog's eyes, this paper discussed a bionic detection technology which suitable for object's information processing based on frog's vision. First, the bionics detection theory by imitating frog vision is established, it is an parallel processing mechanism which including pick-up and pretreatment of object's information, parallel separating of digital image, parallel processing, and information synthesis. The computer vision detection system is described to detect moving objects which has special color, special shape, the experiment indicates that it can scheme out the detecting result in the certain interfered background can be detected. A moving objects detection electro-model by imitating biologic vision based on frog's eyes is established, the video simulative signal is digital firstly in this system, then the digital signal is parallel separated by FPGA. IN the parallel processing, the video information can be caught, processed and displayed in the same time, the information fusion is taken by DSP HPI ports, in order to transmit the data which processed by DSP. This system can watch the bigger visual field and get higher image resolution than ordinary monitor systems. In summary, simulative experiments for edge detection of moving object with canny algorithm based on this system indicate that this system can detect the edge of moving objects in real time, the feasibility of bionic model was fully demonstrated in the engineering system, and it laid a solid foundation for the future study of detection technology by imitating biologic vision.

  19. Aptamer-Based Technologies in Foodborne Pathogen Detection

    Directory of Open Access Journals (Sweden)

    Jun Teng

    2016-09-01

    Full Text Available Aptamers are single stranded DNA or RNA ligands, which can be selected by a method called systematic evolution of ligands by exponential enrichment (SELEX; and they can specifically recognize and bind to their targets. These unique characteristics of aptamers offer great potentials in applications such as pathogen detection and biomolecular screening. Pathogen detection is the first and critical means in detecting and identifying the problems related to public health and food safety; and only the rapid, sensitive and efficient detection technologies can enable the users to make to accurate assessments on the risk of infections (humans and animals or contaminations (foods and other commodities caused by various pathogens. This article reviews the developments in the field of the aptamer-based approaches for pathogen detection, including whole-cell SELEX and Genomic SELEX. Nowadays, a variety of aptamer-based biosensors have been developed for pathogen detection. Thus, in this review, we also cover the development of aptamer-based biosensors including optical biosensors for multiple pathogen detection in multiple-labeling or label-free models such as fluorescence detection and surface plasmon resonance, electrochemical biosensors, and lateral chromatography test strips, and their applications in the pathogen detection and biomolecular screening. While notable progress has been made in the field in the last decade, challenges or drawbacks in their applications such as pathogen detection and biomolecular screening, remain to be overcome.

  20. Aptamer-Based Technologies in Foodborne Pathogen Detection.

    Science.gov (United States)

    Teng, Jun; Yuan, Fang; Ye, Yingwang; Zheng, Lei; Yao, Li; Xue, Feng; Chen, Wei; Li, Baoguang

    2016-01-01

    Aptamers are single stranded DNA or RNA ligands, which can be selected by a method called systematic evolution of ligands by exponential enrichment (SELEX); and they can specifically recognize and bind to their targets. These unique characteristics of aptamers offer great potentials in applications such as pathogen detection and biomolecular screening. Pathogen detection is the critical means in detecting and identifying the problems related to public health and food safety; and only the rapid, sensitive and efficient detection technologies can enable the users to make the accurate assessments on the risks of infections (humans and animals) or contaminations (foods and other commodities) caused by various pathogens. This article reviews the development in the field of the aptamer-based approaches for pathogen detection, including whole-cell SELEX and Genomic SELEX. Nowadays, a variety of aptamer-based biosensors have been developed for pathogen detection. Thus, in this review, we also cover the development in aptamer-based biosensors including optical biosensors for multiple pathogen detection by multiple-labeling or label-free models such as fluorescence detection and surface plasmon resonance, electrochemical biosensors and lateral chromatography test strips, and their applications in pathogen detection and biomolecular screening. While notable progress has been made in the field in the last decade, challenges or drawbacks in their applications such as pathogen detection and biomolecular screening remain to be overcome.

  1. Complex partial seizures: cerebellar metabolism

    Energy Technology Data Exchange (ETDEWEB)

    Theodore, W.H.; Fishbein, D.; Deitz, M.; Baldwin, P.

    1987-07-01

    We used positron emission tomography (PET) with (/sup 18/F)2-deoxyglucose to study cerebellar glucose metabolism (LCMRglu) and the effect of phenytoin (PHT) in 42 patients with complex partial seizures (CPS), and 12 normal controls. Mean +/- SD patient LCMRglu was 6.9 +/- 1.8 mg glucose/100 g/min (left = right), significantly lower than control values of 8.5 +/- 1.8 (left, p less than 0.006), and 8.3 +/- 1.6 (right, p less than 0.02). Only four patients had cerebellar atrophy on CT/MRI; cerebellar LCMRglu in these was 5.5 +/- 1.5 (p = 0.054 vs. total patient sample). Patients with unilateral temporal hypometabolism or EEG foci did not have lateralized cerebellar hypometabolism. Patients receiving phenytoin (PHT) at the time of scan and patients with less than 5 years total PHT exposure had lower LCMRglu, but the differences were not significant. There were weak inverse correlations between PHT level and cerebellar LCMRglu in patients receiving PHT (r = -0.36; 0.05 less than p less than 0.1), as well as between length of illness and LCMRglu (r = -0.22; 0.05 less than p less than 0.1). Patients with complex partial seizures have cerebellar hypometabolism that is bilateral and due only in part to the effect of PHT.

  2. Accelerator based techniques for contraband detection

    Science.gov (United States)

    Vourvopoulos, George

    1994-05-01

    It has been shown that narcotics, explosives, and other contraband materials, contain various chemical elements such as H, C, N, O, P, S, and Cl in quantities and ratios that differentiate them from each other and from other innocuous substances. Neutrons and γ-rays have the ability to penetrate through various materials at large depths. They are thus able, in a non-intrusive way, to interrogate volumes ranging from suitcases to Sea-Land containers, and have the ability to image the object with an appreciable degree of reliability. Neutron induced reactions such as (n, γ), (n, n') (n, p) or proton induced γ-resonance absorption are some of the reactions currently investigated for the identification of the chemical elements mentioned above. Various DC and pulsed techniques are discussed and their advantages, characteristics, and current progress are shown. Areas where use of these methods is currently under evaluation are detection of hidden explosives, illicit drug interdiction, chemical war agents identification, nuclear waste assay, nuclear weapons destruction and others.

  3. Novel gas-based detection techniques

    International Nuclear Information System (INIS)

    Graaf, Harry van der

    2009-01-01

    This year we celebrate the 100th birthday of gaseous detectors: Hans Geiger operated the first gas-filled counter in Manchester in 1908. The thin wires, essential for obtaining gas amplification, have been replaced by Micro Pattern Gas Detectors (MPGDs): Micromegas (1995) and GEM (1996). In the GridPix detector, each of the grid holes of a MPGD is equipped with its own electronic readout channel in the form of an active pixel in suitable pixel CMOS chips. By means of MEMS technology, the grid has been integrated with the chip, forming a monolithic readout device for gas volumes. By applying a protection layer made of hydrogenated amorphous silicon, the chips can be made spark proof. New protection layers have been made of silicon nitride. The use of gas as detection material for trackers is compared to Si, and the issue of chamber aging is addressed. New developments are set out: the development of Micro Channel Plates, integrated on pixel chips, the development of electron emission foil, and the realization of TimePix-2: a general-purpose pixel chip with time and amplitude measurement, per pixel, of charge signals.

  4. Neutron detection with water Cerenkov based detectors

    International Nuclear Information System (INIS)

    Dazeley, S.; Bernstein, A.; Bowden, N.; Carr, D.; Ouedraogo, S.; Svoboda, R.; Sweany, M.; Tripathi, M.

    2009-01-01

    Legitimate cross border trade involves the transport of an enormous number of cargo containers. Especially following the September 11 attacks, it has become an international priority to verify that these containers are not transporting Special Nuclear Material (SNM) without impeding legitimate trade. Fission events from SNM produce a number of neutrons and MeV-scale gammas correlated in time. The observation of consistent time correlations between neutrons and gammas emitted from a cargo container could, therefore, constitute a robust signature for SNM, since this time coincident signature stands out strongly against the higher rate of uncorrelated gamma-ray backgrounds from the local environment. We are developing a cost effective way to build very large neutron detectors for this purpose. We have recently completed the construction of two new water Cherenkov detectors, a 250 liter prototype and a new 4-ton detector. The 250-liter prototype uses an ultra-pure water detection medium doped with a small amount of gadolinium tri-chloride (0.2%). A 55 μCi 252 Cf neutron source was placed at a distance of 1 meter from the detector behind a 2 inch thick wall of lead. The presence of the source is easily discernible from the background in both the uncorrelated count rate and the correlated one. The 4-ton detector will shortly undergo filling and testing

  5. De novo psychogenic seizures after epilepsy surgery: case report

    Directory of Open Access Journals (Sweden)

    MONTENEGRO MARIA AUGUSTA

    2000-01-01

    Full Text Available The occurrence of de novo psychogenic seizures after epilepsy surgery is rare, and is estimated in 1.8% to 3.6%. Seizures after epilepsy surgery should be carefully evaluated, and de novo psychogenic seizures should be considered especially when there is a change in the ictal semiology. We report a patient with de novo psychogenic seizures after anterior temporal lobe removal for refractory temporal lobe epilepsy. Once psychogenic seizures were diagnosed and psychiatric treatment was started, seizures stopped.

  6. SCOPE-mTL: A non-invasive tool for identifying and lateralizing mesial temporal lobe seizures prior to scalp EEG ictal onset.

    Science.gov (United States)

    Lam, Alice D; Maus, Douglas; Zafar, Sahar F; Cole, Andrew J; Cash, Sydney S

    2017-09-01

    In mesial temporal lobe (mTL) epilepsy, seizure onset can precede the appearance of a scalp EEG ictal pattern by many seconds. The ability to identify this early, occult mTL seizure activity could improve lateralization and localization of mTL seizures on scalp EEG. Using scalp EEG spectral features and machine learning approaches on a dataset of combined scalp EEG and foramen ovale electrode recordings in patients with mTL epilepsy, we developed an algorithm, SCOPE-mTL, to detect and lateralize early, occult mTL seizure activity, prior to the appearance of a scalp EEG ictal pattern. Using SCOPE-mTL, 73% of seizures with occult mTL onset were identified as such, and no seizures that lacked an occult mTL onset were identified as having one. Predicted mTL seizure onset times were highly correlated with actual mTL seizure onset times (r=0.69). 50% of seizures with early mTL onset were lateralizable prior to scalp ictal onset, with 94% accuracy. SCOPE-mTL can identify and lateralize mTL seizures prior to scalp EEG ictal onset, with high sensitivity, specificity, and accuracy. Quantitative analysis of scalp EEG can provide important information about mTL seizures, even in the absence of a visible scalp EEG ictal correlate. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  7. GNSS Spoofing Detection Based on Signal Power Measurements: Statistical Analysis

    Directory of Open Access Journals (Sweden)

    V. Dehghanian

    2012-01-01

    Full Text Available A threat to GNSS receivers is posed by a spoofing transmitter that emulates authentic signals but with randomized code phase and Doppler values over a small range. Such spoofing signals can result in large navigational solution errors that are passed onto the unsuspecting user with potentially dire consequences. An effective spoofing detection technique is developed in this paper, based on signal power measurements and that can be readily applied to present consumer grade GNSS receivers with minimal firmware changes. An extensive statistical analysis is carried out based on formulating a multihypothesis detection problem. Expressions are developed to devise a set of thresholds required for signal detection and identification. The detection processing methods developed are further manipulated to exploit incidental antenna motion arising from user interaction with a GNSS handheld receiver to further enhance the detection performance of the proposed algorithm. The statistical analysis supports the effectiveness of the proposed spoofing detection technique under various multipath conditions.

  8. Seizures in adults with bacterial meningitis

    NARCIS (Netherlands)

    Zoons, E.; Weisfelt, M.; de Gans, J.; Spanjaard, L.; Koelman, J. H. T. M.; Reitsma, J. B.; van de Beek, D.

    2008-01-01

    Objective: To evaluate the occurrence and prognostic relevance of seizures in adults with community-acquired bacterial meningitis. Methods: An observational cross-sectional study, in which patients with seizures are selected from a prospective nationwide cohort of 696 episodes of community-acquired

  9. A Neonate with persistent hypoglycemia and seizures.

    African Journals Online (AJOL)

    MBY

    disorder was diagnosed and managed with limited success as the episodes hydroglycemic seizures persisted. ... the presence of hyperinsulinemia as the cause of the hypoglycemic dependent seizures. Case Presentation. A three day old girl was admitted to the neonatal .... the Prader-Willi syndrome, has been reported.

  10. Seizure complicating interscalene brachail plexus block | Idehen ...

    African Journals Online (AJOL)

    We describe a case of seizure occurring immediately after completion of interscalene brachial plexus block, using 20mls mixture of 10mls of 0.5% bupivacaine and 10mls of 2% lidocaine with adrenaline for post operative analgesia. Seizure occurred despite negative test aspiration and non response to the use of 0.5mls of ...

  11. febrile seizures, Tripoli, Libya, knowledge, attitude

    African Journals Online (AJOL)

    kim

    aim of the audit is to assess the attitude and knowledge of parents of children with febrile seizures before .... The purpose of the study was explained to all parents and written informed consent was also obtained. Sample Description. Parents who witnessed the febrile seizure had rushed the child to the hospital as the first ...

  12. Long-term treatment with responsive brain stimulation in adults with refractory partial seizures.

    Science.gov (United States)

    Bergey, Gregory K; Morrell, Martha J; Mizrahi, Eli M; Goldman, Alica; King-Stephens, David; Nair, Dileep; Srinivasan, Shraddha; Jobst, Barbara; Gross, Robert E; Shields, Donald C; Barkley, Gregory; Salanova, Vicenta; Olejniczak, Piotr; Cole, Andrew; Cash, Sydney S; Noe, Katherine; Wharen, Robert; Worrell, Gregory; Murro, Anthony M; Edwards, Jonathan; Duchowny, Michael; Spencer, David; Smith, Michael; Geller, Eric; Gwinn, Ryder; Skidmore, Christopher; Eisenschenk, Stephan; Berg, Michel; Heck, Christianne; Van Ness, Paul; Fountain, Nathan; Rutecki, Paul; Massey, Andrew; O'Donovan, Cormac; Labar, Douglas; Duckrow, Robert B; Hirsch, Lawrence J; Courtney, Tracy; Sun, Felice T; Seale, Cairn G

    2015-02-24

    The long-term efficacy and safety of responsive direct neurostimulation was assessed in adults with medically refractory partial onset seizures. All participants were treated with a cranially implanted responsive neurostimulator that delivers stimulation to 1 or 2 seizure foci via chronically implanted electrodes when specific electrocorticographic patterns are detected (RNS System). Participants had completed a 2-year primarily open-label safety study (n = 65) or a 2-year randomized blinded controlled safety and efficacy study (n = 191); 230 participants transitioned into an ongoing 7-year study to assess safety and efficacy. The average participant was 34 (±11.4) years old with epilepsy for 19.6 (±11.4) years. The median preimplant frequency of disabling partial or generalized tonic-clonic seizures was 10.2 seizures a month. The median percent seizure reduction in the randomized blinded controlled trial was 44% at 1 year and 53% at 2 years (p < 0.0001, generalized estimating equation) and ranged from 48% to 66% over postimplant years 3 through 6 in the long-term study. Improvements in quality of life were maintained (p < 0.05). The most common serious device-related adverse events over the mean 5.4 years of follow-up were implant site infection (9.0%) involving soft tissue and neurostimulator explantation (4.7%). The RNS System is the first direct brain responsive neurostimulator. Acute and sustained efficacy and safety were demonstrated in adults with medically refractory partial onset seizures arising from 1 or 2 foci over a mean follow-up of 5.4 years. This experience supports the RNS System as a treatment option for refractory partial seizures. This study provides Class IV evidence that for adults with medically refractory partial onset seizures, responsive direct cortical stimulation reduces seizures and improves quality of life over a mean follow-up of 5.4 years. © 2015 American Academy of Neurology.

  13. Seizure phenotypes, periodicity, and sleep-wake pattern of seizures in Kcna-1 null mice.

    Science.gov (United States)

    Wright, Samantha; Wallace, Eli; Hwang, Youngdeok; Maganti, Rama

    2016-02-01

    This study was undertaken to describe seizure phenotypes, natural progression, sleep-wake patterns, as well as periodicity of seizures in Kcna-1 null mutant mice. These mice were implanted with epidural electroencephalography (EEG) and electromyography (EMG) electrodes, and simultaneous video-EEG recordings were obtained while animals were individually housed under either diurnal (LD) condition or constant darkness (DD) over ten days of recording. The video-EEG data were analyzed to identify electrographic and behavioral phenotypes and natural progression and to examine the periodicity of seizures. Sleep-wake patterns were analyzed to understand the distribution and onset of seizures across the sleep-wake cycle. Four electrographically and behaviorally distinct seizure types were observed. Regardless of lighting condition that animals were housed in, Kcna-1 null mice initially expressed only a few of the most severe seizure types that progressively increased in frequency and decreased in seizure severity. In addition, a circadian periodicity was noted, with seizures peaking in the first 12h of the Zeitgeber time (ZT) cycle, regardless of lighting conditions. Interestingly, seizure onset differed between lighting conditions where more seizures arose out of sleep in LD conditions, whereas under DD conditions, the majority occurred out of the wakeful state. We suggest that this model be used to understand the circadian pattern of seizures as well as the pathophysiological implications of sleep and circadian disturbances in limbic epilepsies. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. Do recurrent seizure-related head injuries affect seizures in people with epilepsy?

    Science.gov (United States)

    Friedman, David E.; Chiang, Sharon; Tobias, Ronnie S.

    2015-01-01

    Seizure-related head injuries (SRHIs) are among the most commonly encountered injuries in people with epilepsy (PWE). Whether head injury has an effect on preexisting epilepsy is not known. The purpose of this study was to systematically assess for any possible effects of SRHIs on seizure frequency and seizure semiology over a 2-year period. We identified 204 patients who have been followed at the Baylor Comprehensive Epilepsy Center from 2008 to 2010. SRHI occurred in 18.1% of the cohort. Most injuries (91%) were classified as mild. Though seizure frequency varied following head injury, overall seizure frequency was not significantly impacted by presence or absence of SRHI over the 2-year study period. Changes in seizure semiology were not observed in those with SRHIs. Although mild SRHI is common among PWE, it does not appear to have an effect on seizure characteristics over a relatively short period. PMID:22227592

  15. Research about Memory Detection Based on the Embedded Platform

    Science.gov (United States)

    Sun, Hao; Chu, Jian

    As is known to us all, the resources of memory detection of the embedded systems are very limited. Taking the Linux-based embedded arm as platform, this article puts forward two efficient memory detection technologies according to the characteristics of the embedded software. Especially for the programs which need specific libraries, the article puts forwards portable memory detection methods to help program designers to reduce human errors,improve programming quality and therefore make better use of the valuable embedded memory resource.

  16. Adeno-associated viral vector-induced overexpression of neuropeptide Y Y2 receptors in the hippocampus suppresses seizures

    DEFF Research Database (Denmark)

    Woldbye, David Paul Drucker; Ängehagen, Mikael; Gøtzsche, Casper René

    2010-01-01

    Gene therapy using recombinant adeno-associated viral vectors overexpressing neuropeptide Y in the hippocampus exerts seizure-suppressant effects in rodent epilepsy models and is currently considered for clinical application in patients with intractable mesial temporal lobe epilepsy. Seizure...... recombinant adeno-associated viral vectors. In two temporal lobe epilepsy models, electrical kindling and kainate-induced seizures, vector-based transduction of Y2 receptor complementary DNA in the hippocampus of adult rats exerted seizure-suppressant effects. Simultaneous overexpression of Y2...

  17. Developing nucleic acid-based electrical detection systems

    Directory of Open Access Journals (Sweden)

    Gabig-Ciminska Magdalena

    2006-03-01

    Full Text Available Abstract Development of nucleic acid-based detection systems is the main focus of many research groups and high technology companies. The enormous work done in this field is particularly due to the broad versatility and variety of these sensing devices. From optical to electrical systems, from label-dependent to label-free approaches, from single to multi-analyte and array formats, this wide range of possibilities makes the research field very diversified and competitive. New challenges and requirements for an ideal detector suitable for nucleic acid analysis include high sensitivity and high specificity protocol that can be completed in a relatively short time offering at the same time low detection limit. Moreover, systems that can be miniaturized and automated present a significant advantage over conventional technology, especially if detection is needed in the field. Electrical system technology for nucleic acid-based detection is an enabling mode for making miniaturized to micro- and nanometer scale bio-monitoring devices via the fusion of modern micro- and nanofabrication technology and molecular biotechnology. The electrical biosensors that rely on the conversion of the Watson-Crick base-pair recognition event into a useful electrical signal are advancing rapidly, and recently are receiving much attention as a valuable tool for microbial pathogen detection. Pathogens may pose a serious threat to humans, animal and plants, thus their detection and analysis is a significant element of public health. Although different conventional methods for detection of pathogenic microorganisms and their toxins exist and are currently being applied, improvements of molecular-based detection methodologies have changed these traditional detection techniques and introduced a new era of rapid, miniaturized and automated electrical chip detection technologies into pathogen identification sector. In this review some developments and current directions in

  18. Scintillation particle detection based on microfluidics

    CERN Document Server

    Mapelli, A; Renaud, P; Gorini, B; Trivino, N Vico; Jiguet, S; Vandelli, W; Haguenauer, M

    2010-01-01

    A novel type of particle detector based on scintillation, with precise spatial resolution and high radiation hardness, is being studied. It consists of a single microfluidic channel filled with a liquid scintillator and is designed to define an array of scintillating waveguides each independently coupled to a photodetector. Prototype detectors built using an SU-8 epoxy resin have been tested with electrons from a radioactive source. The experimental results show a light yield compatible with the theoretical expectations and confirm the validity of the approach. (C) 2010 Elsevier B.V. All rights reserved.

  19. Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

    Directory of Open Access Journals (Sweden)

    Wenhui Li

    2014-01-01

    Full Text Available Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS and Blind Spot Detection Systems (BSDS. The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS.

  20. Video-Based Affect Detection in Noninteractive Learning Environments

    Science.gov (United States)

    Chen, Yuxuan; Bosch, Nigel; D'Mello, Sidney

    2015-01-01

    The current paper explores possible solutions to the problem of detecting affective states from facial expressions during text/diagram comprehension, a context devoid of interactive events that can be used to infer affect. These data present an interesting challenge for face-based affect detection because likely locations of affective facial…

  1. Revisiting Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.

    2009-01-01

    Intrusion detection systems (IDSs) are well-known and widely-deployed security tools to detect cyber-attacks and malicious activities in computer systems and networks. A signature-based IDS works similar to anti-virus software. It employs a signature database of known attacks, and a successful match

  2. Model Based Fault Detection in a Centrifugal Pump Application

    DEFF Research Database (Denmark)

    Kallesøe, Carsten; Cocquempot, Vincent; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    A model based approach for fault detection in a centrifugal pump, driven by an induction motor, is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, observer design and Analytical Redundancy Relation (ARR) design. Structural considerations...

  3. Post-stroke seizures are clinically underestimated.

    Science.gov (United States)

    Bentes, Carla; Martins, Hugo; Peralta, Ana Rita; Casimiro, Carlos; Morgado, Carlos; Franco, Ana Catarina; Fonseca, Ana Catarina; Geraldes, Ruth; Canhão, Patrícia; Pinho E Melo, Teresa; Paiva, Teresa; Ferro, José M

    2017-09-01

    Cerebrovascular disease is the leading cause of epilepsy in adults, although post-stroke seizures reported frequency is variable and few studies used EEG in their identification. To describe and compare EEG and clinical epileptic manifestations frequency in patients with an anterior circulation ischaemic stroke. Prospective study of acute anterior circulation ischaemic stroke patients, consecutively admitted to a Stroke Unit over 24 months and followed-up for 1 year. All patients underwent standardized clinical and diagnostic assessment. Seizure occurrence was clinically evaluated during hospitalization and by a telephone interview at 6 months and a clinical appointment at 12 months after stroke. Video-EEG was performed in the first 72 h (1st EEG), daily after the 1st EEG for the first 7 days after the stroke, or later if neurological worsening, at discharge, and at 12 months. 151 patients were included (112 men) with a mean age of 67.4 (11.9) years. In the 1st year after stroke, 38 patients (25.2%) had an epileptic seizure. During hospitalization, 27 patients (17.9%) had epileptiform activity (interictal or ictal) in the EEG, 7 (25.9%) of them electrographic seizures. During the first week after stroke, 22 (14.6%) patients had a seizure and 4 (2.6%) non-convulsive status epilepticus criteria. Five (22.7%) acute symptomatic seizures were exclusively electrographic. At least one remote symptomatic seizure occurred in 23 (16%) patients. In the first 7 days after stroke, more than one-fifth of patients with seizures had exclusively electrographic seizures. Without a systematic neurophysiological evaluation the frequency of post-stroke seizures are clinically underestimated.

  4. Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies.

    Science.gov (United States)

    Bidwell, Jonathan; Khuwatsamrit, Thanin; Askew, Brittain; Ehrenberg, Joshua Andrew; Helmers, Sandra

    2015-11-01

    This review surveys current seizure detection and classification technologies as they relate to aiding clinical decision-making during epilepsy treatment. Interviews and data collected from neurologists and a literature review highlighted a strong need for better distinguishing between patients exhibiting generalized and partial seizure types as well as achieving more accurate seizure counts. This information is critical for enabling neurologists to select the correct class of antiepileptic drugs (AED) for their patients and evaluating AED efficiency during long-term treatment. In our questionnaire, 100% of neurologists reported they would like to have video from patients prior to selecting an AED during an initial consultation. Presently, only 30% have access to video. In our technology review we identified that only a subset of available technologies surpassed patient self-reporting performance due to high false positive rates. Inertial seizure detection devices coupled with video capture for recording seizures at night could stand to address collecting seizure counts that are more accurate than current patient self-reporting during day and night time use. Copyright © 2015. Published by Elsevier Ltd.

  5. Vibration Based Sun Gear Damage Detection

    Science.gov (United States)

    Hood, Adrian; LaBerge, Kelsen; Lewicki, David; Pines, Darryll

    2013-01-01

    Seeded fault experiments were conducted on the planetary stage of an OH-58C helicopter transmission. Two vibration based methods are discussed that isolate the dynamics of the sun gear from that of the planet gears, bearings, input spiral bevel stage, and other components in and around the gearbox. Three damaged sun gears: two spalled and one cracked, serve as the focus of this current work. A non-sequential vibration separation algorithm was developed and the resulting signals analyzed. The second method uses only the time synchronously averaged data but takes advantage of the signal/source mapping required for vibration separation. Both algorithms were successful in identifying the spall damage. Sun gear damage was confirmed by the presence of sun mesh groups. The sun tooth crack condition was inconclusive.

  6. Management of Wolff-Parkinson-White Tachyarrhythmia Presenting as Syncope with Seizure-like Activity

    Directory of Open Access Journals (Sweden)

    Samuel Kaplan

    2017-09-01

    Full Text Available Audience: Emergency Medicine residents and medical students. Introduction: An estimated 3% of the United States population suffers from recurrent convulsive episodes that are most often attributed to primary epileptic seizures.1 However, recent studies have estimated about 20%-30% of such episodes are associated with occult cardiac etiology,2 which carry one-year mortality rates of up to 30%.3 Cardiogenic cerebral hypoxia has been associated with a wide variety of neurologic disturbances, including dizzy spells, headache, syncope, focal motor deficit, generalized tonic-clonic seizure, confusion, dementia, and psychosis.4 Convulsive activity has tentatively been ascribed to the ensuing activation of the medullary reticular formation.5,6 This scenario is based on a patient that presented to University of California Irvine Medical Center Emergency Department in April 2017 who, following witnessed seizure-like episodes, was diagnosed with underlying Wolff-Parkinson-White (WPW disorder. WPW is a congenital condition involving aberrantly conductive cardiac tissue between the atria and the ventricles that provides a pathway for a reentrant tachycardia circuit and ventricular pre-excitation.7 Diagnosis is primarily based on the presence of a short PR interval and delta waves on electrocardiography.8 While definitive treatment is catheter-based radiofrequency ablation of the accessory pathway, the hallmark of acute management is vagal maneuvers and antiarrhythmic drugs in the symptomatic but hemodynamically stable patient, and synchronized cardioversion in the unstable patient.9 WPW is thought to affect between 0.1% and 0.3% of the population, and while the usual clinical course is benign, sudden cardiac death occurs in about 3%-4% of such patients.7,10 One survey found 19% of patients with WPW had a history of syncopal episodes;11 however, precise prevalence surveys of WPW-associated seizure-like episodes are lacking in the current literature. This case

  7. Ensemble of classifiers based network intrusion detection system performance bound

    CSIR Research Space (South Africa)

    Mkuzangwe, Nenekazi NP

    2017-11-01

    Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...

  8. Adaptive, Model-Based Monitoring and Threat Detection

    National Research Council Canada - National Science Library

    Valdes, Alfonso

    2002-01-01

    .... We describe a network intrusion detection system (IDS) using Bayes inference, wherein the knowledge base is encoded not as rules but as conditional probability relations between observables and hypotheses of normal and malicious usage...

  9. Algorithms for Speeding up Distance-Based Outlier Detection

    Data.gov (United States)

    National Aeronautics and Space Administration — The problem of distance-based outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address...

  10. Functional MRI-based lie detection: scientific and societal challenges.

    Science.gov (United States)

    Farah, Martha J; Hutchinson, J Benjamin; Phelps, Elizabeth A; Wagner, Anthony D

    2014-02-01

    Functional MRI (fMRI)-based lie detection has been marketed as a tool for enhancing personnel selection, strengthening national security and protecting personal reputations, and at least three US courts have been asked to admit the results of lie detection scans as evidence during trials. How well does fMRI-based lie detection perform, and how should the courts, and society more generally, respond? Here, we address various questions — some of which are based on a meta-analysis of published studies — concerning the scientific state of the art in fMRI-based lie detection and its legal status, and discuss broader ethical and societal implications. We close with three general policy recommendations.

  11. Knowledge-Base Application to Ground Moving Target Detection

    National Research Council Canada - National Science Library

    Adve, R

    2001-01-01

    This report summarizes a multi-year in-house effort to apply knowledge-base control techniques and advanced Space-Time Adaptive Processing algorithms to improve detection performance and false alarm...

  12. Intermittent prophylaxis of recurrent febrile seizures with clobazam versus diazepam.

    Science.gov (United States)

    Sattar, S; Saha, S K; Parveen, F; Banu, L A; Momen, A; Ahmed, A U; Quddush, M R; Karim, M M; Begum, S A; Haque, M A; Hoque, M R

    2014-10-01

    Febrile seizures are the most common type of seizure among children that can be prevented by using prophylactic drugs like Clobazam and Diazepam. The present prospective study was conducted in the Department of Pediatrics, Mymensingh Medical College Hospital and Community Based Medical College Hospital, Bangladesh over a period of 1 year from July 2012 to June 2013 to compare the effectiveness of intermittent Clobazam versus Diazepam therapy in preventing the recurrence of febrile seizures and assessed adverse effects of each drug. A total of 65 patients (32 children administered Clobazam and rest 33 children received Diazepam) of simple and complex febrile seizures aged 6 months to 5 years of both sexes were the study population. Data were collected by interview of the patients, clinical examination and laboratory investigations using the research instrument. Data were analyzed by using Chi-square (χ2) Test, Student's 't' Test and Fisher's Exact Test. For all analytical tests, the level of significance was set at 0.05 and pDiazepam groups. Over 31% of patients in Clobazam group who experienced episode of fever within 3 months, 40.6% within 6 months and 9.4% within 9 months compared to 36.4% in Diazepam group within 3 months, 45.5% within 6 months & 12.1% within 9 months after discharge from the hospital. Three (9.4%) patients in Clobazam group and 7(21.3%) in Diazepam group who experienced febrile convulsion during the follow up period. From the data adverse effects within 3 and 6 months experienced by the patient's drowsiness, sedation and ataxia were higher in Diazepam group than those in Clobazam group. However, within 9 months lethargy and irritability were somewhat higher in Clobazam group than those in Diazepam group. The mean duration of hospitalization was significantly higher in Diazepam group compared to Clobazam group (6.0±1.0 vs. 4.6±0.08 days, PDiazepam group had a history of recurrent seizures, whereas 3(9.4%) of 32 children in the Clobazam group

  13. Vehicle Detection Based on Probability Hypothesis Density Filter

    Directory of Open Access Journals (Sweden)

    Feihu Zhang

    2016-04-01

    Full Text Available In the past decade, the developments of vehicle detection have been significantly improved. By utilizing cameras, vehicles can be detected in the Regions of Interest (ROI in complex environments. However, vision techniques often suffer from false positives and limited field of view. In this paper, a LiDAR based vehicle detection approach is proposed by using the Probability Hypothesis Density (PHD filter. The proposed approach consists of two phases: the hypothesis generation phase to detect potential objects and the hypothesis verification phase to classify objects. The performance of the proposed approach is evaluated in complex scenarios, compared with the state-of-the-art.

  14. USING MORPHLET-BASED IMAGE REPRESENTATION FOR OBJECT DETECTION

    Directory of Open Access Journals (Sweden)

    V. S. Gorbatsevich

    2016-06-01

    Full Text Available In this paper, we propose an original method for objects detection based on a special tree-structured image representation – the trees of morphlets. The method provides robust detection of various types of objects in an image without employing a machine learning procedure. Along with a bounding box creation on a detection step, the method makes pre-segmentation, which can be further used for recognition purposes. Another important feature of the proposed approach is that there are no needs to use a running window as well as a features pyramid in order to detect the objects of different sizes.

  15. Controlling mechanism of absence seizures by deep brain stimulus applied on subthalamic nucleus.

    Science.gov (United States)

    Hu, Bing; Guo, Yu; Zou, Xiaoqiang; Dong, Jing; Pan, Long; Yu, Min; Yang, Zhejia; Zhou, Chaowei; Cheng, Zhang; Tang, Wanyue; Sun, Haochen

    2018-02-01

    Based on a classical model of the basal ganglia thalamocortical network, in this paper, we employed a type of the deep brain stimulus voltage on the subthalamic nucleus to study the control mechanism of absence epilepsy seizures. We found that the seizure can be well controlled by turning the period and the duration of current stimulation into suitable ranges. It is the very interesting bidirectional periodic adjustment phenomenon. These parameters are easily regulated in clinical practice, therefore, the results obtained in this paper may further help us to understand the treatment mechanism of the epilepsy seizure.

  16. Upconversion based continuous-wave mid-infrared detection

    DEFF Research Database (Denmark)

    Tidemand-Lichtenberg, Peter; Dam, Jeppe Seidelin; Pedersen, Christian

    2013-01-01

    We present theoretical and experimental work on upconversion based mid-wavelength infrared detection using silicon detectors without the need for cryogenic cooling. We consider both multi-spectral imaging and point spectroscopy targeting several specific applications.......We present theoretical and experimental work on upconversion based mid-wavelength infrared detection using silicon detectors without the need for cryogenic cooling. We consider both multi-spectral imaging and point spectroscopy targeting several specific applications....

  17. Voltage Sag Source Location Based on Instantaneous Energy Detection

    DEFF Research Database (Denmark)

    Chen, Zhe; Kong, Wei; Dong, Xinzhou

    2008-01-01

    Voltage sag is a major power quality problem, which could disrupt the operation of voltage-sensitive equipment. This paper presents the method based on variation components-based instantaneous energy for voltage sag source detection. Simulations have been performed to provide the thorough analysis...... for system with distributed generation units. The studies show that the presented method can effectively detect the location of voltage sag source....

  18. Machine Learning Based Classifier for Falsehood Detection

    Science.gov (United States)

    Mallikarjun, H. M.; Manimegalai, P., Dr.; Suresh, H. N., Dr.

    2017-08-01

    The investigation of physiological techniques for Falsehood identification tests utilizing the enthusiastic aggravations started as a part of mid 1900s. The need of Falsehood recognition has been a piece of our general public from hundreds of years back. Different requirements drifted over the general public raising the need to create trick evidence philosophies for Falsehood identification. The established similar addressing tests have been having a tendency to gather uncertain results against which new hearty strategies are being explored upon for acquiring more productive Falsehood discovery set up. Electroencephalography (EEG) is a non-obtrusive strategy to quantify the action of mind through the anodes appended to the scalp of a subject. Electroencephalogram is a record of the electric signs produced by the synchronous activity of mind cells over a timeframe. The fundamental goal is to accumulate and distinguish the important information through this action which can be acclimatized for giving surmising to Falsehood discovery in future analysis. This work proposes a strategy for Falsehood discovery utilizing EEG database recorded on irregular people of various age gatherings and social organizations. The factual investigation is directed utilizing MATLAB v-14. It is a superior dialect for specialized registering which spares a considerable measure of time with streamlined investigation systems. In this work center is made on Falsehood Classification by Support Vector Machine (SVM). 72 Samples are set up by making inquiries from standard poll with a Wright and wrong replies in a diverse era from the individual in wearable head unit. 52 samples are trained and 20 are tested. By utilizing Bluetooth based Neurosky’s Mindwave kit, brain waves are recorded and qualities are arranged appropriately. In this work confusion matrix is derived by matlab programs and accuracy of 56.25 % is achieved.

  19. Randomized, controlled trial of ibuprofen syrup administered during febrile illnesses to prevent febrile seizure recurrences

    NARCIS (Netherlands)

    M. van Stuijvenberg (Margriet); G. Derksen-Lubsen (Gerarda); E.W. Steyerberg (Ewout); J.D.F. Habbema (Dik); H.A. Moll (Henriëtte)

    1998-01-01

    textabstractOBJECTIVES: Febrile seizures recur frequently. Factors increasing the risk of febrile seizure recurrence include young age at onset, family history of febrile seizures, previous recurrent febrile seizures, time lapse since previous seizure <6 months,

  20. [Detecting fire smoke based on the multispectral image].

    Science.gov (United States)

    Wei, Ying-Zhuo; Zhang, Shao-Wu; Liu, Yan-Wei

    2010-04-01

    Smoke detection is very important for preventing forest-fire in the fire early process. Because the traditional technologies based on video and image processing are easily affected by the background dynamic information, three limitations exist in these technologies, i. e. lower anti-interference ability, higher false detection rate and the fire smoke and water fog being not easily distinguished. A novel detection method for detecting smoke based on the multispectral image was proposed in the present paper. Using the multispectral digital imaging technique, the multispectral image series of fire smoke and water fog were obtained in the band scope of 400 to 720 nm, and the images were divided into bins. The Euclidian distance among the bins was taken as a measurement for showing the difference of spectrogram. After obtaining the spectral feature vectors of dynamic region, the regions of fire smoke and water fog were extracted according to the spectrogram feature difference between target and background. The indoor and outdoor experiments show that the smoke detection method based on multispectral image can be applied to the smoke detection, which can effectively distinguish the fire smoke and water fog. Combined with video image processing method, the multispectral image detection method can also be applied to the forest fire surveillance, reducing the false alarm rate in forest fire detection.

  1. An Improved Wavelet‐Based Multivariable Fault Detection Scheme

    KAUST Repository

    Harrou, Fouzi

    2017-07-06

    Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)‐based Q and EWMA methods and MSPLS‐based Q method.

  2. Transistor-based particle detection systems and methods

    Science.gov (United States)

    Jain, Ankit; Nair, Pradeep R.; Alam, Muhammad Ashraful

    2015-06-09

    Transistor-based particle detection systems and methods may be configured to detect charged and non-charged particles. Such systems may include a supporting structure contacting a gate of a transistor and separating the gate from a dielectric of the transistor, and the transistor may have a near pull-in bias and a sub-threshold region bias to facilitate particle detection. The transistor may be configured to change current flow through the transistor in response to a change in stiffness of the gate caused by securing of a particle to the gate, and the transistor-based particle detection system may configured to detect the non-charged particle at least from the change in current flow.

  3. Region duplication forgery detection technique based on SURF and HAC.

    Science.gov (United States)

    Mishra, Parul; Mishra, Nishchol; Sharma, Sanjeev; Patel, Ravindra

    2013-01-01

    Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC). SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions.

  4. DDoS Attack Detection Algorithms Based on Entropy Computing

    Science.gov (United States)

    Li, Liying; Zhou, Jianying; Xiao, Ning

    Distributed Denial of Service (DDoS) attack poses a severe threat to the Internet. It is difficult to find the exact signature of attacking. Moreover, it is hard to distinguish the difference of an unusual high volume of traffic which is caused by the attack or occurs when a huge number of users occasionally access the target machine at the same time. The entropy detection method is an effective method to detect the DDoS attack. It is mainly used to calculate the distribution randomness of some attributes in the network packets' headers. In this paper, we focus on the detection technology of DDoS attack. We improve the previous entropy detection algorithm, and propose two enhanced detection methods based on cumulative entropy and time, respectively. Experiment results show that these methods could lead to more accurate and effective DDoS detection.

  5. Laser-based instrumentation for the detection of chemical agents

    International Nuclear Information System (INIS)

    Hartford, A. Jr.; Sander, R.K.; Quigley, G.P.; Radziemski, L.J.; Cremers, D.A.

    1982-01-01

    Several laser-based techniques are being evaluated for the remote, point, and surface detection of chemical agents. Among the methods under investigation are optoacoustic spectroscopy, laser-induced breakdown spectroscopy (LIBS), and synchronous detection of laser-induced fluorescence (SDLIF). Optoacoustic detection has already been shown to be capable of extremely sensitive point detection. Its application to remote sensing of chemical agents is currently being evaluated. Atomic emission from the region of a laser-generated plasma has been used to identify the characteristic elements contained in nerve (P and F) and blister (S and Cl) agents. Employing this LIBS approach, detection of chemical agent simulants dispersed in air and adsorbed on a variety of surfaces has been achieved. Synchronous detection of laser-induced fluorescence provides an attractive alternative to conventional LIF, in that an artificial narrowing of the fluorescence emission is obtained. The application of this technique to chemical agent simulants has been successfully demonstrated. 19 figures

  6. Image Fakery Detection Based on Singular Value Decomposition

    Directory of Open Access Journals (Sweden)

    T. Basaruddin

    2009-11-01

    Full Text Available The growing of image processing technology nowadays make it easier for user to modify and fake the images. Image fakery is a process to manipulate part or whole areas of image either in it content or context with the help of digital image processing techniques. Image fakery is barely unrecognizable because the fake image is looking so natural. Yet by using the numerical computation technique it is able to detect the evidence of fake image. This research is successfully applied the singular value decomposition method to detect image fakery. The image preprocessing algorithm prior to the detection process yields two vectors orthogonal to the singular value vector which are important to detect fake image. The result of experiment to images in several conditions successfully detects the fake images with threshold value 0.2. Singular value decomposition-based detection of image fakery can be used to investigate fake image modified from original image accurately.

  7. Region Duplication Forgery Detection Technique Based on SURF and HAC

    Directory of Open Access Journals (Sweden)

    Parul Mishra

    2013-01-01

    Full Text Available Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF and hierarchical agglomerative clustering (HAC. SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions.

  8. An Incremental Support Vector Machine based Speech Activity Detection Algorithm.

    Science.gov (United States)

    Xianbo, Xiao; Guangshu, Hu

    2005-01-01

    Traditional voice activity detection algorithms are mostly threshold-based or statistical model-based. All those methods are absent of the ability to react quickly to variations of environments. This paper describes an incremental SVM (Support Vector Machine) method for speech activity detection. The proposed incremental procedure makes it adaptive to variation of environments and the special construction of incremental training data set decreases computing consumption effectively. Experiments results demonstrated its higher end point detection accuracy. Further work will be focused on decreasing computing consumption and importing multi-class SVM classifiers.

  9. Computer-based instrumentation for partial discharge detection in GIS

    International Nuclear Information System (INIS)

    Md Enamul Haque; Ahmad Darus; Yaacob, M.M.; Halil Hussain; Feroz Ahmed

    2000-01-01

    Partial discharge is one of the prominent indicators of defects and insulation degradation in a Gas Insulated Switchgear (GIS). Partial discharges (PD) have a harmful effect on the life of insulation of high voltage equipment. The PD detection using acoustic technique and subsequent analysis is currently an efficient method of performing non-destructive testing of GIS apparatus. A low cost PC-based acoustic PD detection instrument has been developed for the non-destructive diagnosis of GIS. This paper describes the development of a PC-based instrumentation system for partial discharge detection in GIS and some experimental results have also presented. (Author)

  10. A novel line segment detection algorithm based on graph search

    Science.gov (United States)

    Zhao, Hong-dan; Liu, Guo-ying; Song, Xu

    2018-02-01

    To overcome the problem of extracting line segment from an image, a method of line segment detection was proposed based on the graph search algorithm. After obtaining the edge detection result of the image, the candidate straight line segments are obtained in four directions. For the candidate straight line segments, their adjacency relationships are depicted by a graph model, based on which the depth-first search algorithm is employed to determine how many adjacent line segments need to be merged. Finally we use the least squares method to fit the detected straight lines. The comparative experimental results verify that the proposed algorithm has achieved better results than the line segment detector (LSD).

  11. Antiepileptic drugs as prophylaxis for post-craniotomy seizures.

    Science.gov (United States)

    Weston, Jennifer; Greenhalgh, Janette; Marson, Anthony G

    2015-03-04

    meta-analysis; we presented the findings of the review in narrative format. We included eight RCTs (N = 1602), which were published between 1983 and 2013. Three trials compared a single AED (phenytoin) with a placebo or no treatment. One three-arm trial compared two AEDs (carbamazepine, phenytoin) with no treatment. A second three-arm trial compared phenytoin, phenobarbital and no treatment. Three other trials were head-to-head trials of AEDs (phenytoin vs. valproate; zonisamide vs. phenobarbital) and levetiracetam vs. phenytoin. Of the five trials comparing AEDs with controls, only one trial reported a significant difference between AED treatment and controls for early seizure occurrence. All other comparisons were non-significant. Of the head-to-head trials, none reported statistically significant differences between treatments for either early or late seizures. One head-to-head trial showed an increase in the number of deaths following one AED treatment compared to another AED treatment. Incidences of adverse effects of treatment were poorly reported, and the most trials reported no significant differences between treatment groups. However data on adverse events were limited. There is little evidence to suggest that AED treatment administered prophylactically is effective or not effective in preventing post-craniotomy seizures. The current evidence base is limited due to the differing methodologies employed in the trials and inconsistencies in reporting of outcomes. Further evidence from good-quality, contemporary trials is required in order to assess the effectiveness of prophylactic AED treatment compared to control groups or other AEDs in preventing post-craniotomy seizures properly.

  12. Epileptic seizure, as the first symptom of hypoparathyroidism in children, does not require antiepileptic drugs.

    Science.gov (United States)

    Liu, Meng-Jia; Li, Jiu-Wei; Shi, Xiu-Yu; Hu, Lin-Yan; Zou, Li-Ping

    2017-02-01

    Patients with hypoparathyroidism exhibit metabolic disorders (hypocalcemia) and brain structural abnormalities (brain calcifications). Currently, studies have determined whether antiepileptic drug (AED) treatment is required for epileptic seizures in children with hypoparathyroidism. This study aims to evaluate the data of two medical centers in Beijing based on the diagnosis of epileptic seizures as the first symptom of hypoparathyroidism in children. A total of 42 patients were included and assigned into AED and non-AED treatment groups in a 1:2 matched case-control study. Results show that the seizure outcome after 1 year of AED treatment is not significantly different from that of the control. In the subgroup analysis of patients with subcortical calcifications, the seizure outcome is still not significantly different from that of the control. Thus, AED treatment cannot improve the seizure outcomes in children with parathyroid disorder, even in such cases as suspected structural seizure caused by subcortical calcifications. Clinicians must take adequate considerations on the use of AEDs in these patients. Epileptic seizures, as the first symptom of hypoparathyroidism in children, do not require epilepsy drugs.

  13. The chemical induction of seizures in psychiatric therapy: were flurothyl (indoklon) and pentylenetetrazol (metrazol) abandoned prematurely?

    Science.gov (United States)

    Cooper, Kathryn; Fink, Max

    2014-10-01

    Camphor-induced and pentylenetetrazol-induced brain seizures were first used to relieve psychiatric illnesses in 1934. Electrical inductions (electroconvulsive therapy, ECT) followed in 1938. These were easier and less expensive to administer and quickly became the main treatment method. In 1957, seizure induction with the inhalant anesthetic flurothyl was tested and found to be clinically effective.For many decades, complaints of memory loss have stigmatized and inhibited ECT use. Many variations of electricity in form, electrode placement, dosing, and stimulation method offered some relief, but complaints still limit its use. The experience with chemical inductions of seizures was reviewed based on searches for reports of each agent in Medline and in the archival files of original studies by the early investigators. Camphor injections were inefficient and were rapidly replaced by pentylenetetrazol. These were effective but difficult to administer. Flurothyl inhalation-induced seizures were as clinically effective as electrical inductions with lesser effects on memory functions. Flurothyl inductions were discarded because of the persistence of the ethereal aroma and the fears induced in the professional staff that they might seize. Persistent complaints of memory loss plague electricity induced seizures. Flurothyl induced seizures are clinically as effective without the memory effects associated with electricity. Reexamination of seizure inductions using flurothyl in modern anesthesia facilities is encouraged to relieve medication-resistant patients with mood disorders and catatonia.

  14. Evaluation of the first seizure patient: Key points in the history and physical examination.

    Science.gov (United States)

    Nowacki, Tomasz A; Jirsch, Jeffrey D

    2017-07-01

    This review will present the history and physical examination as the launching point of the first seizure evaluation, from the initial characterization of the event, to the exclusion of alternative diagnoses, and then to the determination of specific acute or remote causes. Clinical features that may distinguish seizures from alternative diagnoses are discussed in detail, followed by a discussion of acute and remote first seizure etiologies. This review article is based on a discretionary selection of English language articles retrieved by a literature search in the PubMed database, and the authors' clinical experience. The first seizure is a dramatic event with often profound implications for patients and family members. The initial clinical evaluation focuses on an accurate description of the spell to confirm the diagnosis, along with careful scrutiny for previously unrecognized seizures that would change the diagnosis more definitively to one of epilepsy. The first seizure evaluation rests primarily on the clinical history, and to a lesser extent, the physical examination. Even in the era of digital EEG recording and neuroimaging, the initial clinical evaluation remains essential for the diagnosis, treatment, and prognostication of the first seizure. Copyright © 2016. Published by Elsevier Ltd.

  15. Classification of clinical semiology in epileptic seizures in neonates.

    Science.gov (United States)

    Nagarajan, Lakshmi; Palumbo, Linda; Ghosh, Soumya

    2012-03-01

    The clinical semiology of 61 neonatal seizures with EEG correlates, in 24 babies was analysed. Most seizures (89%) had multiple features during the EEG discharge. The seizures were classified using the prominent clinical feature at onset, and all features seen during the seizure, using an extended classification scheme. Orolingual features occurred most frequently at onset (30%), whereas ocular phenomena occurred most often during the seizure (70%). Orolingual, ocular and autonomic features were seen at onset in 55% of the seizures. Seizure onsets with clonic, tonic and hypomotor features were seen in 20%, 8% and 18% respectively. Clinico-electrical correlations were as follows. The EEG discharge involved both hemispheres in 54% of all seizures, in clonic seizures this was 93%. Focal clonic seizures were associated with EEG seizure onset from the contralateral hemisphere. Majority of the clonic and hypomotor seizures had a left hemisphere ictal EEG onset. Orolingual seizures frequently started from the right hemisphere, whereas ocular and autonomic seizures arose from either hemisphere. There was no significant difference in mortality, morbidity, abnormal neuroimaging and EEG background abnormalities in babies with or without clonic seizures. This study provides insights into neuronal networks that underpin electroclinical seizures, by analysing and classifying the obvious initial clinical features and those during the seizure. Copyright © 2011 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

  16. Islanding detection scheme based on adaptive identifier signal estimation method.

    Science.gov (United States)

    Bakhshi, M; Noroozian, R; Gharehpetian, G B

    2017-11-01

    This paper proposes a novel, passive-based anti-islanding method for both inverter and synchronous machine-based distributed generation (DG) units. Unfortunately, when the active/reactive power mismatches are near to zero, majority of the passive anti-islanding methods cannot detect the islanding situation, correctly. This study introduces a new islanding detection method based on exponentially damped signal estimation method. The proposed method uses adaptive identifier method for estimating of the frequency deviation of the point of common coupling (PCC) link as a target signal that can detect the islanding condition with near-zero active power imbalance. Main advantage of the adaptive identifier method over other signal estimation methods is its small sampling window. In this paper, the adaptive identifier based islanding detection method introduces a new detection index entitled decision signal by estimating of oscillation frequency of the PCC frequency and can detect islanding conditions, properly. In islanding conditions, oscillations frequency of PCC frequency reach to zero, thus threshold setting for decision signal is not a tedious job. The non-islanding transient events, which can cause a significant deviation in the PCC frequency are considered in simulations. These events include different types of faults, load changes, capacitor bank switching, and motor starting. Further, for islanding events, the capability of the proposed islanding detection method is verified by near-to-zero active power mismatches. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Research of detection depth for graphene-based optical sensor

    Science.gov (United States)

    Yang, Yong; Sun, Jialve; Liu, Lu; Zhu, Siwei; Yuan, Xiaocong

    2018-03-01

    Graphene-based optical sensors have been developed for research into the biological intercellular refractive index (RI) because they offer greater detection depths than those provided by the surface plasmon resonance technique. In this Letter, we propose an experimental approach for measurement of the detection depth in a graphene-based optical sensor system that uses transparent polydimethylsiloxane layers with different thicknesses. The experimental results show that detection depths of 2.5 μm and 3 μm can be achieved at wavelengths of 532 nm and 633 nm, respectively. These results prove that graphene-based optical sensors can realize long-range RI detection and are thus promising for use as tools in the biological cell detection field. Additionally, we analyze the factors that influence the detection depth and provide a feasible approach for detection depth control based on adjustment of the wavelength and the angle of incidence. We believe that this approach will be useful in RI tomography applications.

  18. Methylated flavonoids as anti-seizure agents: Naringenin 4',7-dimethyl ether attenuates epileptic seizures in zebrafish and mouse models.

    Science.gov (United States)

    Copmans, Daniëlle; Orellana-Paucar, Adriana M; Steurs, Gert; Zhang, Yifan; Ny, Annelii; Foubert, Kenn; Exarchou, Vasiliki; Siekierska, Aleksandra; Kim, Youngju; De Borggraeve, Wim; Dehaen, Wim; Pieters, Luc; de Witte, Peter A M

    2018-01-01

    Epilepsy is a neurological disease that affects more than 70 million people worldwide and is characterized by the presence of spontaneous unprovoked recurrent seizures. Existing anti-seizure drugs (ASDs) have side effects and fail to control seizures in 30% of patients due to drug resistance. Hence, safer and more efficacious drugs are sorely needed. Flavonoids are polyphenolic structures naturally present in most plants and consumed daily with no adverse effects reported. These structures have shown activity in several seizure and epilepsy animal models through allosteric modulation of GABA A receptors, but also via potent anti-inflammatory action in the brain. As such, dietary flavonoids offer an interesting source for ASD and anti-epileptogenic drug (AED) discovery, but their pharmaceutical potential is often hampered by metabolic instability and low oral bioavailability. It has been argued that their drug-likeness can be improved via methylation of the free hydroxyl groups, thereby dramatically enhancing metabolic stability and membrane transport, facilitating absorption and highly increasing bioavailability. Since no scientific data is available regarding the use of methylated flavonoids in the fight against epilepsy, we studied naringenin (NRG), kaempferol (KFL), and three methylated derivatives, i.e., naringenin 7-O-methyl ether (NRG-M), naringenin 4',7-dimethyl ether (NRG-DM), and kaempferide (4'-O-methyl kaempferol) (KFD) in the zebrafish pentylenetetrazole (PTZ) seizure model. We demonstrate that the methylated flavanones NRG-DM and NRG-M are highly effective against PTZ-induced seizures in larval zebrafish, whereas NRG and the flavonols KFL and KFD possess only a limited activity. Moreover, we show that NRG-DM is active in two standard acute mouse seizure models, i.e., the timed i.v. PTZ seizure model and the 6-Hz psychomotor seizure model. Based on these results, NRG-DM is proposed as a lead compound that is worth further investigation for the treatment

  19. Smart phone based bacterial detection using bio functionalized fluorescent nanoparticles

    International Nuclear Information System (INIS)

    Rajendran, Vinoth Kumar; Bakthavathsalam, Padmavathy; Ali, Baquir Mohammed Jaffar

    2014-01-01

    We are describing immunochromatographic test strips with smart phone-based fluorescence readout. They are intended for use in the detection of the foodborne bacterial pathogens Salmonella spp. and Escherichia coli O157. Silica nanoparticles (SiNPs) were doped with FITC and Ru(bpy), conjugated to the respective antibodies, and then used in a conventional lateral flow immunoassay (LFIA). Fluorescence was recorded by inserting the nitrocellulose strip into a smart phone-based fluorimeter consisting of a light weight (40 g) optical module containing an LED light source, a fluorescence filter set and a lens attached to the integrated camera of the cell phone in order to acquire high-resolution fluorescence images. The images were analysed by exploiting the quick image processing application of the cell phone and enable the detection of pathogens within few minutes. This LFIA is capable of detecting pathogens in concentrations as low as 10 5 cfu mL −1 directly from test samples without pre-enrichment. The detection is one order of magnitude better compared to gold nanoparticle-based LFIAs under similar condition. The successful combination of fluorescent nanoparticle-based pathogen detection by LFIAs with a smart phone-based detection platform has resulted in a portable device with improved diagnosis features and having potential application in diagnostics and environmental monitoring. (author)

  20. Combining Host-based and network-based intrusion detection system

    African Journals Online (AJOL)

    These attacks were simulated using hping. The proposed system is implemented in Java. The results show that the proposed system is able to detect attacks both from within (host-based) and outside sources (network-based). Key Words: Intrusion Detection System (IDS), Host-based, Network-based, Signature, Security log.

  1. Multicriteria Similarity-Based Anomaly Detection Using Pareto Depth Analysis.

    Science.gov (United States)

    Hsiao, Ko-Jen; Xu, Kevin S; Calder, Jeff; Hero, Alfred O

    2016-06-01

    We consider the problem of identifying patterns in a data set that exhibits anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g., as measured by the nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including nonmetric measures, and one can test for anomalies by scalarizing using a nonnegative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based anomaly detection using a novel multicriteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) anomaly detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria, and shows superior performance on experiments with synthetic and real data sets.

  2. Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks.

    Science.gov (United States)

    Chen, Xiaoguang; Jiang, Xu; Wu, Zhilu; Zhuang, Shufeng

    2017-02-16

    High throughput, low latency and reliable communication has always been a hot topic for wireless sensor networks (WSNs) in various applications. Multiuser detection is widely used to suppress the bad effect of multiple access interference in WSNs. In this paper, a novel multiuser detection method based on differential characteristics is proposed to suppress multiple access interference. The proposed iterative receive method consists of three stages. Firstly, a differential characteristics function is presented based on the optimal multiuser detection decision function; then on the basis of differential characteristics, a preliminary threshold detection is utilized to find the potential wrongly received bits; after that an error bit corrector is employed to correct the wrong bits. In order to further lower the bit error ratio (BER), the differential characteristics calculation, threshold detection and error bit correction process described above are iteratively executed. Simulation results show that after only a few iterations the proposed multiuser detection method can achieve satisfactory BER performance. Besides, BER and near far resistance performance are much better than traditional suboptimal multiuser detection methods. Furthermore, the proposed iterative multiuser detection method also has a large system capacity.

  3. Utility of laboratory studies in seizures of children older than one month of age.

    Science.gov (United States)

    Karbasi, S Akhavan; Mosadegh, M Modares; Fallah, R

    2009-08-01

    Seizure is the most common paediatric neurological disease which occurs in ten percent of children. In approaching a convulsive patient, finding the causes of seizure is essential, and the patient's history as well as the physical examination are important. The role of routine laboratory tests for children's seizures (except neonates) is undetermined, but checking for serum sodium, glucose, calcium and urea routinely has been advised. The purpose of this study was to determine the diagnostic efficacy of these serum chemistry tests in the seizures of children older than one month of age. In this descriptive, retrospective study, medical records of 302 hospitalised children with seizure were reviewed. Results of laboratory tests, like sodium, calcium, blood glucose and urea levels, pertinent history and physical examination, and the change in patient management based on serum chemistry test results, were analysed. All the children in the study were classified as having seizure with or without fever. In 302 hospitalised children with seizure, about ten percent of 938 tests were abnormal. 27.7 percent of these abnormal results were seen in 1-12-month-old infants. Only 11 percent of abnormal tests (1.3 percent of total tests) might have caused a seizure. Also, 0.2 percent of the results could not be predicted from the history or physical examination, which was conducted in patients younger than one year of age. Routine determination of serum chemistry values in seizures of children does not contribute to therapy, and are costly and time-consuming. It may not be helpful and informative unless the patient is less than one year of age.

  4. Is there a Relation between Food Intake and Epileptic Seizures in Children?

    Directory of Open Access Journals (Sweden)

    R Fallah

    2011-04-01

    Full Text Available Introduction: Seizure is one of the most common pediatric neurology problems. The purpose of this study was to evaluate effects of different kinds of food on seizures of epileptic children based on their mothers attitude and experience. Methods: In a descriptive- analytic study done at the pediatric neurology clinic of Shahid Sadoughi University, attitude and experience of mothers regarding the effect of different kinds of foods on children seizures was evaluated via a questionnaire. Results: A total of 148 mothers with ages ranging between 17-52 years (mean± SD:31.6± 6.6 years were evaluated. Their children were 58.5% boys and 41.5% girls with age range of 1-19 years (mean± SD: 6.2±3.8 years. Eighty percent of mothers believed that different kinds of food affected the seizures and this belief was not related to their educational level, gender of child or state of seizure control. The most common kinds of food which triggered seizures based on mothers attitude were salty food and pickled vegetables and based on experience, they were cucumber and milk and . The most common kinds of food that caused decreasing in seizures frequency based on mothers attitude were coffee and cattle oil and based on experience, they were honey and sugar. Conclusion: In this study, majority of mothers believe that different kinds of food have an effect on the seizures in their children and it is necessary to educate epileptic patients about their diet after extraction and testing of effective materials of different kinds of food in animals and human models via further researches.

  5. A Portable Pesticide Residues Detection Instrument Based on Impedance Immunosensor

    Directory of Open Access Journals (Sweden)

    Jiang Ding

    2014-06-01

    Full Text Available In this paper, a design of portable pesticide residues detection instrument was presented based on an impedance immunosensor. The immunosensor exploited the novel multilayer films based on Au nanoparticles (AuNPs and polyaniline/carboxylated multiwall carbon nanotubes- chitosan nanocomposite (PANI/MWCNTs/CS. The detection principle of the instrument was based on the electrochemical characteristic of antigen-specific antibody immune response. With a stronger signal generated from the antigen-specific antibody immune response, the signal detection circuit was designed more easily. We integrated immunosensor and signal detection circuit to fabricate pesticide residues detection instrument. This proposed instrument could realize the rapid detection of pesticide residues in fruits and vegetables with automatic data processing and presented the result on the spot. The impedance test error was less than 5 %. The results showed that the proposed instrument had a good consistence compared with the traditional analytical methods. Thus, it would be a promising rapid detection instrument for pesticide residues in agricultural products.

  6. Frontal gray matter abnormalities predict seizure outcome in refractory temporal lobe epilepsy patients.

    Science.gov (United States)

    Doucet, Gaelle E; He, Xiaosong; Sperling, Michael; Sharan, Ashwini; Tracy, Joseph I

    2015-01-01

    Developing more reliable predictors of seizure outcome following temporal lobe surgery for intractable epilepsy is an important clinical goal. In this context, we investigated patients with refractory temporal lobe epilepsy (TLE) before and after temporal resection. In detail, we explored gray matter (GM) volume change in relation with seizure outcome, using a voxel-based morphometry (VBM) approach. To do so, this study was divided into two parts. The first one involved group analysis of differences in regional GM volume between the groups (good outcome (GO), e.g., no seizures after surgery; poor outcome (PO), e.g., persistent postoperative seizures; and controls, N = 24 in each group), pre- and post-surgery. The second part of the study focused on pre-surgical data only (N = 61), determining whether the degree of GM abnormalities can predict surgical outcomes. For this second step, GM abnormalities were identified, within each lobe, in each patient when compared with an ad hoc sample of age-matched controls. For the first analysis, the results showed larger GM atrophy, mostly in the frontal lobe, in PO patients, relative to both GO patients and controls, pre-surgery. When comparing pre-to-post changes, we found relative GM gains in the GO but not in the PO patients, mostly in the non-resected hemisphere. For the second analysis, only the frontal lobe displayed reliable prediction of seizure outcome. 81% of the patients showing pre-surgical increased GM volume in the frontal lobe became seizure free, post-surgery; while 77% of the patients with pre-surgical reduced frontal GM volume had refractory seizures, post-surgery. A regression analysis revealed that the proportion of voxels with reduced frontal GM volume was a significant predictor of seizure outcome (p = 0.014). Importantly, having less than 1% of the frontal voxels with GM atrophy increased the likelihood of being seizure-free, post-surgery, by seven times. Overall, our results suggest that using pre

  7. Biotelemetry system for Epilepsy Seizure Control

    Energy Technology Data Exchange (ETDEWEB)

    Smith, LaCurtise; Bohnert, George W.

    2009-07-02

    The Biotelemetry System for Epilepsy Seizure Control Project developed and tested an automated telemetry system for use in an epileptic seizure prevention device that precisely controls localized brain temperature. This project was a result of a Department of Energy (DOE) Global Initiatives for Proliferation Prevention (GIPP) grant to the Kansas City Plant (KCP), Argonne National Laboratory (ANL), and Pacific Northwest National Laboratory (PNNL) to partner with Flint Hills Scientific, LLC, Lawrence, KS and Biophysical Laboratory Ltd (BIOFIL), Sarov, Russia to develop a method to help control epileptic seizures.

  8. Distributed Iterative Multiuser Detection through Base Station Cooperation

    Directory of Open Access Journals (Sweden)

    Shahid Khattak

    2008-08-01

    Full Text Available This paper deals with multiuser detection through base station cooperation in an uplink, interference-limited, high frequency reuse scenario. Distributed iterative detection (DID is an interference mitigation technique in which the base stations at different geographical locations exchange detected data iteratively while performing separate detection and decoding of their received data streams. This paper explores possible DID receive strategies and proposes to exchange between base stations only the processed information for their associated mobile terminals. The resulting backhaul traffic is considerably lower than that of existing cooperative multiuser detection strategies. Single-antenna interference cancellation techniques are employed to generate local estimates of the dominant interferers at each base station, which are then combined with their independent received copies from other base stations, resulting in more effective interference suppression. Since hard information bits or quantized log-likelihood ratios (LLRs are transferred, we investigate the effect of quantization of the LLR values with the objective of further reducing the backhaul traffic. Our findings show that schemes based on nonuniform quantization of the “soft bits” allow for reducing the backhaul to 1–2 exchanged bits/coded bit.

  9. Distributed Iterative Multiuser Detection through Base Station Cooperation

    Directory of Open Access Journals (Sweden)

    Khattak Shahid

    2008-01-01

    Full Text Available Abstract This paper deals with multiuser detection through base station cooperation in an uplink, interference-limited, high frequency reuse scenario. Distributed iterative detection (DID is an interference mitigation technique in which the base stations at different geographical locations exchange detected data iteratively while performing separate detection and decoding of their received data streams. This paper explores possible DID receive strategies and proposes to exchange between base stations only the processed information for their associated mobile terminals. The resulting backhaul traffic is considerably lower than that of existing cooperative multiuser detection strategies. Single-antenna interference cancellation techniques are employed to generate local estimates of the dominant interferers at each base station, which are then combined with their independent received copies from other base stations, resulting in more effective interference suppression. Since hard information bits or quantized log-likelihood ratios (LLRs are transferred, we investigate the effect of quantization of the LLR values with the objective of further reducing the backhaul traffic. Our findings show that schemes based on nonuniform quantization of the "soft bits" allow for reducing the backhaul to 1–2 exchanged bits/coded bit.

  10. Home Camera-Based Fall Detection System for the Elderly

    Directory of Open Access Journals (Sweden)

    Koldo de Miguel

    2017-12-01

    Full Text Available Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

  11. Home Camera-Based Fall Detection System for the Elderly.

    Science.gov (United States)

    de Miguel, Koldo; Brunete, Alberto; Hernando, Miguel; Gambao, Ernesto

    2017-12-09

    Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

  12. A vision based row detection system for sugar beet

    NARCIS (Netherlands)

    Bakker, T.; Wouters, H.; Asselt, van C.J.; Bontsema, J.; Tang, L.; Müller, J.; Straten, van G.

    2008-01-01

    One way of guiding autonomous vehicles through the field is using a vision based row detection system. A new approach for row recognition is presented which is based on grey-scale Hough transform on intelligently merged images resulting in a considerable improvement of the speed of image processing.

  13. A Labeled Data Set For Flow-based Intrusion Detection

    NARCIS (Netherlands)

    Sperotto, Anna; Sadre, R.; van Vliet, Frank; Pras, Aiko; Nunzi, Giorgio; Scoglio, Caterina; Li, Xing

    2009-01-01

    Flow-based intrusion detection has recently become a promising security mechanism in high speed networks (1-10 Gbps). Despite the richness in contributions in this field, benchmarking of flow-based IDS is still an open issue. In this paper, we propose the first publicly available, labeled data set

  14. Dust storm detection using random forests and physical-based ...

    Indian Academy of Sciences (India)

    This paper investigates the capability of two physical-based methods, and random forests (RF) classifier, for the first time, to detect dust storms using MODIS imagery. Since the physical-based approaches are empirical, they suffer from certain drawbacks such as high variability of thresholds depending on the underlying ...

  15. Smartphone-based low light detection for bioluminescence application

    Science.gov (United States)

    We report a smartphone-based device and associated imaging-processing algorithm to maximize the sensitivity of standard smartphone cameras, that can detect the presence of single-digit pW of radiant flux intensity. The proposed hardware and software, called bioluminescent-based analyte quantitation ...

  16. RANDOM FOREST BASED MISFIRE DETECTION USING KONONENKO DISCRETISER

    Directory of Open Access Journals (Sweden)

    S. Babu Devasenapati

    2012-01-01

    Full Text Available This paper evaluates the use of random forest (RF as a tool for misfire detection using statistical features. The engine block vibration contains hidden information about the events occurring inside the engine. Misfire detection was achieved by processing the vibration signals acquired from the engine using a piezoelectric accelerometer. The hidden information regarding misfire was decoded using feature extraction techniques. The effect of Kononenko based discretiser as feature size reduction tool and Correlation-based Feature Selection (CFS based feature subset selection is analysed for performance improvement in the RF model. The random forest based model is found to have a consistent high classification accuracy of around 90% when designed as a multi class ,ode and reaches 100% when the conditions are clubbed to simulate a two-class mode . From the results obtained the authors conclude that the combination of statistical features and RF algorithm is well suited for detection of misfire in spark ignition engines.

  17. An Android malware detection system based on machine learning

    Science.gov (United States)

    Wen, Long; Yu, Haiyang

    2017-08-01

    The Android smartphone, with its open source character and excellent performance, has attracted many users. However, the convenience of the Android platform also has motivated the development of malware. The traditional method which detects the malware based on the signature is unable to detect unknown applications. The article proposes a machine learning-based lightweight system that is capable of identifying malware on Android devices. In this system we extract features based on the static analysis and the dynamitic analysis, then a new feature selection approach based on principle component analysis (PCA) and relief are presented in the article to decrease the dimensions of the features. After that, a model will be constructed with support vector machine (SVM) for classification. Experimental results show that our system provides an effective method in Android malware detection.

  18. Robust facial landmark detection based on initializing multiple poses

    Directory of Open Access Journals (Sweden)

    Xin Chai

    2016-10-01

    Full Text Available For robot systems, robust facial landmark detection is the first and critical step for face-based human identification and facial expression recognition. In recent years, the cascaded-regression-based method has achieved excellent performance in facial landmark detection. Nevertheless, it still has certain weakness, such as high sensitivity to the initialization. To address this problem, regression based on multiple initializations is established in a unified model; face shapes are then estimated independently according to these initializations. With a ranking strategy, the best estimate is selected as the final output. Moreover, a face shape model based on restricted Boltzmann machines is built as a constraint to improve the robustness of ranking. Experiments on three challenging datasets demonstrate the effectiveness of the proposed facial landmark detection method against state-of-the-art methods.

  19. A measurement-based technique for incipient anomaly detection

    KAUST Repository

    Harrou, Fouzi

    2016-06-13

    Fault detection is essential for safe operation of various engineering systems. Principal component analysis (PCA) has been widely used in monitoring highly correlated process variables. Conventional PCA-based methods, nevertheless, often fail to detect small or incipient faults. In this paper, we develop new PCA-based monitoring charts, combining PCA with multivariate memory control charts, such as the multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) monitoring schemes. The multivariate control charts with memory are sensitive to small and moderate faults in the process mean, which significantly improves the performance of PCA methods and widen their applicability in practice. Using simulated data, we demonstrate that the proposed PCA-based MEWMA and MCUSUM control charts are more effective in detecting small shifts in the mean of the multivariate process variables, and outperform the conventional PCA-based monitoring charts. © 2015 IEEE.

  20. Seizure variables and cognitive performance in patients with epilepsy

    African Journals Online (AJOL)

    There are scanty reports on the contributions of seizure variables like seizure types, frequency of seizures, duration of epilepsy, age at onset and anti-epileptic drugs to cognitive disturbances in Nigerian Africans. This study assessed the effects of seizure variables on the cognitive performances of patients with epilepsy.

  1. Seizure semiology identifies patients with bilateral temporal lobe epilepsy.

    Science.gov (United States)

    Loesch, Anna Mira; Feddersen, Berend; Tezer, F Irsel; Hartl, Elisabeth; Rémi, Jan; Vollmar, Christian; Noachtar, Soheyl

    2015-01-01

    Laterality in temporal lobe epilepsy is usually defined by EEG and imaging results. We investigated whether the analysis of seizure semiology including lateralizing seizure phenomena identifies bilateral independent temporal lobe seizure onset. We investigated the seizure semiology in 17 patients in whom invasive EEG-video-monitoring documented bilateral temporal seizure onset. The results were compared to 20 left and 20 right consecutive temporal lobe epilepsy (TLE) patients who were seizure free after anterior temporal lobe resection. The seizure semiology was analyzed using the semiological seizure classification with particular emphasis on the sequence of seizure phenomena over time and lateralizing seizure phenomena. Statistical analysis included chi-square test or Fisher's exact test. Bitemporal lobe epilepsy patients had more frequently different seizure semiology (100% vs. 40%; psemiology for the identification of bilateral TLE was high (100%) with a specificity of 60%. Lateralizing seizure phenomena had a low sensitivity (59%) but a high specificity (89%). The combination of lateralizing seizure phenomena and different seizure semiology showed a high specificity (94%) but a low sensitivity (59%). The analysis of seizure semiology including lateralizing seizure phenomena adds important clinical information to identify patients with bilateral TLE. Copyright © 2014 Elsevier B.V. All rights reserved.

  2. Managing first-time seizures and epilepsy in children

    African Journals Online (AJOL)

    2011-04-04

    Apr 4, 2011 ... diagnosis and minimises unnecessary investigations and treatment.1. Epilepsy is defined as 2 or more unprovoked seizures and also requires a logical approach to management. Stepwise approach to a child with a suspected first-time seizure. • Is this a true seizure? • If so, what type of seizure is it?

  3. Soy infant formula and seizures in children with autism: a retrospective study.

    Directory of Open Access Journals (Sweden)

    Cara J Westmark

    Full Text Available Seizures are a common phenotype in many neurodevelopmental disorders including fragile X syndrome, Down syndrome and autism. We hypothesized that phytoestrogens in soy-based infant formula were contributing to lower seizure threshold in these disorders. Herein, we evaluated the dependence of seizure incidence on infant formula in a population of autistic children. Medical record data were obtained on 1,949 autistic children from the SFARI Simplex Collection. An autism diagnosis was determined by scores on the ADI-R and ADOS exams. The database included data on infant formula use, seizure incidence, the specific type of seizure exhibited and IQ. Soy-based formula was utilized in 17.5% of the study population. Females comprised 13.4% of the subjects. There was a 2.6-fold higher rate of febrile seizures [4.2% versus 1.6%, OR = 2.6, 95% CI = 1.3-5.3], a 2.1-fold higher rate of epilepsy comorbidity [3.6% versus 1.7%, OR = 2.2, 95% CI = 1.1-4.7] and a 4-fold higher rate of simple partial seizures [1.2% versus 0.3%, OR = 4.8, 95% CI = 1.0-23] in the autistic children fed soy-based formula. No statistically significant associations were found with other outcomes including: IQ, age of seizure onset, infantile spasms and atonic, generalized tonic clonic, absence and complex partial seizures. Limitations of the study included: infant formula and seizure data were based on parental recall, there were significantly less female subjects, and there was lack of data regarding critical confounders such as the reasons the subjects used soy formula, age at which soy formula was initiated and the length of time on soy formula. Despite these limitations, our results suggest that the use of soy-based infant formula may be associated with febrile seizures in both genders and with a diagnosis of epilepsy in males in autistic children. Given the lack of data on critical confounders and the retrospective nature of the study, a prospective study is

  4. Target Detection Based on EBPSK Satellite Passive Radar

    Directory of Open Access Journals (Sweden)

    Lu Zeyuan

    2015-05-01

    Full Text Available Passive radar is a topic anti stealth technology with simple structure, and low cost. Radiation source model, signal transmission model, and target detection are the key points of passive radar technology research. The paper analyzes the characteristics of EBPSK signal modulation and target detection method aspect of spaceborne radiant source. By comparison with other satellite navigation and positioning system, the characteristics of EBPSK satellite passive radar system are analyzed. It is proved that the maximum detection range of EBPSK satellite signal can satisfy the needs of the proposed model. In the passive radar model, sparse representation is used to achieve high resolution DOA detection. The comparison with the real target track by simulation demonstrates that effective detection of airborne target using EBPSK satellite passive radar system based on sparse representation is efficient.

  5. Dim target detection method based on salient graph fusion

    Science.gov (United States)

    Hu, Ruo-lan; Shen, Yi-yan; Jiang, Jun

    2018-02-01

    Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

  6. Edge detection based on computational ghost imaging with structured illuminations

    Science.gov (United States)

    Yuan, Sheng; Xiang, Dong; Liu, Xuemei; Zhou, Xin; Bing, Pibin

    2018-03-01

    Edge detection is one of the most important tools to recognize the features of an object. In this paper, we propose an optical edge detection method based on computational ghost imaging (CGI) with structured illuminations which are generated by an interference system. The structured intensity patterns are designed to make the edge of an object be directly imaged from detected data in CGI. This edge detection method can extract the boundaries for both binary and grayscale objects in any direction at one time. We also numerically test the influence of distance deviations in the interference system on edge extraction, i.e., the tolerance of the optical edge detection system to distance deviation. Hopefully, it may provide a guideline for scholars to build an experimental system.

  7. Research on Daily Objects Detection Based on Deep Neural Network

    Science.gov (United States)

    Ding, Sheng; Zhao, Kun

    2018-03-01

    With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.

  8. Automated detection and classification for craters based on geometric matching

    Science.gov (United States)

    Chen, Jian-qing; Cui, Ping-yuan; Cui, Hui-tao

    2011-08-01

    Crater detection and classification are critical elements for planetary mission preparations and landing site selection. This paper presents a methodology for the automated detection and matching of craters on images of planetary surface such as Moon, Mars and asteroids. For craters usually are bowl shaped depression, craters can be figured as circles or circular arc during landing phase. Based on the hypothesis that detected crater edges is related to craters in a template by translation, rotation and scaling, the proposed matching method use circles to fitting craters edge, and align circular arc edges from the image of the target body with circular features contained in a model. The approach includes edge detection, edge grouping, reference point detection and geometric circle model matching. Finally we simulate planetary surface to test the reasonableness and effectiveness of the proposed method.

  9. Wavelet Packet based Detection of Surface Faults on Compact Discs

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Wickerhauser, Mladen Victor

    2006-01-01

    In this paper the detection of faults on the surface of a compact disc is addressed. Surface faults like scratches and fingerprints disturb the on-line measurement of the pick-up position relative to the track. This is critical since the pick-up is focused on and tracked at the information track...... based on these measurements. A precise detection of the surface fault is a prerequisite to a correct handling of the faults in order to protect the pick-up of the compact disc player from audible track losses. The actual fault handling which is addressed in other publications can be carried out...... by the use of dedicated filters adapted to remove the faults from the measurements. In this paper detection using wavelet packet filters is demonstrated. The filters are designed using the joint best basis method. Detection using these filters shows a distinct improvement compared to detection using ordinary...

  10. Prenatal stress and risk of febrile seizures in children: a nationwide longitudinal study in Denmark

    DEFF Research Database (Denmark)

    Li, Jiong; Olsen, Jørn; Obel, Carsten

    2009-01-01

    We aimed to examine whether exposure to prenatal stress following maternal bereavement is associated with an increased risk of febrile seizures. In a longitudinal population-based cohort study, we followed 1,431,175 children born in Denmark. A total of 34,777 children were born to women who lost...... or timing of bereavement. Our data do not suggest any causal link between exposure to prenatal stress and febrile seizures in childhood....

  11. Optimized feature subsets for epileptic seizure prediction studies.

    Science.gov (United States)

    Direito, Bruno; Ventura, Francisco; Teixeira, César; Dourado, António

    2011-01-01

    The reduction of the number of EEG features to give as inputs to epilepsy seizure predictors is a needed step towards the development of a transportable device for real-time warning. This paper presents a comparative study of three feature selection methods, based on Support Vector Machines. Minimum-Redundancy Maximum-Relevance, Recursive Feature Elimination, Genetic Algorithms, show that, for three patients of the European Database on Epilepsy, the most important univariate features are related to spectral information and statistical moments.

  12. Arduino-based noise robust online heart-rate detection.

    Science.gov (United States)

    Das, Sangita; Pal, Saurabh; Mitra, Madhuchhanda

    2017-04-01

    This paper introduces a noise robust real time heart rate detection system from electrocardiogram (ECG) data. An online data acquisition system is developed to collect ECG signals from human subjects. Heart rate is detected using window-based autocorrelation peak localisation technique. A low-cost Arduino UNO board is used to implement the complete automated process. The performance of the system is compared with PC-based heart rate detection technique. Accuracy of the system is validated through simulated noisy ECG data with various levels of signal to noise ratio (SNR). The mean percentage error of detected heart rate is found to be 0.72% for the noisy database with five different noise levels.

  13. Stratification-Based Outlier Detection over the Deep Web.

    Science.gov (United States)

    Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

    2016-01-01

    For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

  14. A UAV-BASED ROE DEER FAWN DETECTION SYSTEM

    Directory of Open Access Journals (Sweden)

    M. Israel

    2012-09-01

    Full Text Available This paper presents a UAV based remote sensing system for the detection of fawns in the meadows. There is a high demand because during pasture mowing many wild animals, especially roe deer fawns are killed by mowing machines. The system was tested in several real situations especially with differing weather and iluminating conditions. Its primary sensor is a lightweight thermal infrared camera. The images are captured onboard of the flight system and also transmitted as analog video stream to the ground station, where the user can follow the camera live stream on a monitor for manual animal detection. Beside a high detection rate a fast workflow is another very important objective for this application. Therefore a waypoint planning software was developed that accelerates the workflow. At adequate illuminating and weather conditions the presented UAV-based fawn detection via thermal imaging is a comfortable, fast and reliable method.

  15. a Uav-Based ROE Deer Fawn Detection System

    Science.gov (United States)

    Israel, M.

    2011-09-01

    This paper presents a UAV based remote sensing system for the detection of fawns in the meadows. There is a high demand because during pasture mowing many wild animals, especially roe deer fawns are killed by mowing machines. The system was tested in several real situations especially with differing weather and iluminating conditions. Its primary sensor is a lightweight thermal infrared camera. The images are captured onboard of the flight system and also transmitted as analog video stream to the ground station, where the user can follow the camera live stream on a monitor for manual animal detection. Beside a high detection rate a fast workflow is another very important objective for this application. Therefore a waypoint planning software was developed that accelerates the workflow. At adequate illuminating and weather conditions the presented UAV-based fawn detection via thermal imaging is a comfortable, fast and reliable method.

  16. The Unknown Computer Viruses Detection Based on Similarity

    Science.gov (United States)

    Liu, Zhongda; Nakaya, Naoshi; Koui, Yuuji

    New computer viruses are continually being generated and they cause damage all over the world. In general, current anti-virus software detects viruses by matching a pattern based on the signature; thus, unknown viruses without any signature cannot be detected. Although there are some static analysis technologies that do not depend on signatures, virus writers often use code obfuscation techniques, which make it difficult to execute a code analysis. As is generally known, unknown viruses and known viruses share a common feature. In this paper we propose a new static analysis technology that can circumvent code obfuscation to extract the common feature and detect unknown viruses based on similarity. The results of evaluation experiments demonstrated that this technique is able to detect unknown viruses without false positives.

  17. Enhancing Community Detection By Affinity-based Edge Weighting Scheme

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Andy [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sanders, Geoffrey [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Henson, Van [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Vassilevski, Panayot [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-10-05

    Community detection refers to an important graph analytics problem of finding a set of densely-connected subgraphs in a graph and has gained a great deal of interest recently. The performance of current community detection algorithms is limited by an inherent constraint of unweighted graphs that offer very little information on their internal community structures. In this paper, we propose a new scheme to address this issue that weights the edges in a given graph based on recently proposed vertex affinity. The vertex affinity quantifies the proximity between two vertices in terms of their clustering strength, and therefore, it is ideal for graph analytics applications such as community detection. We also demonstrate that the affinity-based edge weighting scheme can improve the performance of community detection algorithms significantly.

  18. Evaluation of the Effect of Jobelyn® on Chemoconvulsants- Induced Seizure in Mice

    Directory of Open Access Journals (Sweden)

    Solomon Umukoro

    2013-04-01

    Full Text Available Introduction: Epilepsy is a common central nervous system (CNS disorder characterized by seizures resulting from episodic neuronal discharges. The incidence of toxicity and refractoriness has compromised the clinical efficacy of the drugs currently used for the treatment of convulsions. Thus, there is a need to search for new medicines from plant origin that are readily available and safer for the control of seizures. Jobelyn® (JB is a unique African polyherbal preparation used by the natives to treat seizures in children. This investigation was carried out to evaluate whether JB has anti-seizure property in mice. Methods: The animals received JB (5, 10 and 20 mg/kg, p.o 30 min before induction of convulsions with intraperitoneal (i.p. injection of picrotoxin (6 mg/kg, strychnine (2 mg/ kg and pentylenetetrazole (85 mg/kg respectively. Diazepam (2 mg/kg, p.o. was used as the reference drug. Anti-seizure activities were assessed based on the ability of test drugs to prevent convulsions, death or to delay the onset of seizures in mice. Results: JB (5, 10 and 20 mg/kg, p.o could only delay the onset of seizures induced by pentylenetetrazole (85 mg/kg, i.p. in mice. However, it did not offer any protection against seizure episodes, as it failed to prevent the animals, from exhibiting tonic-clonic convulsions caused by pentylenetetrazole (85 mg/kg, i.p., strychnine (2 mg/kg or picrotoxin (6 mg/kg, i.p.. On the other hand, diazepam (2 mg/kg, p.o., offered 100% protection against convulsive seizures, induced by pentylenetetrazole (85 mg/kg, i.p.. However, it failed to prevent seizures produced by strychnine (2 mg/kg, i.p. or picrotoxin (6 mg/kg, i.p.. Discussion: Our results suggest that JB could not prevent the examined chemoconvulsants-induced convulsions. However, its ability to delay the latency to seizures induced by pentylenetetrazole suggests that JB might be effective in the control of the seizure spread in epileptic brains.

  19. A survey of seizures and current treatments in 15q duplication syndrome.

    Science.gov (United States)

    Conant, Kerry D; Finucane, Brenda; Cleary, Nicole; Martin, Ashley; Muss, Candace; Delany, Mary; Murphy, Erin K; Rabe, Olivia; Luchsinger, Kadi; Spence, Sarah J; Schanen, Carolyn; Devinsky, Orrin; Cook, Edwin H; LaSalle, Janine; Reiter, Lawrence T; Thibert, Ronald L

    2014-03-01

    Seizures are common in individuals with duplications of chromosome 15q11.2-q13 (Dup15q). The goal of this study was to examine the phenotypes and treatments of seizures in Dup15q in a large population. A detailed electronic survey was conducted through the Dup15q Alliance containing comprehensive questions regarding seizures and their treatments in Dup15q. There were 95 responses from Dup15q families. For the 83 with idic(15), 63% were reported to have seizures, of which 81% had multiple seizure types and 42% had infantile spasms. Other common seizure types were tonic-clonic, atonic, myoclonic, and focal. Only 3 of 12 individuals with int dup(15) had seizures. Broad spectrum antiepileptic drugs (AEDs) were the most effective medications, but carbamazepine and oxcarbazepine were also effective, although typical benzodiazepines were relatively ineffective. There was a 24% response rate (>90% seizure reduction) to the first AED tried. For those with infantile spasms, adrenocorticotropic hormone (ACTH) was more effective than vigabatrin. This is the largest study assessing seizures in Duplication 15q syndrome, but because this was a questionnaire-based study with a low return rate, it is susceptible to bias. Seizures are common in idic(15) and typically difficult to control, often presenting with infantile spasms and progressing to a Lennox-Gastaut-type syndrome. Seizures in those with int dup(15) are less common, with a frequency similar to the general autism population. In addition to broad spectrum AED, medications such as carbamazepine and oxcarbazepine are also relatively effective in controlling seizures in this population, suggesting a possible multifocal etiology, which may also explain the high rate of infantile spasms. Our small sample suggests a relative lack of efficacy of vigabatrin and other γ-aminobutyric acid (GABA)ergic medications, such as typical benzodiazepines, which may be attributable to abnormal GABAergic transmission resulting from the

  20. Pipeline Processing with an Iterative, Context-Based Detection Model

    Science.gov (United States)

    2016-01-22

    unlimited. 59 optimum choice for steering vectors is the adaptive beamformer weighting [ Capon et al ., 1967] also known as the minimum variance...AFRL-RV-PS- AFRL-RV-PS- TR-2016-0080 TR-2016-0080 PIPELINE PROCESSING WITH AN ITERATIVE, CONTEXT-BASED DETECTION MODEL T. Kværna, et al ...conditions, the capability to detect events down to magnitude 2.0 [Gibbons et al ., 2011]. Figure 3. Location of the SPITS array in relation to Novaya

  1. An Automated Energy Detection Algorithm Based on Consecutive Mean Excision

    Science.gov (United States)

    2018-01-01

    ARL-TR-8268 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Consecutive Mean Excision...not return it to the originator. ARL-TR-8268 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm...2018 2. REPORT TYPE Technical Report 3. DATES COVERED (From - To) 1 October 2016–30 September 2017 4. TITLE AND SUBTITLE An Automated Energy

  2. Flow-based Brute-force Attack Detection

    OpenAIRE

    Drašar, Martin; Vykopal, Jan; Winter, Philipp

    2013-01-01

    Brute-force attacks are a prevalent phenomenon that is getting harderto successfully detect on a network level due to increasing volume and en-cryption of network traffic and growing ubiquity of high-speed networks.Although the research in this field advanced considerably, there still remainclasses of attacks that are undetectable. In this chapter, we present sev-eral methods for the detection of brute-force attacks based on the analysisof network flows. We discuss their strengths and shortco...

  3. Computer-vision-based car logotype detection and recognition

    OpenAIRE

    Tomažič, Gašper

    2015-01-01

    This thesis addresses the problem of image-based logotype detection and recognition. A new algorithm for logotype detection in images of cars is proposed. In the first stage, the algorithm localizes all maximally-stable extremal regions as candidates of logotype parts. In the next stage, the regions are combined to create logotype candidates, which are encoded by histograms of gradients. A random forest classifier is then used to verify the candidate regions as being logotypes or not and simu...

  4. Fiber-Optic Based Compact Gas Leak Detection System

    Science.gov (United States)

    deGroot, Wim A.

    1995-01-01

    A propellant leak detection system based on Raman scattering principles is introduced. The proposed system is flexible and versatile as the result of the use of optical fibers. It is shown that multiple species can be monitored simultaneously. In this paper oxygen, nitrogen, carbon monoxide, and hydrogen are detected and monitored. The current detection sensitivity for both hydrogen and carbon monoxide is 1% partial pressure at ambient conditions. The sensitivity for oxygen and nitrogen is 0.5% partial pressure. The response time to changes in species concentration is three minutes. This system can be used to monitor multiple species at several locations.

  5. Statistical Outlier Detection for Jury Based Grading Systems

    DEFF Research Database (Denmark)

    Thompson, Mary Kathryn; Clemmensen, Line Katrine Harder; Rosas, Harvey

    2013-01-01

    This paper presents an algorithm that was developed to identify statistical outliers from the scores of grading jury members in a large project-based first year design course. The background and requirements for the outlier detection system are presented. The outlier detection algorithm...... and the follow-up procedures for score validation and appeals are described in detail. Finally, the impact of various elements of the outlier detection algorithm, their interactions, and the sensitivity of their numerical values are investigated. It is shown that the difference in the mean score produced...

  6. Gold nanoparticle-based microfluidic sensor for mercury detection

    DEFF Research Database (Denmark)

    Lafleur, Josiane P.; Jensen, Thomas Glasdam; Kutter, Jörg Peter

    2011-01-01

    The contamination of natural resources by human activity can have severe socio-economical impacts. Conventional methods of environmental analysis can be significantly improved by the development of portable microscale technologies for remote/field sensing. A gold nanoparticle-based lab-on-a-chip ......-on-a-chip device was developed for the rapid, in-field detection and quantification of mercury in aquatic environments. Rhodamine 6G functionalized gold nanoparticles allowed the on-chip fluorescence detection of mercury in aqueous samples with a limit of detection of 7 nM....

  7. Counselling adults who experience a first seizure.

    Science.gov (United States)

    Legg, Karen T; Newton, Mark

    2017-07-01

    A first seizure can result in significant uncertainty, fear and apprehension. One of the key roles of the clinician in the setting of first seizure is to provide accurate, timely information and counselling. We review the numerous components to be considered when counselling an adult patient after a first seizure. We provide a framework and manner to provide that counselling. We focus on an individualized approach and provide recommendations and information on issues of diagnosis, etiology, prognosis, the role and importance of medical testing, lifestyle considerations, driving, medication and other key counselling considerations. Accurate, timely counselling can allay fears and anxieties, remove misconceptions and reduce the risk for injury in seizure recurrence. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  8. Seizures associated with Lupus during pregnancy

    OpenAIRE

    Aoki, Shigeru; Kobayashi, Natsuko; Mochimaru, Aya; Takahashi, Tsuneo; Hirahara, Fumiki

    2016-01-01

    Key Clinical Message A sudden flare of previously stable SLE may give rise to CNS lupus. During pregnancy, seizures associated with CNS lupus can cause hypoxic?ischemic encephalopathy (HIE) in the infant.

  9. Seizures and Munchausen Syndrome by Proxy

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2002-05-01

    Full Text Available The prevalence, morbidity and mortality, diagnosis and management of cases of fabricated seizures and child abuse (Munchausen syndrome by proxy (MSbp are assessed by pediatricians at the University of Wales College of Medicine, Cardiff, UK.

  10. Serum Prolactin in Diagnosis of Epileptic Seizures

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2005-09-01

    Full Text Available The results of studies in databases and references concerning serum prolactin levels (PRL in patients with suspected seizures were rated for quality and analyzed by members of the Therapeutics Subcommittee of the American Academy of Neurology.

  11. Multiple Sclerosis: Can It Cause Seizures?

    Science.gov (United States)

    ... it cause seizures? Is there any connection between multiple sclerosis and epilepsy? Answers from B Mark Keegan, M. ... article: http://www.mayoclinic.org/diseases-conditions/multiple-sclerosis/expert-answers/multiple-sclerosis/FAQ-20058138 . Mayo Clinic Footer Legal Conditions ...

  12. Types of Seizures Affecting Individuals with TSC

    Science.gov (United States)

    ... body, and upper legs. May cause person to spill what they were holding or fall off a ... reflects recent advances in our understanding of the brain and seizures. This new system will make diagnosis ...

  13. Tactile sensor of hardness recognition based on magnetic anomaly detection

    Science.gov (United States)

    Xue, Lingyun; Zhang, Dongfang; Chen, Qingguang; Rao, Huanle; Xu, Ping

    2018-03-01

    Hardness, as one kind of tactile sensing, plays an important role in the field of intelligent robot application such as gripping, agricultural harvesting, prosthetic hand and so on. Recently, with the rapid development of magnetic field sensing technology with high performance, a number of magnetic sensors have been developed for intelligent application. The tunnel Magnetoresistance(TMR) based on magnetoresistance principal works as the sensitive element to detect the magnetic field and it has proven its excellent ability of weak magnetic detection. In the paper, a new method based on magnetic anomaly detection was proposed to detect the hardness in the tactile way. The sensor is composed of elastic body, ferrous probe, TMR element, permanent magnet. When the elastic body embedded with ferrous probe touches the object under the certain size of force, deformation of elastic body will produce. Correspondingly, the ferrous probe will be forced to displace and the background magnetic field will be distorted. The distorted magnetic field was detected by TMR elements and the output signal at different time can be sampled. The slope of magnetic signal with the sampling time is different for object with different hardness. The result indicated that the magnetic anomaly sensor can recognize the hardness rapidly within 150ms after the tactile moment. The hardness sensor based on magnetic anomaly detection principal proposed in the paper has the advantages of simple structure, low cost, rapid response and it has shown great application potential in the field of intelligent robot.

  14. Deceiving entropy-based DoS detection

    Science.gov (United States)

    Özçelik, Ä.°lker; Brooks, Richard R.

    2014-06-01

    Denial of Service (DoS) attacks disable network services for legitimate users. A McAfee report shows that eight out of ten Critical Infrastructure Providers (CIPs) surveyed had a significant Distributed DoS (DDoS) attack in 2010.1 Researchers proposed many approaches for detecting these attacks in the past decade. Anomaly based DoS detection is the most common. In this approach, the detector uses statistical features; such as the entropy of incoming packet header fields like source IP addresses or protocol type. It calculates the observed statistical feature and triggers an alarm if an extreme deviation occurs. However, intrusion detection systems (IDS) using entropy based detection can be fooled by spoofing. An attacker can sniff the network to collect header field data of network packets coming from distributed nodes on the Internet and fuses them to calculate the entropy of normal background traffic. Then s/he can spoof attack packets to keep the entropy value in the expected range during the attack. In this study, we present a proof of concept entropy spoofing attack that deceives entropy based detection approaches. Our preliminary results show that spoofing attacks cause significant detection performance degradation.

  15. Vision-Based People Detection System for Heavy Machine Applications

    Directory of Open Access Journals (Sweden)

    Vincent Fremont

    2016-01-01

    Full Text Available This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance.

  16. Vision-Based People Detection System for Heavy Machine Applications.

    Science.gov (United States)

    Fremont, Vincent; Bui, Manh Tuan; Boukerroui, Djamal; Letort, Pierrick

    2016-01-20

    This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance.

  17. Compressive Sensing-Based Detection With Multimodal Dependent Data

    Science.gov (United States)

    Wimalajeewa, Thakshila; Varshney, Pramod K.

    2018-02-01

    Detection with high dimensional multimodal data is a challenging problem when there are complex inter- and intra- modal dependencies. While several approaches have been proposed for dependent data fusion (e.g., based on copula theory), their advantages come at a high price in terms of computational complexity. In this paper, we treat the detection problem with compressive sensing (CS) where compression at each sensor is achieved via low dimensional random projections. CS has recently been exploited to solve detection problems under various assumptions on the signals of interest, however, its potential for dependent data fusion has not been explored adequately. We exploit the capability of CS to capture statistical properties of uncompressed data in order to compute decision statistics for detection in the compressed domain. First, a Gaussian approximation is employed to perform likelihood ratio (LR) based detection with compressed data. In this approach, inter-modal dependence is captured via a compressed version of the covariance matrix of the concatenated (temporally and spatially) uncompressed data vector. We show that, under certain conditions, this approach with a small number of compressed measurements per node leads to enhanced performance compared to detection with uncompressed data using widely considered suboptimal approaches. Second, we develop a nonparametric approach where a decision statistic based on the second order statistics of uncompressed data is computed in the compressed domain. The second approach is promising over other related nonparametric approaches and the first approach when multimodal data is highly correlated at the expense of slightly increased computational complexity.

  18. Hyperspectral anomaly detection based on stacked denoising autoencoders

    Science.gov (United States)

    Zhao, Chunhui; Li, Xueyuan; Zhu, Haifeng

    2017-10-01

    Hyperspectral anomaly detection (AD) is an important technique of unsupervised target detection and has significance in real situations. Due to the high dimensionality of hyperspectral data, AD will be influenced by noise, nonlinear correlation of band, or other factors that lead to the decline of detection accuracy. To overcome this problem, a method of hyperspectral AD based on stacked denoising autoencoders (AE) (HADSDA) is proposed. Simultaneously, two different feature detection models, spectral feature (SF) and fused feature by clustering (FFC), are constructed to verify the effectiveness of the proposed algorithm. The SF detection model uses the SF of each pixel. The FFC detection model uses a similar set of pixels constructed by clustering and then fuses the set of pixels by the stacked denoising autoencoders algorithm (SDA). The SDA is an algorithm that can automatically learn nonlinear deep features of the image. Compared with other linear or nonlinear feature extraction methods, the detection result of the proposed algorithm is greatly improved. Experiment results show that the proposed algorithm is an excellent feature learning method and can achieve higher detection performance.

  19. Seizures in dominantly inherited Alzheimer disease.

    Science.gov (United States)

    Zarea, Aline; Charbonnier, Camille; Rovelet-Lecrux, Anne; Nicolas, Gaël; Rousseau, Stéphane; Borden, Alaina; Pariente, Jeremie; Le Ber, Isabelle; Pasquier, Florence; Formaglio, Maite; Martinaud, Olivier; Rollin-Sillaire, Adeline; Sarazin, Marie; Croisile, Bernard; Boutoleau-Bretonnière, Claire; Ceccaldi, Mathieu; Gabelle, Audrey; Chamard, Ludivine; Blanc, Frédéric; Sellal, François; Paquet, Claire; Campion, Dominique; Hannequin, Didier; Wallon, David

    2016-08-30

    To assess seizure frequency in a large French cohort of autosomal dominant early-onset Alzheimer disease (ADEOAD) and to determine possible correlations with causative mutations. A national multicentric study was performed in patients with ADEOAD harboring a pathogenic mutation within PSEN1, PSEN2, APP, or a duplication of APP, and a minimal follow-up of 5 years. Clinical, EEG, and imaging data were systematically recorded. We included 132 patients from 77 families: 94 PSEN1 mutation carriers (MCs), 16 APP duplication carriers, 15 APP MCs, and 7 PSEN2 MCs. Seizure frequency was 47.7% after a mean follow-up of 8.4 years (range 5-25). After 5-year follow-up and using a Cox model analysis, the percentages of patients with seizures were respectively 19.1% (10.8%-26.7%) for PSEN1, 28.6% (0%-55.3%) for PSEN2, 31.2% (4.3%-50.6%) for APP duplications, and no patient for APP mutation. APP duplication carriers showed a significantly increased seizure risk compared to both APP MCs (hazard ratio [HR] = 5.55 [95% confidence interval 1.87-16.44]) and PSEN1 MCs (HR = 4.46 [2.11-9.44]). Among all PSEN1 mutations, those within the domains of protein hydrophilic I, transmembrane II (TM-II), TM-III, TM-IV, and TM-VII were associated with a significant increase in seizure frequency compared to other domains (HR = 4.53 [1.93-10.65], p = 0.0005). Seizures are a common feature of ADEOAD. In this population, risk was significantly higher in the APP duplication group than in all other groups. Within PSEN1, 5 specific domains were associated with a higher seizure risk indicating specific correlations between causative mutation and seizures. © 2016 American Academy of Neurology.

  20. Ketogenic diet: Predictors of seizure control

    Science.gov (United States)

    Agarwal, Nitin; Arkilo, Dimitrios; Farooq, Osman; Gillogly, Cynthia; Kavak, Katelyn S; Weinstock, Arie

    2017-01-01

    Background: The ketogenic diet is an effective non-pharmacologic treatment for medically resistant epilepsy. The aim of this study was to identify any predictors that may influence the response of ketogenic diet. Methods: A retrospective chart review for all patients with medically resistant epilepsy was performed at a tertiary care epilepsy center from 1996 to 2012. Patient- and diet-related variables were evaluated with respect to seizure reduction at 1, 3, 6, 9 and 12-month intervals and divided into four possible outcome classes. Results: Sixty-three patients met inclusion. Thirty-seven (59%) reported >50% seizure reduction at 3 months with 44% and 37% patients benefiting at 6-month and 12-month follow up, respectively. A trend toward significant seizure improvement was noted in 48% patients with seizure onset >1 year at 12-month (p = 0.09) interval and in 62% patients with >10 seizure/day at 6-month interval (p = 0.054). An ordinal logistic regression showed later age of seizure to have higher odds of favorable response at 1-month (p = 0.005) and 3-month (p = 0.013) follow up. Patients with non-fasting diet induction were more likely to have a favorable outcome at 6 months (p = 0.008) as do females (p = 0.037) and those treated with higher fat ratio diet (p = 0.034). Conclusion: Our study reports the effectiveness of ketogenic diet in children with medically resistant epilepsy. Later age of seizure onset, female gender, higher ketogenic diet ratio and non-fasting induction were associated with better odds of improved seizure outcome. A larger cohort is required to confirm these findings. PMID:28620490

  1. Ketogenic diet: Predictors of seizure control.

    Science.gov (United States)

    Agarwal, Nitin; Arkilo, Dimitrios; Farooq, Osman; Gillogly, Cynthia; Kavak, Katelyn S; Weinstock, Arie

    2017-01-01

    The ketogenic diet is an effective non-pharmacologic treatment for medically resistant epilepsy. The aim of this study was to identify any predictors that may influence the response of ketogenic diet. A retrospective chart review for all patients with medically resistant epilepsy was performed at a tertiary care epilepsy center from 1996 to 2012. Patient- and diet-related variables were evaluated with respect to seizure reduction at 1, 3, 6, 9 and 12-month intervals and divided into four possible outcome classes. Sixty-three patients met inclusion. Thirty-seven (59%) reported >50% seizure reduction at 3 months with 44% and 37% patients benefiting at 6-month and 12-month follow up, respectively. A trend toward significant seizure improvement was noted in 48% patients with seizure onset >1 year at 12-month (p = 0.09) interval and in 62% patients with >10 seizure/day at 6-month interval (p = 0.054). An ordinal logistic regression showed later age of seizure to have higher odds of favorable response at 1-month (p = 0.005) and 3-month (p = 0.013) follow up. Patients with non-fasting diet induction were more likely to have a favorable outcome at 6 months (p = 0.008) as do females (p = 0.037) and those treated with higher fat ratio diet (p = 0.034). Our study reports the effectiveness of ketogenic diet in children with medically resistant epilepsy. Later age of seizure onset, female gender, higher ketogenic diet ratio and non-fasting induction were associated with better odds of improved seizure outcome. A larger cohort is required to confirm these findings.

  2. Infantile Spasms: Little Seizures, BIG Consequences

    Science.gov (United States)

    Shields, W Donald

    2006-01-01

    Infantile spasms is one of the “catastrophic childhood epilepsies” because of the difficulty in controlling seizures and the association with mental retardation. However, early recognition, a careful diagnostic evaluation, and proper treatment may allow some children to attain seizure control and to achieve a normal, or at least much improved, level of development. Thus, there is the opportunity to have an important impact in the lives of these unfortunate children and their families. PMID:16761063

  3. Analysis of Vehicle Detection with WSN-Based Ultrasonic Sensors

    Directory of Open Access Journals (Sweden)

    Youngtae Jo

    2014-08-01

    Full Text Available Existing traffic information acquisition systems suffer from high cost and low scalability. To address these problems, the application of wireless sensor networks (WSNs has been studied, as WSN-based systems are highly scalable and have a low cost of installing and replacing the systems. Magnetic, acoustic and accelerometer sensors have been considered for WSN-based traffic surveillance, but the use of ultrasonic sensors has not been studied. The limitations of WSN-based systems make it necessary to employ power saving methods and vehicle detection algorithms with low computational complexity. In this paper, we model and analyze optimal power saving methodologies for an ultrasonic sensor and present a computationally-efficient vehicle detection algorithm using ultrasonic data. The proposed methodologies are implemented and evaluated with a tiny microprocessor on real roads. The evaluation results show that the low computational complexity of our algorithm does not compromise the accuracy of vehicle detection.

  4. DNA methylation detection based on difference of base content

    Science.gov (United States)

    Sato, Shinobu; Ohtsuka, Keiichi; Honda, Satoshi; Sato, Yusuke; Takenaka, Shigeori

    2016-04-01

    Methylation frequently occurs in cytosines of CpG sites to regulate gene expression. The identification of aberrant methylation of certain genes is important for cancer marker analysis. The aim of this study was to determine the methylation frequency in DNA samples of unknown length and/or concentration. Unmethylated cytosine is known to be converted to thymine following bisulfite treatment and subsequent PCR. For this reason, the AT content in DNA increases with an increasing number of methylation sites. In this study, the fluorescein-carrying bis-acridinyl peptide (FKA) molecule was used for the detection of methylation frequency. FKA contains fluorescein and two acridine moieties, which together allow for the determination of the AT content of double-stranded DNA fragments. Methylated and unmethylated human genomes were subjected to bisulfide treatment and subsequent PCR using primers specific for the CFTR, CDH4, DBC1, and NPY genes. The AT content in the resulting PCR products was estimated by FKA, and AT content estimations were found to be in good agreement with those determined by DNA sequencing. This newly developed method may be useful for determining methylation frequencies of many PCR products by measuring the fluorescence in samples excited at two different wavelengths.

  5. (1)H NMR metabolomics to study the effects of diazepam on anisatin induced convulsive seizures.

    Science.gov (United States)

    Li, Pei; Wei, Dan-Dan; Wang, Jun-Song; Yang, Ming-Hua; Kong, Ling-Yi

    2016-01-05

    The anticonvulsive properties of diazepam have been extensively studied, mainly focusing on the γ-amino butyrate (GABA) system. The aim of this investigation was to integrally analyze the metabolic events related to neuroprotection of diazepam on anisatin-induced convulsive seizures by a NMR-based metabolomic approach combined with histopathological examination and behavior examination. Multivariate analysis on metabolic profiles of the piriform cortex and cerebellum of mice revealed that diazepam could relieve mice suffering from the convulsive seizures by recovering destructed neurotransmitter and neuromodulator metabolism, ameliorating oxidative stress, alleviating the disturbance in energy, amino acid and nucleic acid metabolism in anisatin intoxicated mice. This integrated metabolomics study provided a powerful and highly effective approach to elucidate therapeutic effects and assessed the safety of diazepam. This study should be helpful for our understanding of convulsive seizures, and provide a holistic view of the treatment effects of benzodiazepine on convulsive seizures. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Prenatal exposure to cigarettes, alcohol, and coffee and the risk for febrile seizures

    DEFF Research Database (Denmark)

    Vestergaard, M; Wisborg, K; Henriksen, TB

    2005-01-01

    of extensive brain growth and differentiation in this period. We evaluated the association between prenatal exposure to cigarettes, alcohol, and coffee and the risk for febrile seizures in 2 population-based birth cohorts. METHODS: The Aarhus Birth Cohort consisted of 25,196 children of mothers who were...... Birth Cohort, but the corresponding association was weak in the Aalborg-Odense cohort. We found no association between maternal alcohol and coffee consumption and the risk for febrile seizures. The results were similar for simple and complex febrile seizures. CONCLUSIONS: Our data suggest that prenatal...... exposure to low to moderate levels of alcohol and coffee has no impact on the risk for febrile seizures, whereas a modest smoking effect cannot be ruled out....

  7. State-based Event Detection Optimization for Complex Event Processing

    Directory of Open Access Journals (Sweden)

    Shanglian PENG

    2014-02-01

    Full Text Available Detection of patterns in high speed, large volume of event streams has been an important paradigm in many application areas of Complex Event Processing (CEP including security monitoring, financial markets analysis and health-care monitoring. To assure real-time responsive complex pattern detection over high volume and speed event streams, efficient event detection techniques have to be designed. Unfortunately evaluation of the Nondeterministic Finite Automaton (NFA based event detection model mainly considers single event query and its optimization. In this paper, we propose multiple event queries evaluation on event streams. In particular, we consider scalable multiple event detection model that shares NFA transfer states of different event queries. For each event query, the event query is parse into NFA and states of the NFA are partitioned into different units. With this partition, the same individual state of NFA is run on different processing nodes, providing states sharing and reducing partial matches maintenance. We compare our state-based approach with Stream-based And Shared Event processing (SASE. Our experiments demonstrate that state-based approach outperforms SASE both on CPU time usage and memory consumption.

  8. The detection of bulk explosives using nuclear-based techniques

    Energy Technology Data Exchange (ETDEWEB)

    Morgado, R.E.; Gozani, T.; Seher, C.C.

    1988-01-01

    In 1986 we presented a rationale for the detection of bulk explosives based on nuclear techniques that addressed the requirements of civil aviation security in the airport environment. Since then, efforts have intensified to implement a system based on thermal neutron activation (TNA), with new work developing in fast neutron and energetic photon reactions. In this paper we will describe these techniques and present new results from laboratory and airport testing. Based on preliminary results, we contended in our earlier paper that nuclear-based techniques did provide sufficiently penetrating probes and distinguishable detectable reaction products to achieve the FAA operational goals; new data have supported this contention. The status of nuclear-based techniques for the detection of bulk explosives presently under investigation by the US Federal Aviation Administration (FAA) is reviewed. These include thermal neutron activation (TNA), fast neutron activation (FNA), the associated particle technique, nuclear resonance absorption, and photoneutron activation. The results of comprehensive airport testing of the TNA system performed during 1987-88 are summarized. From a technical point of view, nuclear-based techniques now represent the most comprehensive and feasible approach for meeting the operational criteria of detection, false alarms, and throughput. 9 refs., 5 figs., 2 tabs.

  9. Anoxic seizures: self-terminating syncopes.

    Science.gov (United States)

    Stephenson, J B

    2001-01-01

    This review focuses on anoxic seizures induced by self terminating syncopes in the young. Anoxic seizures are nonepileptic events consequent upon abrupt interruption of the energy supply to metabolically active cerebral neurones. Anoxic seizures are the most common paroxysmal events misdiagnosed as epilepsy. Neurally mediated syncopes have numerous appellations, especially in the young. This proliferation of terminology likely results from uncertainty regarding pathophysiology. The most important type of self-limiting syncope from the point of view of diagnostic difficulty has been called neurocardiogenic or vasovagal syncope and reflex anoxic seizure, amongst other names: this review includes a video clip of such a child with prolonged asystole. It also includes a detailed case history emphasising the feelings of a patient with this type of syncope who was misdiagnosed as having epilepsy for many years. The second class of self-terminating syncope discussed and illustrated on video is the so-called breath-holding spell of young children. The third example illustrated is the compulsive Valsalva manoeuvre of individuals with autistic spectrum disorder, in which anoxic seizures - as shown on the video clips - are easily misdiagnosed as epileptic seizures, with unfortunate consequences.

  10. Temperament and Mood Detection Using Case-Based Reasoning

    OpenAIRE

    Adebayo Kolawole John; Adekoya Adewale M.; Ekwonna Chinnasa

    2014-01-01

    Case-Based Reasoning (CBR) is a branch of AI that is employed to solving problems which emphasizes the use of previous solutions in solving similar new problems. This work presents TAMDS, a Temperament and Mood Detection system which employs Case-Based Reasoning technique. The proposed system is adapted to the field of psychology to help psychologists solve part of the problems in their complex domain. We have designed TAMDS to detect temperament and moods of individuals. A major aim of our s...

  11. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

  12. A Frequency-Based Approach to Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Mian Zhou

    2004-06-01

    Full Text Available Research on network security and intrusion detection strategies presents many challenging issues to both theoreticians and practitioners. Hackers apply an array of intrusion and exploit techniques to cause disruption of normal system operations, but on the defense, firewalls and intrusion detection systems (IDS are typically only effective in defending known intrusion types using their signatures, and are far less than mature when faced with novel attacks. In this paper, we adapt the frequency analysis techniques such as the Discrete Fourier Transform (DFT used in signal processing to the design of intrusion detection algorithms. We demonstrate the effectiveness of the frequency-based detection strategy by running synthetic network intrusion data in simulated networks using the OPNET software. The simulation results indicate that the proposed intrusion detection strategy is effective in detecting anomalous traffic data that exhibit patterns over time, which include several types of DOS and probe attacks. The significance of this new strategy is that it does not depend on the prior knowledge of attack signatures, thus it has the potential to be a useful supplement to existing signature-based IDS and firewalls.

  13. Analysis of Android Device-Based Solutions for Fall Detection.

    Science.gov (United States)

    Casilari, Eduardo; Luque, Rafael; Morón, María-José

    2015-07-23

    Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.

  14. Semantic-based technique for thai documents plagiarism detection

    Directory of Open Access Journals (Sweden)

    Sorawat Prapanitisatian

    2014-03-01

    Full Text Available Plagiarism is the act of taking another person's writing or idea without referring to the source of information. This is one of major problems in educational institutes. There is a number of plagiarism detection software available on the Internet. However, a few numbers of them works. Typically, they use a simple method for plagiarism detection e.g. string matching. The main weakness of this method is it cannot detect the plagiarism when the author replaces some words using synonyms. As such, this paper presents a new technique for a semantic-based plagiarism detection using Semantic Role Labeling (SRL and term weighting. SRL is deployed in order to calculate the semantic-based similarity. The main different from the existing framework is terms in a sentence are weighted dynamically depending on their roles in the sentence e.g. subject, verb or object. This technique enhances the plagiarism detection mechanism more efficiently than existing system although positions of terms in a sentence are reordered. The experimental results show that the proposed method can detect the plagiarism document more effective than the existing methods, Anti-kobpae, Turnit-in and Traditional Semantic Role Labeling.

  15. An Effective Conversation-Based Botnet Detection Method

    Directory of Open Access Journals (Sweden)

    Ruidong Chen

    2017-01-01

    Full Text Available A botnet is one of the most grievous threats to network security since it can evolve into many attacks, such as Denial-of-Service (DoS, spam, and phishing. However, current detection methods are inefficient to identify unknown botnet. The high-speed network environment makes botnet detection more difficult. To solve these problems, we improve the progress of packet processing technologies such as New Application Programming Interface (NAPI and zero copy and propose an efficient quasi-real-time intrusion detection system. Our work detects botnet using supervised machine learning approach under the high-speed network environment. Our contributions are summarized as follows: (1 Build a detection framework using PF_RING for sniffing and processing network traces to extract flow features dynamically. (2 Use random forest model to extract promising conversation features. (3 Analyze the performance of different classification algorithms. The proposed method is demonstrated by well-known CTU13 dataset and nonmalicious applications. The experimental results show our conversation-based detection approach can identify botnet with higher accuracy and lower false positive rate than flow-based approach.

  16. Accounting for detectability in fish distribution models: an approach based on time-to-first-detection

    Directory of Open Access Journals (Sweden)

    Mário Ferreira

    2015-12-01

    Full Text Available Imperfect detection (i.e., failure to detect a species when the species is present is increasingly recognized as an important source of uncertainty and bias in species distribution modeling. Although methods have been developed to solve this problem by explicitly incorporating variation in detectability in the modeling procedure, their use in freshwater systems remains limited. This is probably because most methods imply repeated sampling (≥ 2 of each location within a short time frame, which may be impractical or too expensive in most studies. Here we explore a novel approach to control for detectability based on the time-to-first-detection, which requires only a single sampling occasion and so may find more general applicability in freshwaters. The approach uses a Bayesian framework to combine conventional occupancy modeling with techniques borrowed from parametric survival analysis, jointly modeling factors affecting the probability of occupancy and the time required to detect a species. To illustrate the method, we modeled large scale factors (elevation, stream order and precipitation affecting the distribution of six fish species in a catchment located in north-eastern Portugal, while accounting for factors potentially affecting detectability at sampling points (stream depth and width. Species detectability was most influenced by depth and to lesser extent by stream width and tended to increase over time for most species. Occupancy was consistently affected by stream order, elevation and annual precipitation. These species presented a widespread distribution with higher uncertainty in tributaries and upper stream reaches. This approach can be used to estimate sampling efficiency and provide a practical framework to incorporate variations in the detection rate in fish distribution models.

  17. Computed tomography perfusion as a diagnostic tool for seizures after ischemic stroke

    Energy Technology Data Exchange (ETDEWEB)

    Koome, Miriam; Thevathasan, Arthur; Yan, Bernard [The Royal Melbourne Hospital, Melbourne Brain Centre, Parkville, VIC (Australia); Churilov, Leonid [University of Melbourne, Florey Neuroscience Institutes, Austin Health, Melbourne (Australia); Chen, Ziyuan [The Royal Melbourne Hospital, Melbourne Brain Centre, Parkville, VIC (Australia); Tsinghua University, School of Medicine, Beijing (China); Chen, Ziyi [The Royal Melbourne Hospital, Melbourne Brain Centre, Parkville, VIC (Australia); Sun Yat-Sen University, First Affiliated Hospital, Guangdong (China); Naylor, Jillian; Kwan, Patrick [The Royal Melbourne Hospital, Melbourne Brain Centre, Parkville, VIC (Australia); The University of Melbourne, Department of Medicine, Melbourne (Australia)

    2016-06-15

    Cerebral cortical ischemia is a risk factor for post-stroke seizures. However, the optimal imaging method is unclear. We investigated CT perfusion (CTP) in detecting cortical ischemia and its correlation with post-stroke seizures compared with non-contrast CT (NCCT). We included patients with acute ischemic stroke admitted to the Royal Melbourne Hospital between 2009 and 2014. Post-stroke seizure information was collected. Cortical involvement was determined on acute NCCT and CTP (T{sub max}, cerebral blood volume [CBV], and cerebral blood flow [CBF]). The association between cortical involvement detected by different imaging modalities and post-stroke seizures was examined. Three-hundred fifty-two patients were included for analysis. Fifty-nine percent were male, and median age was 73 years (inter-quartile range 61-82). Follow-up was available for 96 %; median follow-up duration was 377 days (inter-quartile range 91-1018 days). Thirteen patients had post-stroke seizures (3.9 %). Cortical involvement was significantly associated with post-stroke seizures across all modalities. CBV had the highest hazard ratio (11.3, 95 % confidence interval (CI) 1.1-41.2), followed by NCCT (5.3, 95 % CI 1.5-18.0) and CBF (4.2, 95 % CI 1.1-15.2). Sensitivity was highest for T{sub max} (100 %), followed by CBV and CBF (both 76.9 %) and NCCT (63.6 %). Specificity was highest for CBV (77.8 %), then NCCT (75.6 %), CBF (54.0 %), and T{sub max} (29.1 %). Receiver-operating characteristic area under the curve was significantly different between imaging modalities (p < 0.001), CBV 0.77, NCCT 0.70, CBF 0.65, and T{sub max} 0.65. CTP may improve sensitivity and specificity of cortical involvement for post-stroke seizures compared to NCCT. (orig.)

  18. A stereo vision-based obstacle detection system in vehicles

    Science.gov (United States)

    Huh, Kunsoo; Park, Jaehak; Hwang, Junyeon; Hong, Daegun

    2008-02-01

    Obstacle detection is a crucial issue for driver assistance systems as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision with the front vehicle. The vision-based obstacle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an obstacle detection system using stereo vision sensors is developed. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the obstacles. The proposed system can detect a front obstacle, a leading vehicle and a vehicle cutting into the lane. Then, the position parameters of the obstacles and leading vehicles can be obtained. The proposed obstacle detection system is implemented on a passenger car and its performance is verified experimentally.

  19. Ground-based detection of G star superflares with NGTS

    Science.gov (United States)

    Jackman, James A. G.; Wheatley, Peter J.; Pugh, Chloe E.; Gänsicke, Boris T.; Gillen, Edward; Broomhall, Anne-Marie; Armstrong, David J.; Burleigh, Matthew R.; Chaushev, Alexander; Eigmüller, Philipp; Erikson, Anders; Goad, Michael R.; Grange, Andrew; Günther, Maximilian N.; Jenkins, James S.; McCormac, James; Raynard, Liam; Thompson, Andrew P. G.; Udry, Stéphane; Walker, Simon; Watson, Christopher A.; West, Richard G.

    2018-04-01

    We present high cadence detections of two superflares from a bright G8 star (V = 11.56) with the Next Generation Transit Survey (NGTS). We improve upon previous superflare detections by resolving the flare rise and peak, allowing us to fit a solar flare inspired model without the need for arbitrary break points between rise and decay. Our data also enables us to identify substructure in the flares. From changing starspot modulation in the NGTS data we detect a stellar rotation period of 59 hours, along with evidence for differential rotation. We combine this rotation period with the observed ROSAT X-ray flux to determine that the star's X-ray activity is saturated. We calculate the flare bolometric energies as 5.4^{+0.8}_{-0.7}× 10^{34}and 2.6^{+0.4}_{-0.3}× 10^{34}erg and compare our detections with G star superflares detected in the Kepler survey. We find our main flare to be one of the largest amplitude superflares detected from a bright G star. With energies more than 100 times greater than the Carrington event, our flare detections demonstrate the role that ground-based instruments such as NGTS can have in assessing the habitability of Earth-like exoplanets, particularly in the era of PLATO.

  20. Colour based fire detection method with temporal intensity variation filtration

    International Nuclear Information System (INIS)

    Trambitckii, K; Musalimov, V; Anding, K; Linß, G

    2015-01-01

    Development of video, computing technologies and computer vision gives a possibility of automatic fire detection on video information. Under that project different algorithms was implemented to find more efficient way of fire detection. In that article colour based fire detection algorithm is described. But it is not enough to use only colour information to detect fire properly. The main reason of this is that in the shooting conditions may be a lot of things having colour similar to fire. A temporary intensity variation of pixels is used to separate them from the fire. These variations are averaged over the series of several frames. This algorithm shows robust work and was realised as a computer program by using of the OpenCV library