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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...... on the amplitude of the signal. The other algorithm was based on information of the signal in the frequency domain, and it focused on synchronisation of the electrical activity in a single muscle during the seizure. Results: The amplitude-based algorithm reliably detected seizures in 2 of the patients, while...... 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. Detection of tonic epileptic seizures based on surface electromyography

    DEFF Research Database (Denmark)

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

    2014-01-01

    The purpose of this project was to design an algorithm for detection of tonic seizures based on surface electromyography signals from the deltoids. A successful algorithm has a future prospect of being implemented in a wearable device as part of an alarm system. This has already been done......, 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...

  3. Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics

    Directory of Open Access Journals (Sweden)

    Jonatas Pavei

    2017-10-01

    Full Text Available Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecomposition of HRV parameter covariance matrices was used to create an input for a support vector machine (SVM-based classifier. We analyzed clinical data from 12 patients (9 female; 3 male; age 34.5 ± 7.5 years, involving 34 seizures and a total of 55.2 h of interictal electrocardiogram (ECG recordings. Data from 123.6 h of ECG recordings from healthy subjects were used to test false positive rate per hour (FP/h in a completely independent data set. Our methodological approach allowed the detection of impending seizures from 5 min to just before the onset of a clinical/electrical seizure with a sensitivity of 94.1%. The FP rate was 0.49 h−1 in the recordings from patients with epilepsy and 0.19 h−1 in the recordings from healthy subjects. Our results suggest that it is feasible to use the dynamics of HRV parameters for the early detection and, potentially, the prediction of epileptic seizures.

  4. Accelerometry based detection of epileptic seizures

    NARCIS (Netherlands)

    Nijsen, T.M.E.

    2008-01-01

    Epilepsy is one of the most common neurological disorders. Epileptic seizures are the manifestation of abnormal hypersynchronous discharges of cortical neurons that impair brain function. Most of the people affected can be treated successfully with drug therapy or neurosurgical procedures. But there

  5. Adaptive heart rate-based epileptic seizure detection using real-time user feedback

    DEFF Research Database (Denmark)

    De Cooman, Thomas; Kjær, Troels Wesenberg; Van Huffel, Sabine

    2017-01-01

    Automated seizure detection in a home environment has been of increased interest the last couple of decades. Heart rate-based seizure detection is a way to detect temporal lobe epilepsy seizures at home, but patient-independent algorithms showed to be insufficiently accurate due to the high patient...... with incorrect user feedback, making it ideal for implementation in a home environment for a seizure warning system....

  6. 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...... 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...... test subjects were used for this pilot project. Each subject simulated 15 seizures and in addition performed some predefined normal activities, during a 4-hour monitoring with electromyography (EMG), accelerometer, magnetometer and gyroscope (AMG), electrocardiography (ECG), electroencephalography (EEG...

  7. 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

  8. 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...... algorithm on motion data is extracting features as “log-sum” measures of discrete wavelet components. Classification into the two groups “seizure” versus “nonseizure” is made based on the support vector machine (SVM) algorithm. The algorithm performs with a sensitivity of 91-100%, a median latency of 1...... second and a specificity of 100% on multi-modal data from five healthy subjects simulating seizures. The uni-modal algorithm based on sEMG data from the subjects and patients performs satisfactorily in some cases. As expected, our results clearly show superiority of the multimodal approach, as compared...

  9. 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.

  10. An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG.

    Science.gov (United States)

    Orosco, Lorena; Laciar, Eric; Correa, Agustina Garces; Torres, Abel; Graffigna, Juan P

    2009-01-01

    Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates the energy of each IMF and performs the detection based on an energy threshold and a minimum duration decision. The algorithm was tested in 9 invasive EEG records provided and validated by the Epilepsy Center of the University Hospital of Freiburg. In 90 segments analyzed (39 with epileptic seizures) the sensitivity and specificity obtained with the method were of 56.41% and 75.86% respectively. It could be concluded that EMD is a promissory method for epileptic seizure detection in EEG records.

  11. Multi-modal intelligent seizure acquisition (MISA) system--a new approach towards seizure detection based on full body motion measures.

    Science.gov (United States)

    Conradsen, Isa; Beniczky, Sandor; Wolf, Peter; Terney, Daniella; Sams, Thomas; Sorensen, Helge B D

    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 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 test subjects were used for this pilot project. Each subject simulated 15 seizures and in addition performed some predefined normal activities, during a 4-hour monitoring with electromyography (EMG), accelerometer, magnetometer and gyroscope (AMG), electrocardiography (ECG), electroencephalography (EEG) 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 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.

  12. Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment.

    Science.gov (United States)

    Vandecasteele, Kaat; De Cooman, Thomas; Gu, Ying; Cleeren, Evy; Claes, Kasper; Paesschen, Wim Van; Huffel, Sabine Van; Hunyadi, Borbála

    2017-10-13

    Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.

  13. Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment

    Directory of Open Access Journals (Sweden)

    Kaat Vandecasteele

    2017-10-01

    Full Text Available Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG and photoplethysmography (PPG, are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.

  14. Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.

    Science.gov (United States)

    Yuan, Shasha; Zhou, Weidong; Wu, Qi; Zhang, Yanli

    2016-05-01

    Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.

  15. Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

    Science.gov (United States)

    Mesbah, Mostefa; Balakrishnan, Malarvili; Colditz, Paul B.; Boashash, Boualem

    2012-12-01

    This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

  16. 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...

  17. 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...... of an automatic channel selection method. The characteristics of the seizures are extracted by the use of a wavelet analysis and classified by a support vector machine. The best channel selection method is based upon maximum variance during the seizure. Results: Using only three channels, a seizure detection...... sensitivity of 96% and a false detection rate of 0.14/h were obtained. This corresponds to the performance obtained when channels are selected through visual inspection by a clinical neurophysiologist, and constitutes a 4% improvement in sensitivity compared to seizure detection using channels recorded...

  18. Diagnostic accuracy of audio-based seizure detection in patients with severe epilepsy and an intellectual disability

    NARCIS (Netherlands)

    Arends, Johan B.; van Dorp, Jasper; van Hoek, Dennis; Kramer, Niels; van Mierlo, Petra; van der Vorst, Derek; Tan, Francis I.Y.

    2016-01-01

    We evaluated the performance of audio-based detection of major seizures (tonic–clonic and long generalized tonic) in adult patients with intellectual disability living in an institute for residential care. Methods First, we checked in a random sample (n = 17, 102 major seizures) how many patients

  19. VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

    Science.gov (United States)

    Feng, Lichen; Li, Zunchao; Wang, Yuanfa

    2018-02-01

    Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the three-level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time-frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.

  20. 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.

  1. Probability of detection of clinical seizures using heart rate changes.

    Science.gov (United States)

    Osorio, Ivan; Manly, B F J

    2015-08-01

    Heart rate-based seizure detection is a viable complement or alternative to ECoG/EEG. This study investigates the role of various biological factors on the probability of clinical seizure detection using heart rate. Regression models were applied to 266 clinical seizures recorded from 72 subjects to investigate if factors such as age, gender, years with epilepsy, etiology, seizure site origin, seizure class, and data collection centers, among others, shape the probability of EKG-based seizure detection. Clinical seizure detection probability based on heart rate changes, is significantly (pprobability of detecting clinical seizures (>0.8 in the majority of subjects) using heart rate is highest for complex partial seizures, increases with a patient's years with epilepsy, is lower for females than for males and is unrelated to the side of hemisphere origin. Clinical seizure detection probability using heart rate is multi-factorially dependent and sufficiently high (>0.8) in most cases to be clinically useful. Knowledge of the role that these factors play in shaping said probability will enhance its applicability and usefulness. Heart rate is a reliable and practical signal for extra-cerebral detection of clinical seizures originating from or spreading to central autonomic network structures. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  2. LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

    Science.gov (United States)

    Zhang, Tao; Chen, Wanzhong

    2017-08-01

    Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

  3. Optimal Feature Space Selection in Detecting Epileptic Seizure based on Recurrent Quantification Analysis and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Saleh LAshkari

    2016-06-01

    Full Text Available Selecting optimal features based on nature of the phenomenon and high discriminant ability is very important in the data classification problems. Since it doesn't require any assumption about stationary condition and size of the signal and the noise in Recurrent Quantification Analysis (RQA, it may be useful for epileptic seizure Detection. In this study, RQA was used to discriminate ictal EEG from the normal EEG where optimal features selected by combination of algorithm genetic and Bayesian Classifier. Recurrence plots of hundred samples in each two categories were obtained with five distance norms in this study: Euclidean, Maximum, Minimum, Normalized and Fixed Norm. In order to choose optimal threshold for each norm, ten threshold of ε was generated and then the best feature space was selected by genetic algorithm in combination with a bayesian classifier. The results shown that proposed method is capable of discriminating the ictal EEG from the normal EEG where for Minimum norm and 0.1˂ε˂1, accuracy was 100%. In addition, the sensitivity of proposed framework to the ε and the distance norm parameters was low. The optimal feature presented in this study is Trans which it was selected in most feature spaces with high accuracy.

  4. 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.

  5. 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.

  6. Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

    Science.gov (United States)

    Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

    2018-06-25

    The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability

  7. Validation of an automated seizure detection algorithm for term neonates

    Science.gov (United States)

    Mathieson, Sean R.; Stevenson, Nathan J.; Low, Evonne; Marnane, William P.; Rennie, Janet M.; Temko, Andrey; Lightbody, Gordon; Boylan, Geraldine B.

    2016-01-01

    Objective The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. Methods EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed. Results Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6–75.0%, with false detection (FD) rates of 0.04–0.36 FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen’s Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures. Conclusion The SDA achieved promising performance and warrants further testing in a live clinical evaluation. Significance The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens. PMID:26055336

  8. Seizure detection using dynamic warping for patients with intellectual disability

    NARCIS (Netherlands)

    Wang, L.; Arends, J.B.A.M.; Long, X.; Wu, Y.; Cluitmans, P.J.M.

    2016-01-01

    Electroencephalography (EEG) is paramount for both retrospective analysis and real-time monitoring of epileptic seizures. Studies have shown that EEG-based seizure detection is very difficult for a specific epileptic population with intellectual disability due to the cerebral development disorders.

  9. 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.

  10. Automated seizure detection systems and their effectiveness for each type of seizure.

    Science.gov (United States)

    Ulate-Campos, A; Coughlin, F; Gaínza-Lein, M; Fernández, I Sánchez; Pearl, P L; Loddenkemper, T

    2016-08-01

    Epilepsy affects almost 1% of the population and most of the approximately 20-30% of patients with refractory epilepsy have one or more seizures per month. Seizure detection devices allow an objective assessment of seizure frequency and a treatment tailored to the individual patient. A rapid recognition and treatment of seizures through closed-loop systems could potentially decrease morbidity and mortality in epilepsy. However, no single detection device can detect all seizure types. Therefore, the choice of a seizure detection device should consider the patient-specific seizure semiologies. This review of the literature evaluates seizure detection devices and their effectiveness for different seizure types. Our aim is to summarize current evidence, offer suggestions on how to select the most suitable seizure detection device for each patient and provide guidance to physicians, families and researchers when choosing or designing seizure detection devices. Further, this review will guide future prospective validation studies. Copyright © 2016. Published by Elsevier Ltd.

  11. 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

    measures of reconstructed sub-bands from the discrete wavelet transformation (DWT) and the wavelet packet transformation (WPT). Based on the extracted features all data segments were classified using a support vector machine (SVM) algorithm as simulated seizure or normal activity. A case study...... of the seizure from the patient showed that the simulated seizures were visually similar to the epileptic one. The multi-modal intelligent seizure acquisition (MISA) system showed high sensitivity, short detection latency and low false detection rate. The results showed superiority of the multi- modal detection...... system compared to the uni-modal one. The presented system has a promising potential for seizure detection based on multi-modal data....

  12. Seizures

    Science.gov (United States)

    Secondary seizures; Reactive seizures; Seizure - secondary; Seizure - reactive; Convulsions ... or kidney failure Very high blood pressure ( malignant hypertension ) Venomous bites and stings ( snake bite ) Withdrawal from ...

  13. Improving staff response to seizures on the epilepsy monitoring unit with online EEG seizure detection algorithms.

    Science.gov (United States)

    Rommens, Nicole; Geertsema, Evelien; Jansen Holleboom, Lisanne; Cox, Fieke; Visser, Gerhard

    2018-05-11

    User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time. EEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU. EEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h. EEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures. Copyright © 2018. Published by Elsevier Inc.

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

    Science.gov (United States)

    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.

  15. Extended seizure detection algorithm for intracranial EEG recordings

    DEFF Research Database (Denmark)

    Kjaer, T. W.; Remvig, L. S.; Henriksen, J.

    2010-01-01

    Objective: We implemented and tested an existing seizure detection algorithm for scalp EEG (sEEG) with the purpose of improving it to intracranial EEG (iEEG) recordings. Method: iEEG was obtained from 16 patients with focal epilepsy undergoing work up for resective epilepsy surgery. Each patient...... had 4 or 5 recorded seizures and 24 hours of non-ictal data were used for evaluation. Data from three electrodes placed at the ictal focus were used for the analysis. A wavelet based feature extraction algorithm delivered input to a support vector machine (SVM) classifier for distinction between ictal...... and non-ictal iEEG. We compare our results to a method published by Shoeb in 2004. While the original method on sEEG was optimal with the use of only four subbands in the wavelet analysis, we found that better seizure detection could be made if all subbands were used for iEEG. Results: When using...

  16. Non-EEG based ambulatory seizure detection designed for home use : What is available and how will it influence epilepsy care?

    NARCIS (Netherlands)

    van Andel, Judith; Thijs, Roland D.; de Weerd, Al; Arends, Johan; Leijten, Frans

    OBJECTIVE: This study aimed to (1) evaluate available systems and algorithms for ambulatory automatic seizure detection and (2) discuss benefits and disadvantages of seizure detection in epilepsy care. METHODS: PubMed and EMBASE were searched up to November 2014, using variations and synonyms of

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

    DEFF Research Database (Denmark)

    Conradsen, Isa

    convulsive seizures tested. Another study was performed, involving quantitative parameters in the time and frequency domain. The study showed, that there are several differences between tonic seizures and the tonic phase of GTC seizures and furthermore revealed differences of the epileptic (tonic and tonic...... phase of GTC) and simulated seizures. This was valuable information concerning a seizure detection algorithm, and the findings from this research provided evidence for a change in the definition of these seizures by the International League Against Epilepsy (ILAE). Our final study presents a novel...

  18. Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

    2018-04-02

    Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

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

    NARCIS (Netherlands)

    Wang, Lei; Long, Xi; Arends, J.B.A.M.; Aarts, R.M.

    2017-01-01

    Background 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. New method A single-channel

  20. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy.

    Science.gov (United States)

    Ramgopal, Sriram; Thome-Souza, Sigride; Jackson, Michele; Kadish, Navah Ester; Sánchez Fernández, Iván; Klehm, Jacquelyn; Bosl, William; Reinsberger, Claus; Schachter, Steven; Loddenkemper, Tobias

    2014-08-01

    Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy. Copyright © 2014. Published by Elsevier Inc.

  1. Epileptic Seizure Detection based on Wavelet Transform Statistics Map and EMD Method for Hilbert-Huang Spectral Analyzing in Gamma Frequency Band of EEG Signals

    Directory of Open Access Journals (Sweden)

    Morteza Behnam

    2015-08-01

    Full Text Available Seizure detection using brain signal (EEG analysis is the important clinical methods in drug therapy and the decisions before brain surgery. In this paper, after signal conditioning using suitable filtering, the Gamma frequency band has been extracted and the other brain rhythms, ambient noises and the other bio-signal are canceled. Then, the wavelet transform of brain signal and the map of wavelet transform in multi levels are computed. By dividing the color map to different epochs, the histogram of each sub-image is obtained and the statistics of it based on statistical momentums and Negentropy values are calculated. Statistical feature vector using Principle Component Analysis (PCA is reduced to one dimension. By EMD algorithm and sifting procedure for analyzing the data by Intrinsic Mode Function (IMF and computing the residues of brain signal using spectrum of Hilbert transform and Hilbert – Huang spectrum forming, one spatial feature based on the Euclidian distance for signal classification is obtained. By K-Nearest Neighbor (KNN classifier and by considering the optimal neighbor parameter, EEG signals are classified in two classes, seizure and non-seizure signal, with the rate of accuracy 76.54% and with variance of error 0.3685 in the different tests.

  2. 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...... study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures....

  3. Automatic multimodal detection for long-term seizure documentation in epilepsy.

    Science.gov (United States)

    Fürbass, F; Kampusch, S; Kaniusas, E; Koren, J; Pirker, S; Hopfengärtner, R; Stefan, H; Kluge, T; Baumgartner, C

    2017-08-01

    This study investigated sensitivity and false detection rate of a multimodal automatic seizure detection algorithm and the applicability to reduced electrode montages for long-term seizure documentation in epilepsy patients. An automatic seizure detection algorithm based on EEG, EMG, and ECG signals was developed. EEG/ECG recordings of 92 patients from two epilepsy monitoring units including 494 seizures were used to assess detection performance. EMG data were extracted by bandpass filtering of EEG signals. Sensitivity and false detection rate were evaluated for each signal modality and for reduced electrode montages. All focal seizures evolving to bilateral tonic-clonic (BTCS, n=50) and 89% of focal seizures (FS, n=139) were detected. Average sensitivity in temporal lobe epilepsy (TLE) patients was 94% and 74% in extratemporal lobe epilepsy (XTLE) patients. Overall detection sensitivity was 86%. Average false detection rate was 12.8 false detections in 24h (FD/24h) for TLE and 22 FD/24h in XTLE patients. Utilization of 8 frontal and temporal electrodes reduced average sensitivity from 86% to 81%. Our automatic multimodal seizure detection algorithm shows high sensitivity with full and reduced electrode montages. Evaluation of different signal modalities and electrode montages paces the way for semi-automatic seizure documentation systems. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  4. Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals

    Science.gov (United States)

    Feltane, Amal; Boudreaux-Bartels, G. Faye; Besio, Walter

    2012-01-01

    Automated detection of seizures is still a challenging problem. This study presents an approach to detect seizure segments in Laplacian electroencephalography (tEEG) recorded from rats using the tripolar concentric ring electrode (TCRE) configuration. Three features, namely, median absolute deviation, approximate entropy, and maximum singular value were calculated and used as inputs into two different classifiers: support vector machines and adaptive boosting. The relative performance of the extracted features on TCRE tEEG was examined. Results are obtained with an overall accuracy between 84.81 and 96.51%. In addition to using TCRE tEEG data, the seizure detection algorithm was also applied to the recorded EEG signals from Andrzejak et al. database to show the efficiency of the proposed method for seizure detection. PMID:23073989

  5. Unsupervised EEG analysis for automated epileptic seizure detection

    Science.gov (United States)

    Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

    2016-07-01

    Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

  6. Automatic Epileptic Seizure Onset Detection Using Matching Pursuit

    DEFF Research Database (Denmark)

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

    2010-01-01

    . The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial...... electroencephalography (iEEG). A sensitivity of 78-100% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature xtractor for detection of epileptic seizures....

  7. A novel seizure detection algorithm informed by hidden Markov model event states

    Science.gov (United States)

    Baldassano, Steven; Wulsin, Drausin; Ung, Hoameng; Blevins, Tyler; Brown, Mesha-Gay; Fox, Emily; Litt, Brian

    2016-06-01

    Objective. Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model. Approach. We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control. Main results. Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h-1). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)). Significance. This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.

  8. 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.

  9. Phenobarbital reduces EEG amplitude and propagation of neonatal seizures but does not alter performance of automated seizure detection.

    Science.gov (United States)

    Mathieson, Sean R; Livingstone, Vicki; Low, Evonne; Pressler, Ronit; Rennie, Janet M; Boylan, Geraldine B

    2016-10-01

    Phenobarbital increases electroclinical uncoupling and our preliminary observations suggest it may also affect electrographic seizure morphology. This may alter the performance of a novel seizure detection algorithm (SDA) developed by our group. The objectives of this study were to compare the morphology of seizures before and after phenobarbital administration in neonates and to determine the effect of any changes on automated seizure detection rates. The EEGs of 18 term neonates with seizures both pre- and post-phenobarbital (524 seizures) administration were studied. Ten features of seizures were manually quantified and summary measures for each neonate were statistically compared between pre- and post-phenobarbital seizures. SDA seizure detection rates were also compared. Post-phenobarbital seizures showed significantly lower amplitude (pphenobarbital reduces both the amplitude and propagation of seizures which may help to explain electroclinical uncoupling of seizures. The seizure detection rate of the algorithm was unaffected by these changes. The results suggest that users should not need to adjust the SDA sensitivity threshold after phenobarbital administration. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  10. Seizures

    Science.gov (United States)

    ... wake up between them. Seizures can have many causes, including medicines, high fevers, head injuries and certain diseases. People who have recurring seizures due to a brain disorder have epilepsy. NIH: National Institute of Neurological Disorders and Stroke

  11. Seizures

    Science.gov (United States)

    ... may be diagnosed with epilepsy , also known as seizure disorder. Seizure Basics Usually, electrical activity in the brain involves ... times. Fortunately, fainting rarely is a sign of epilepsy. Most kids recover very quickly (seconds to minutes) ...

  12. 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.

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

    KAUST Repository

    Rana, Puneet; Lipor, John; Lee, Hyong; Van Drongelen, Wim; Kohrman, Michael H.; Van Veen, Barry D.

    2012-01-01

    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.

  14. 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.

  15. Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model.

    Science.gov (United States)

    Zwanenburg, Alex; Andriessen, Peter; Jellema, Reint K; Niemarkt, Hendrik J; Wolfs, Tim G A M; Kramer, Boris W; Delhaas, Tammo

    2015-03-01

    Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection algorithm performance for short seizures through the use of trend templates for seizure onset and end. Bipolar EEG were recorded within a transiently asphyxiated ovine model at 0.7 gestational age, a common experimental model for studying brain development in humans of 30-34 weeks of gestation. Transient asphyxia led to electrographic seizures within 6-8 h. A total of 3159 seizures, 2386 shorter than one minute, were annotated in 1976 h-long EEG recordings from 17 foetal lambs. To capture EEG characteristics, five features, sensitive to seizures, were calculated and used to derive trend information. Feature values and trend information were used as input for support vector machine classification and subsequently post-processed. Performance metrics, calculated after post-processing, were compared between analyses with and without employing trend information. Detector performance was assessed after five-fold cross-validation conducted ten times with random splits. The use of trend templates for seizure onset and end in a neonatal seizure detection algorithm significantly improves the correct detection of short seizures using two-channel EEG recordings from 54.3% (52.6-56.1) to 59.5% (58.5-59.9) at FDR 2.0 (median (range); p seizures by EEG monitoring at the NICU.

  16. Detection and Prediction of Epileptic Seizures

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas

    % from 16 cm2. The coherences of different frequency bands below 16 Hz all seem to have similar declines as a function of the Euclidean distance between channels. Frequencies between 16 and 30 Hz have a steeper decline and will only show coherent parts to cortical channels within 60 cm2....... There is no coherence for frequencies above 30 Hz at any distance. A lot of patients with epilepsy still struggle with a dreadful fear of suddenly having a seizure. The current PhD study identified topics where an EEG monitor could provide improvement in the patient’s quality of life. By algorithm development...

  17. Sensor integration of multiple tripolar concentric ring electrodes improves pentylenetetrazole-induced seizure onset detection in rats.

    Science.gov (United States)

    Makeyev, Oleksandr; Ding, Quan; Kay, Steven M; Besio, Walter G

    2012-01-01

    As epilepsy affects approximately one percent of the world population, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Previously, we applied noninvasive transcranial focal stimulation via tripolar concentric ring electrodes on the scalp of rats after inducing seizures with pentylenetetrazole. We developed a system to detect seizures and automatically trigger the stimulation and evaluated the system on the electrographic activity from rats. In this preliminary study we propose and validate a novel seizure onset detection algorithm based on exponentially embedded family. Unlike the previously proposed approach it integrates the data from multiple electrodes allowing an improvement of the detector performance.

  18. Multiple sensor integration for seizure onset detection in human patients comparing conventional disc versus novel tripolar concentric ring electrodes.

    Science.gov (United States)

    Makeyev, Oleksandr; Ding, Quan; Martínez-Juárez, Iris E; Gaitanis, John; Kay, Steven M; Besio, Walter G

    2013-01-01

    As epilepsy affects approximately one percent of the world population, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Closed-loop systems that apply electrical stimulation when seizure onset is automatically detected require high accuracy of automatic seizure detection based on electrographic brain activity. To improve this accuracy we propose to use noninvasive tripolar concentric ring electrodes that have been shown to have significantly better signal-to-noise ratio, spatial selectivity, and mutual information compared to conventional disc electrodes. The proposed detection methodology is based on integration of multiple sensors using exponentially embedded family (EEF). In this preliminary study it is validated on over 26.3 hours of data collected using both tripolar concentric ring and conventional disc electrodes concurrently each from 7 human patients with epilepsy including five seizures. For a cross-validation based group model EEF correctly detected 100% and 80% of seizures respectively with tripolar concentric ring electrodes.

  19. 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

    were analyzed from 11 consecutive patients. Our method is based on a high-pass filtering with a cutoff at 150 Hz, and monitoring a count of zero crossings with a hysteresis of $\\pm 50\\,\\mu \\hbox{V}$ . Based on data from one sEMG electrode (on the deltoid muscle), we achieved a sensitivity of 100...

  20. 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.

  1. An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

    Science.gov (United States)

    Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

    2016-02-01

    Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

  2. Seizure pattern-specific epileptic epoch detection in patients with intellectual disability

    NARCIS (Netherlands)

    Wang, L.; Arends, J.B.A.M.; Long, X.; Cluitmans, P.J.M.; van Dijk, J.P.

    Electroencephalogram (EEG) features are crucial for the seizure detection performance. Traditional algorithms are designed for a population with normal brain development. However, for patients with an intellectual disability the seizure detection performance is still largely unknown. In addition,

  3. 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.

  4. Febrile seizures: a population-based study

    Directory of Open Access Journals (Sweden)

    Juliane S. Dalbem

    2015-11-01

    Full Text Available Objectives: To determine the prevalence of benign febrile seizures of childhood and describe the clinical and epidemiological profile of this population. Methods: This was a population-based, cross-sectional study, carried out in the city of Barra do Bugres, MT, Brazil, from August 2012 to August 2013. Data were collected in two phases. In the first phase, a questionnaire that was previously validated in another Brazilian study was used to identify suspected cases of seizures. In the second phase, a neurological evaluation was performed to confirm diagnosis. Results: The prevalence was 6.4/1000 inhabitants (95% CI: 3.8–10.1. There was no difference between genders. Simple febrile seizures were found in 88.8% of cases. A family history of febrile seizures in first-degree relatives and history of epilepsy was present in 33.3% and 11.1% of patients, respectively. Conclusions: The prevalence of febrile seizures in Midwestern Brazil was lower than that found in other Brazilian regions, probably due to the inclusion only of febrile seizures with motor manifestations and differences in socioeconomic factors among the evaluated areas. Resumo: Objetivos: Estabelecer a prevalência das crises febris e descrever o perfil clínico e epidemiológico dessa população. Métodos: Estudo transversal de base populacional realizado na cidade de Barra do Bugres (MT, no período de agosto de 2012 a agosto de 2013. Os dados foram coletados em duas etapas. Na primeira fase utilizamos um questionário validado previamente em outro estudo brasileiro, para identificação de casos suspeitos de crises epilépticas. Na segunda etapa realizamos a avaliação neuroclínica para confirmação diagnóstica. Resultados: A prevalência de crise febril foi de 6,4/1000 habitantes (IC95% 3,8; 10,1. Não houve diferença entre os sexos. As crises febris simples foram encontradas em 88,8% dos casos. A história familiar de crise febril e epilepsia em parentes de 1° grau esteve

  5. Automatic seizure detection: going from sEEG to iEEG

    DEFF Research Database (Denmark)

    Henriksen, Jonas; Remvig, Line Sofie; Madsen, Rasmus Elsborg

    2010-01-01

    Several different algorithms have been proposed for automatic detection of epileptic seizures based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours...... of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet transformation (WT) features and classified by a support vector machine. When implementing a method used for sEEG on iEEG data, a great improvement in performance was obtained when the high...... frequency containing lower levels in the WT were included in the analysis. We were able to obtain a sensitivity of 96.4% and a false detection rate (FDR) of 0.20/h. In general, when implementing an automatic seizure detection algorithm made for sEEG on iEEG, great improvement can be obtained if a frequency...

  6. The impact of signal normalization on seizure detection using line length features.

    Science.gov (United States)

    Logesparan, Lojini; Rodriguez-Villegas, Esther; Casson, Alexander J

    2015-10-01

    Accurate automated seizure detection remains a desirable but elusive target for many neural monitoring systems. While much attention has been given to the different feature extractions that can be used to highlight seizure activity in the EEG, very little formal attention has been given to the normalization that these features are routinely paired with. This normalization is essential in patient-independent algorithms to correct for broad-level differences in the EEG amplitude between people, and in patient-dependent algorithms to correct for amplitude variations over time. It is crucial, however, that the normalization used does not have a detrimental effect on the seizure detection process. This paper presents the first formal investigation into the impact of signal normalization techniques on seizure discrimination performance when using the line length feature to emphasize seizure activity. Comparing five normalization methods, based upon the mean, median, standard deviation, signal peak and signal range, we demonstrate differences in seizure detection accuracy (assessed as the area under a sensitivity-specificity ROC curve) of up to 52 %. This is despite the same analysis feature being used in all cases. Further, changes in performance of up to 22 % are present depending on whether the normalization is applied to the raw EEG itself or directly to the line length feature. Our results highlight the median decaying memory as the best current approach for providing normalization when using line length features, and they quantify the under-appreciated challenge of providing signal normalization that does not impair seizure detection algorithm performance.

  7. 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.

  8. Models and detection of spontaneous recurrent seizures in laboratory rodents

    Directory of Open Access Journals (Sweden)

    Bin Gu

    2017-07-01

    Full Text Available Epilepsy, characterized by spontaneous recurrent seizures (SRS, is a serious and common neurological disorder afflicting an estimated 1% of the population worldwide. Animal experiments, especially those utilizing small laboratory rodents, remain essential to understanding the fundamental mechanisms underlying epilepsy and to prevent, diagnose, and treat this disease. While much attention has been focused on epileptogenesis in animal models of epilepsy, there is little discussion on SRS, the hallmark of epilepsy. This is in part due to the technical difficulties of rigorous SRS detection. In this review, we comprehensively summarize both genetic and acquired models of SRS and discuss the methodology used to monitor and detect SRS in mice and rats.

  9. Evaluation of a novel median power spectrogram for seizure detection by non-neurophysiologists.

    Science.gov (United States)

    Yan, Peter; Melman, Tamar; Yan, Sherry; Otgonsuren, Munkhzul; Grinspan, Zachary

    2017-08-01

    (1) To evaluate how well resident physicians use a novel EEG spectral analysis tool (the median power spectrogram; MPS) to detect seizures. (2) To assess the capability of the MPS to identify different seizure types. 120 EEG records from children with intractable seizures were converted to MPS by taking the median power across leads and using multi-taper spectral estimation. Twelve blinded neurology residents were trained to interpret the spectrogram with a five-minute video tutorial and post-test. Two residents independently assessed each set for presence of seizures. Their performance was compared to seizures identified using conventional EEG. Two blinded neurologists separately reviewed the EEGs and spectrograms to independently categorize the seizures. Their results were used to determine the spectrogram's capability to reveal seizures and visualize different seizure types for the user. Three key MPS features distinguished seizures from inter-ictal background: power difference relative to background, down-sloping resonance bands, and power in high frequencies. Using these features, residents identified seizures with 77% sensitivity and 72% specificity. 86% (51/59) of focal seizures and 81% (22/27) of generalized seizures were detected by at least one resident. Missed seizures included brief (seizures, tonic seizures, seizures with predominant delta (0-4Hz) activity, and seizures evident primarily in supplementary low temporal leads. The MPS is a novel qEEG modality that requires minimal training to interpret. It enables physicians without extensive neurophysiology training to identify seizures with sensitivity and specificity comparable to more complex multi-modal qEEG displays. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  10. Non-EEG seizure detection systems and potential SUDEP prevention: State of the art: Review and update.

    Science.gov (United States)

    Van de Vel, Anouk; Cuppens, Kris; Bonroy, Bert; Milosevic, Milica; Jansen, Katrien; Van Huffel, Sabine; Vanrumste, Bart; Cras, Patrick; Lagae, Lieven; Ceulemans, Berten

    2016-10-01

    Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Moreover, the boom of smart phones and tablets created a new market for seizure detection applications. We performed a thorough literature review and had contact with manufacturers of commercially available devices. This review article gives an updated overview of body signals and methods for seizure detection, international research and (commercially) available systems and applications. Reported results of non-EEG based detection devices vary between 2.2% and 100% sensitivity and between 0 and 3.23 false detections per hour compared to the gold standard video-EEG, for seizures ranging from generalized to convulsive or non-convulsive focal seizures with or without loss of consciousness. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP (sudden unexpected death in epilepsy), and of movement, as many seizures have a motor component. Comparison of research results is difficult as studies focus on different seizure types, timing (night versus day) and patients (adult versus pediatric patients). Nevertheless, we are convinced that the most effective seizure detection systems are multimodal, combining for example detection methods for movement and heart rate, and that devices should especially take into account the user's seizure types and personal preferences. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  11. Early seizure detection in an animal model of temporal lobe epilepsy

    Science.gov (United States)

    Talathi, Sachin S.; Hwang, Dong-Uk; Ditto, William; Carney, Paul R.

    2007-11-01

    The performance of five seizure detection schemes, i.e., Nonlinear embedding delay, Hurst scaling, Wavelet Scale, autocorrelation and gradient of accumulated energy, in their ability to detect EEG seizures close to the seizure onset time were evaluated to determine the feasibility of their application in the development of a real time closed loop seizure intervention program (RCLSIP). The criteria chosen for the performance evaluation were, high statistical robustness as determined through the predictability index, the sensitivity and the specificity of a given measure to detect an EEG seizure, the lag in seizure detection with respect to the EEG seizure onset time, as determined through visual inspection and the computational efficiency for each detection measure. An optimality function was designed to evaluate the overall performance of each measure dependent on the criteria chosen. While each of the above measures analyzed for seizure detection performed very well in terms of the statistical parameters, the nonlinear embedding delay measure was found to have the highest optimality index due to its ability to detect seizure very close to the EEG seizure onset time, thereby making it the most suitable dynamical measure in the development of RCLSIP in rat model with chronic limbic epilepsy.

  12. 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 seizure detection algorithm and the cutoff values were prespecified. A total of 71 patients, referred to long-term video-EEG monitoring, on suspicion of GTCS, were recruited in 3 centers. Seizure detection was real-time and fully automated. The reference standard was the evaluation of video-EEG recordings...

  13. 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.

  14. A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

    Science.gov (United States)

    Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Batatia, Hadj; Risk, Marcelo

    2016-04-01

    Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.

  15. A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications.

    Science.gov (United States)

    Raghunathan, Shriram; Gupta, Sumeet K; Markandeya, Himanshu S; Roy, Kaushik; Irazoqui, Pedro P

    2010-10-30

    Implantable neural prostheses that deliver focal electrical stimulation upon demand are rapidly emerging as an alternate therapy for roughly a third of the epileptic patient population that is medically refractory. Seizure detection algorithms enable feedback mechanisms to provide focally and temporally specific intervention. Real-time feasibility and computational complexity often limit most reported detection algorithms to implementations using computers for bedside monitoring or external devices communicating with the implanted electrodes. A comparison of algorithms based on detection efficacy does not present a complete picture of the feasibility of the algorithm with limited computational power, as is the case with most battery-powered applications. We present a two-dimensional design optimization approach that takes into account both detection efficacy and hardware cost in evaluating algorithms for their feasibility in an implantable application. Detection features are first compared for their ability to detect electrographic seizures from micro-electrode data recorded from kainate-treated rats. Circuit models are then used to estimate the dynamic and leakage power consumption of the compared features. A score is assigned based on detection efficacy and the hardware cost for each of the features, then plotted on a two-dimensional design space. An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost. The methods presented in this paper would facilitate the development of a common platform to benchmark seizure detection algorithms for comparison and feasibility analysis in the next generation of implantable neuroprosthetic devices to treat epilepsy. Copyright © 2010 Elsevier B.V. All rights reserved.

  16. 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.

  17. Amplitude Integrated Electroencephalography Compared With Conventional Video EEG for Neonatal Seizure Detection: A Diagnostic Accuracy Study.

    Science.gov (United States)

    Rakshasbhuvankar, Abhijeet; Rao, Shripada; Palumbo, Linda; Ghosh, Soumya; Nagarajan, Lakshmi

    2017-08-01

    This diagnostic accuracy study compared the accuracy of seizure detection by amplitude-integrated electroencephalography with the criterion standard conventional video EEG in term and near-term infants at risk of seizures. Simultaneous recording of amplitude-integrated EEG (2-channel amplitude-integrated EEG with raw trace) and video EEG was done for 24 hours for each infant. Amplitude-integrated EEG was interpreted by a neonatologist; video EEG was interpreted by a neurologist independently. Thirty-five infants were included in the analysis. In the 7 infants with seizures on video EEG, there were 169 seizure episodes on video EEG, of which only 57 were identified by amplitude-integrated EEG. Amplitude-integrated EEG had a sensitivity of 33.7% for individual seizure detection. Amplitude-integrated EEG had an 86% sensitivity for detection of babies with seizures; however, it was nonspecific, in that 50% of infants with seizures detected by amplitude-integrated EEG did not have true seizures by video EEG. In conclusion, our study suggests that amplitude-integrated EEG is a poor screening tool for neonatal seizures.

  18. 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.

  19. 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...

  20. Evaluation of novel algorithm embedded in a wearable sEMG device for seizure detection

    DEFF Research Database (Denmark)

    Conradsen, Isa; Beniczky, Sandor; Wolf, Peter

    2012-01-01

    We implemented a modified version of a previously published algorithm for detection of generalized tonic-clonic seizures into a prototype wireless surface electromyography (sEMG) recording device. The method was modified to require minimum computational load, and two parameters were trained...... on prior sEMG data recorded with the device. Along with the normal sEMG recording, the device is able to set an alarm whenever the implemented algorithm detects a seizure. These alarms are annotated in the data file along with the signal. The device was tested at the Epilepsy Monitoring Unit (EMU......) at the Danish Epilepsy Center. Five patients were included in the study and two of them had generalized tonic-clonic seizures. All patients were monitored for 2–5 days. A double-blind study was made on the five patients. The overall result showed that the device detected four of seven seizures and had a false...

  1. 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.

  2. Non-parametric early seizure detection in an animal model of temporal lobe epilepsy

    Science.gov (United States)

    Talathi, Sachin S.; Hwang, Dong-Uk; Spano, Mark L.; Simonotto, Jennifer; Furman, Michael D.; Myers, Stephen M.; Winters, Jason T.; Ditto, William L.; Carney, Paul R.

    2008-03-01

    The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.

  3. 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.

  4. Epileptic seizure detection in EEG signal with GModPCA and support vector machine.

    Science.gov (United States)

    Jaiswal, Abeg Kumar; Banka, Haider

    2017-01-01

    Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space

  5. 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.

  6. Exploring the capability of wireless near infrared spectroscopy as a portable seizure detection device for epilepsy patients.

    Science.gov (United States)

    Jeppesen, Jesper; Beniczky, Sándor; Johansen, Peter; Sidenius, Per; Fuglsang-Frederiksen, Anders

    2015-03-01

    Near infrared spectroscopy (NIRS) has proved useful in measuring significant hemodynamic changes in the brain during epileptic seizures. The advance of NIRS-technology into wireless and portable devices raises the possibility of using the NIRS-technology for portable seizure detection. This study used NIRS to measure changes in oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT) at left and right side of the frontal lobe in 33 patients with epilepsy undergoing long-term video-EEG monitoring. Fifteen patients had 34 focal seizures (20 temporal-, 11 frontal-, 2 parietal-lobe, one unspecific) recorded and analyzed with NIRS. Twelve parameters consisting of maximum increase and decrease changes of HbO, HbR and HbT during seizures (1 min before- to 3 min after seizure-onset) for left and right side, were compared with the patients' own non-seizure periods (a 2-h period and a 30-min exercise-period). In both non-seizure periods 4 min moving windows with maximum overlapping were applied to find non-seizure maxima of the 12 parameters. Detection was defined as positive when seizure maximum change exceeded non-seizure maximum change. When analyzing the 12 parameters separately the positive seizure detection was in the range of 6-24%. The increase in hemodynamics was in general better at detecting seizures (15-24%) than the decrease in hemodynamics (6-18%) (P=0.02). NIRS did not seem to be a suitable technology for generic seizure detection given the device, settings, and methods used in this study. There are still several challenges to overcome before the NIRS-technology can be used as a home-monitoring seizure detection device. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  7. 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.

  8. TOWARDS A PERSONALIZED REAL-TIME DIAGNOSIS IN NEONATAL SEIZURE DETECTION

    DEFF Research Database (Denmark)

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

    2017-01-01

    recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 FD/h to 0.2 FD/h. This is the first...

  9. 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.

  10. Epileptic seizures in patients with glioma: A single centre- based ...

    African Journals Online (AJOL)

    were used for analysis of seizure incidence differences as per WHO Grades, histology, location ... Keywords: Brain tumour, Epilepsy, Glioma, Seizures, Levetiracetam, .... glioma patients. Characteristics. N (%). Gender. Male. Female. Histology.

  11. 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.

  12. 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...

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

    DEFF Research Database (Denmark)

    Henriksen, Jonas

    is approaching. The primary obstacle is the lack of sufficient large databases to make a patient-specific algorithm rather than a “one-size-fits-all” approach. At Rigshospitalet, a research project is carried out that aims at collecting enough data to be able to do this. The next couple of years will probably...... 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...

  14. 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.

  15. Refractory seizures due to a dural-based cavernoma masquerading as a meningioma.

    Science.gov (United States)

    Zeng, Xianwei; Mahta, Ali; Kim, Ryan Y; Saad, Ali G; Kesari, Santosh

    2012-04-01

    A 37-year-old female presented with medically intractable complex partial seizures with secondary generalization. She was found to have a dural-based lesion with radiologic features of meningioma. A gross total resection was performed and pathology confirmed a diagnosis of cavernous angioma and she became seizure free after the surgical resection. Cavernous angioma should be considered in differential diagnosis of a dural-based lesion manifesting with refractory seizures.

  16. Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

    Science.gov (United States)

    Fergus, Paul; Hignett, David; Hussain, Abir; Al-Jumeily, Dhiya; Abdel-Aziz, Khaled

    2015-01-01

    The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders.

  17. SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

    Science.gov (United States)

    Cherkassky, Vladimir; Lee, Jieun; Veber, Brandon; Patterson, Edward E.; Brinkmann, Benjamin H.; Worrell, Gregory A.

    2017-01-01

    Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages. PMID:27362758

  18. GC-MS-Based metabolomics discovers a shared serum metabolic characteristic among three types of epileptic seizures.

    Science.gov (United States)

    Wang, Dian; Wang, Xingxing; Kong, Jing; Wu, Jiayan; Lai, Minchao

    2016-10-01

    Understanding the overall and common metabolic changes of seizures can provide novel clues for their control and prevention. Here, we aim to investigate the global metabolic feature of serum for three types of seizures. We recruited 27 patients who had experienced a seizure within 48h (including 11 who had a generalized seizure, nine who had a generalized seizure secondary to partial seizure and seven who had a partial seizure) and 23 healthy controls. We analyzed the global metabolic changes of serum after seizures using gas chromatography-mass spectrometry-based metabolomics. Based on differential metabolites, the metabolic pathways and their potential to diagnose seizures were analyzed, and metabolic differences among three types of seizures were compared. The metabolic profiles of serum were distinctive between the seizure group and the controls but were not different among the three types of seizures. Compared to the controls, patients with seizures had higher levels of lactate, butanoic acid, proline and glutamate and lower levels of palmitic acid, linoleic acid, elaidic acid, trans-13-octadecenoic acid, stearic acid, citrate, cysteine, glutamine, asparagine, and glyceraldehyde in the serum. Furthermore, these differential metabolites had common change trends among the three types of seizures. Related pathophysiological processes reflected by these metabolites are energy deficit, inflammation, nervous excitation and neurotoxicity. Importantly, transamination inhibition is suspected to occur in seizures. Lactate, glyceraldehyde and trans-13-octadecenoic acid in serum jointly enabled a precision of 92.9% for diagnosing seizures. There is a common metabolic feature in three types of seizures. Lactate, glyceraldehyde and trans-13-octadecenoic acid levels jointly enable high-precision seizure diagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. 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.

  20. Pre-hospital care after a seizure: Evidence base and United Kingdom management guidelines.

    Science.gov (United States)

    Osborne, Andrew; Taylor, Louise; Reuber, Markus; Grünewald, Richard A; Parkinson, Martin; Dickson, Jon M

    2015-01-01

    Seizures are a common presentation to pre-hospital emergency services and they generate significant healthcare costs. This article summarises the United Kingdom (UK) Ambulance Service guidelines for the management of seizures and explores the extent to which these guidelines are evidence-based. Summary of the Clinical Practice Guidelines of the UK Joint Royal Colleges Ambulance Liaison Committee relating to the management of seizures. Review of the literature relating to pre-hospital management of seizure emergencies. Much standard practice relating to the emergency out of hospital management of patients with seizures is drawn from generic Advanced Life Support (ALS) guidelines although many patients do not need ALS during or after a seizure and the benefit of many ALS interventions in seizure patients remains to be established. The majority of studies identified pertain to medical treatment of status epilepticus. These papers show that benzodiazepines are safe and effective but it is not possible to draw definitive conclusions about the best medication or the optimal route of administration. The evidence base for current pre-hospital guidelines for seizure emergencies is incomplete. A large proportion of patients are transported to hospital after a seizure but many of these may be suitable for home management. However, there is very little research into alternative care pathways or criteria that could be used to help paramedics avoid transport to hospital. More research is needed to improve care for people after a seizure and to improve the cost-effectiveness of the healthcare systems within which they are treated. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  1. Detection of seizures from small samples using nonlinear dynamic system theory.

    Science.gov (United States)

    Yaylali, I; Koçak, H; Jayakar, P

    1996-07-01

    The electroencephalogram (EEG), like many other biological phenomena, is quite likely governed by nonlinear dynamics. Certain characteristics of the underlying dynamics have recently been quantified by computing the correlation dimensions (D2) of EEG time series data. In this paper, D2 of the unbiased autocovariance function of the scalp EEG data was used to detect electrographic seizure activity. Digital EEG data were acquired at a sampling rate of 200 Hz per channel and organized in continuous frames (duration 2.56 s, 512 data points). To increase the reliability of D2 computations with short duration data, raw EEG data were initially simplified using unbiased autocovariance analysis to highlight the periodic activity that is present during seizures. The D2 computation was then performed from the unbiased autocovariance function of each channel using the Grassberger-Procaccia method with Theiler's box-assisted correlation algorithm. Even with short duration data, this preprocessing proved to be computationally robust and displayed no significant sensitivity to implementation details such as the choices of embedding dimension and box size. The system successfully identified various types of seizures in clinical studies.

  2. Absence seizure

    Science.gov (United States)

    Seizure - petit mal; Seizure - absence; Petit mal seizure; Epilepsy - absence seizure ... Elsevier; 2016:chap 101. Marcdante KJ, Kliegman RM. Seizures (paroxysmal disorders). In: Marcdante KJ, Kliegman RM, eds. Nelson Essentials ...

  3. Correlation of Serum Zinc Level with Simple Febrile Seizures: A Hospital based Prospective Case Control Study

    Directory of Open Access Journals (Sweden)

    Imran Gattoo

    2015-04-01

    Full Text Available Background: Febrile seizures are one of the most common neurological conditions of childhood. It seems that zinc deficiency is associated with increased risk of febrile seizures.Aim: To estimate the serum Zinc level in children with simple Febrile seizures and to find the correlation between serum zinc level and simple Febrile seizures.Materials and Methods: The proposed study was a hospital based prospective case control study which included infants and children aged between 6 months to 5 years, at Post Graduate Department of Pediatrics, (SMGS Hospital, GMC Jammu, northern India. A total of 200 infants and children fulfilling the inclusion criteria were included. Patients were divided into 100(cases in Group A with simple febrile seizure and 100(controls in Group B of children with acute febrile illness without seizure. All patients were subjected to detailed history and thorough clinical examination followed by relevant investigations.Results: Our study had slight male prepondance of 62% in cases and 58% in controls . Mean serum zinc level in cases was 61.53±15.87 ugm/dl and in controls it was 71.90+18.50 ugm/dl .Serum zinc level was found significantly low in cases of simple febrile seizures as compaired to controls ,with p value of

  4. Audit on first seizure presentation to Taranaki Base Hospital: a secondary centre experience.

    Science.gov (United States)

    Lance, Sean; Kumar, Rajesh

    2017-11-10

    Management of first seizure should be based on treating the underlying cause and tailoring investigations to identify those patients at high risk of recurrence. To establish the incidence of first seizure presentation to Taranaki Base Hospital and investigate the management of these patients. A retrospective audit was performed identifying patients presenting to Taranaki Base Hospital from 1 January 2015 to 31 December 2015 with a first seizure. Thirty-seven patients presented with their first seizure with 50% found to have an easily reversible precipitant. Forty-three percent had a history of previous brain insult and 52% had an abnormality identified on neuroimaging. Only 14% received formal neurology follow-up and only 8% had electroencephalography. Forty-three percent received chronic antiepileptic drug therapy and 27% had a recurrent seizure within 12 months. Only 43% had documented driving advice. The incidence of first seizure presentation to Taranaki Base Hospital is similar to worldwide data. In general, patients receive basic investigations in keeping with international guidelines. This audit has helped to identify a number of areas to address with the current service provision, including ways to improve access to important investigations and ways to develop a guideline to standardise care.

  5. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

    Science.gov (United States)

    Hussain, Lal

    2018-06-01

    Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

  6. 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.

  7. Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor.

    Science.gov (United States)

    Ansari, A H; Cherian, P J; Dereymaeker, A; Matic, V; Jansen, K; De Wispelaere, L; Dielman, C; Vervisch, J; Swarte, R M; Govaert, P; Naulaers, G; De Vos, M; Van Huffel, S

    2016-09-01

    After identifying the most seizure-relevant characteristics by a previously developed heuristic classifier, a data-driven post-processor using a novel set of features is applied to improve the performance. The main characteristics of the outputs of the heuristic algorithm are extracted by five sets of features including synchronization, evolution, retention, segment, and signal features. Then, a support vector machine and a decision making layer remove the falsely detected segments. Four datasets including 71 neonates (1023h, 3493 seizures) recorded in two different university hospitals, are used to train and test the algorithm without removing the dubious seizures. The heuristic method resulted in a false alarm rate of 3.81 per hour and good detection rate of 88% on the entire test databases. The post-processor, effectively reduces the false alarm rate by 34% while the good detection rate decreases by 2%. This post-processing technique improves the performance of the heuristic algorithm. The structure of this post-processor is generic, improves our understanding of the core visually determined EEG features of neonatal seizures and is applicable for other neonatal seizure detectors. The post-processor significantly decreases the false alarm rate at the expense of a small reduction of the good detection rate. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  8. 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,

  9. 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).

  10. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

    2017-09-27

    An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. Implementation of an Evidence-Based Seizure Algorithm in Intellectual Disability Nursing: A Pilot Study

    Science.gov (United States)

    Auberry, Kathy; Cullen, Deborah

    2016-01-01

    Based on the results of the Surrogate Decision-Making Self Efficacy Scale (Lopez, 2009a), this study sought to determine whether nurses working in the field of intellectual disability (ID) experience increased confidence when they implemented the American Association of Neuroscience Nurses (AANN) Seizure Algorithm during telephone triage. The…

  16. Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors.

    Science.gov (United States)

    Onorati, Francesco; Regalia, Giulia; Caborni, Chiara; Migliorini, Matteo; Bender, Daniel; Poh, Ming-Zher; Frazier, Cherise; Kovitch Thropp, Eliana; Mynatt, Elizabeth D; Bidwell, Jonathan; Mai, Roberto; LaFrance, W Curt; Blum, Andrew S; Friedman, Daniel; Loddenkemper, Tobias; Mohammadpour-Touserkani, Fatemeh; Reinsberger, Claus; Tognetti, Simone; Picard, Rosalind W

    2017-11-01

    New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic-clonic seizures and 49 focal to bilateral tonic-clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system

  17. 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.

  18. 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

  19. Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis.

    Science.gov (United States)

    Kaya, Yılmaz

    2015-09-01

    This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A-E, B-E, C-E, D-E, and A-D clusters, 100, 96, 100, 99.00 and 100% were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.

  20. 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...... 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......, 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...

  1. 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 (pepilepsy 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 (pepilepsy 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 seizure outcome. Copyright © 2017 British Epilepsy Association. Published by

  2. 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×.

  3. Febrile seizures

    Science.gov (United States)

    ... proper care. Occasionally, a provider will prescribe a medicine called diazepam to prevent or treat febrile seizures that occur more than once. However, no drug is completely effective in preventing febrile seizures. Alternative Names Seizure - fever induced; Febrile convulsions Patient Instructions ...

  4. 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.

  5. Online Epileptic Seizure Prediction Using Wavelet-Based Bi-Phase Correlation of Electrical Signals Tomography.

    Science.gov (United States)

    Vahabi, Zahra; Amirfattahi, Rasoul; Shayegh, Farzaneh; Ghassemi, Fahimeh

    2015-09-01

    Considerable efforts have been made in order to predict seizures. Among these methods, the ones that quantify synchronization between brain areas, are the most important methods. However, to date, a practically acceptable result has not been reported. In this paper, we use a synchronization measurement method that is derived according to the ability of bi-spectrum in determining the nonlinear properties of a system. In this method, first, temporal variation of the bi-spectrum of different channels of electro cardiography (ECoG) signals are obtained via an extended wavelet-based time-frequency analysis method; then, to compare different channels, the bi-phase correlation measure is introduced. Since, in this way, the temporal variation of the amount of nonlinear coupling between brain regions, which have not been considered yet, are taken into account, results are more reliable than the conventional phase-synchronization measures. It is shown that, for 21 patients of FSPEEG database, bi-phase correlation can discriminate the pre-ictal and ictal states, with very low false positive rates (FPRs) (average: 0.078/h) and high sensitivity (100%). However, the proposed seizure predictor still cannot significantly overcome the random predictor for all patients.

  6. 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.

  7. Cerebrovascular Diseases and Early Seizure

    Directory of Open Access Journals (Sweden)

    Ayşegül Gündüz

    2006-08-01

    Full Text Available OBJECTIVE: Cerebrovascular disease is one of the important causes of seizures and epilepsy among the advanced age group. Seziures are found to be associated with lesion localization and size in previous studies. METHODS: Here, we aimed to detect prevelance of seizure, relation of seizure and lesion localization, and observed seizure types. RESULTS: Three hundred seventy eight patients with ischemic cerebrovascular disease or intraparenchymal hemorrhage who were followed in Cerrahpasa IVIedical School clinic were studied retrospectively and probability of seizure occurence within 1 month after stroke was evaluated. CONCLUSION: Among 378 patients hospitalized by acute stroke, 339 were diagnosed as ischemic cerebrovascular disease and 39 (10.3% had primary intraparenchymal hematoma. Seizures were observed in 16 patients (4.2%, 2 (%5.1 in intraparenchymal hematoma group and 14 (%4.1 in ischemic cerebrovascular disease. Early seizures were detected in 33% of patients with anterior cerebral artery, in 6.8% of posterior cerebral artery and in 3.3% of middle cerebral artery infarcts and in three patients out of 12 who were known to have epilepsy. Seizure types were secondarily generalised tonic-clonic seizure in nine cases (57%. Among whole group status epilepticus was observed in four patients (1.1%. Conclusion: Early seizure rates are found to be high among patients with anterior cerebral artery infarct and known epilepsy

  8. 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

    Automatic detections of paroxysms in patients with childhood absence epilepsy have been neglected for several years. We acquire reliable detections using only a single-channel brainwave monitor, allowing for unobtrusive monitoring of antiepileptic drug effects. Ultimately we seek to obtain optimal...... 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...

  9. 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.

  10. 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.

  11. Seizure-related factors and non-verbal intelligence in children with epilepsy. A population-based study from Western Norway.

    Science.gov (United States)

    Høie, B; Mykletun, A; Sommerfelt, K; Bjørnaes, H; Skeidsvoll, H; Waaler, P E

    2005-06-01

    To study the relationship between seizure-related factors, non-verbal intelligence, and socio-economic status (SES) in a population-based sample of children with epilepsy. The latest ILAE International classifications of epileptic seizures and syndromes were used to classify seizure types and epileptic syndromes in all 6-12 year old children (N=198) with epilepsy in Hordaland County, Norway. The children had neuropediatric and EEG examinations. Of the 198 patients, demographic characteristics were collected on 183 who participated in psychological studies including Raven matrices. 126 healthy controls underwent the same testing. Severe non-verbal problems (SNVP) were defined as a Raven score at or Raven percentile group, whereas controls were highly over-represented in the higher percentile groups. SNVP were present in 43% of children with epilepsy and 3% of controls. These problems were especially common in children with remote symptomatic epilepsy aetiology, undetermined epilepsy syndromes, myoclonic seizures, early seizure debut, high seizure frequency and in children with polytherapy. Seizure-related characteristics that were not usually associated with SNVP were idiopathic epilepsies, localization related (LR) cryptogenic epilepsies, absence and simple partial seizures, and a late debut of epilepsy. Adjusting for socio-economic status factors did not significantly change results. In childhood epilepsy various seizure-related factors, but not SES factors, were associated with the presence or absence of SNVP. Such deficits may be especially common in children with remote symptomatic epilepsy aetiology and in complex and therapy resistant epilepsies. Low frequencies of SNVP may be found in children with idiopathic and LR cryptogenic epilepsy syndromes, simple partial or absence seizures and a late epilepsy debut. Our study contributes to an overall picture of cognitive function and its relation to central seizure characteristics in a childhood epilepsy population

  12. Toward a noninvasive automatic seizure control system in rats with transcranial focal stimulations via tripolar concentric ring electrodes.

    Science.gov (United States)

    Makeyev, Oleksandr; Liu, Xiang; Luna-Munguía, Hiram; Rogel-Salazar, Gabriela; Mucio-Ramirez, Samuel; Liu, Yuhong; Sun, Yan L; Kay, Steven M; Besio, Walter G

    2012-07-01

    Epilepsy affects approximately 1% of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We are developing a noninvasive, or minimally invasive, transcranial focal electrical stimulation system through our novel tripolar concentric ring electrodes to control seizures. In this study, we demonstrate feasibility of an automatic seizure control system in rats with pentylenetetrazole-induced seizures through single and multiple stimulations. These stimulations are automatically triggered by a real-time electrographic seizure activity detector based on a disjunctive combination of detections from a cumulative sum algorithm and a generalized likelihood ratio test. An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13). Detection of electrographic seizure activity was accomplished in advance of the early behavioral seizure activity in 76.92% of the cases. Automatically triggered stimulation significantly (p = 0.001) reduced the electrographic seizure activity power in the once stimulated group compared to controls in 70% of the cases. To the best of our knowledge this is the first closed-loop automatic seizure control system based on noninvasive electrical brain stimulation using tripolar concentric ring electrode electrographic seizure activity as feedback.

  13. 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.

  14. Identifying seizure clusters in patients with psychogenic nonepileptic seizures.

    Science.gov (United States)

    Baird, Grayson L; Harlow, Lisa L; Machan, Jason T; Thomas, Dave; LaFrance, W C

    2017-08-01

    The present study explored how seizure clusters may be defined for those with psychogenic nonepileptic seizures (PNES), a topic for which there is a paucity of literature. The sample was drawn from a multisite randomized clinical trial for PNES; seizure data are from participants' seizure diaries. Three possible cluster definitions were examined: 1) common clinical definition, where ≥3 seizures in a day is considered a cluster, along with two novel statistical definitions, where ≥3 seizures in a day are considered a cluster if the observed number of seizures statistically exceeds what would be expected relative to a patient's: 1) average seizure rate prior to the trial, 2) observed seizure rate for the previous seven days. Prevalence of clusters was 62-68% depending on cluster definition used, and occurrence rate of clusters was 6-19% depending on cluster definition. Based on these data, clusters seem to be common in patients with PNES, and more research is needed to identify if clusters are related to triggers and outcomes. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. Pathology-Based Approach to Seizure Outcome After Surgery for Pharmacoresistant Medial Temporal Lobe Epilepsy.

    Science.gov (United States)

    Martinoni, Matteo; Berti, Pier Paolo; Marucci, Gianluca; Rubboli, Guido; Volpi, Lilia; Riguzzi, Patrizia; Marliani, Federica; Toni, Francesco; Bisulli, Francesca; Tinuper, Paolo; Michelucci, Roberto; Baruzzi, Agostino; Giulioni, Marco

    2016-06-01

    Hippocampal sclerosis (HS) is the most common cause of drug-resistant medial temporal lobe epilepsy (MTLE). Structural abnormalities such as HS, granule cell pathology (GCP), and focal cortical dysplasia (FCD) have been classified histopathologically, possibly allowing a more accurate assessment of prognostic seizure and neuropsychologic outcomes. We correlated seizure outcome with comprehensive temporal lobe pathologic findings, identified according to the most recent classification systems of HS, GCP, and FCD. All the 83 patients who underwent anterior temporal lobectomy (ATL) for drug-resistant MTLE and with a proven diagnosis of HS between April 2001 and May 2014 were collected. Patients were divided in 2 main groups: 1) isolated HS with/without GCP (HS +/- GCP); and 2) HS associated with FCD with/without GCP (HS+FCD +/- GCP). Patients were followed up at least 1 year, and seizure outcome was reported in accordance with Engel classification. Group I: HS +/- GCP: Statistical analysis confirmed a better outcome in HS + GCP patients than in HS-no GCP (P epilepsy surgery might improve the interpretation of the results, could predict which cases will enjoy a better seizure outcome, and could help to the comprehension of the causes of failures. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. 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.

  17. 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.

  18. Seizure disorders in 43 cattle.

    Science.gov (United States)

    D'Angelo, A; Bellino, C; Bertone, I; Cagnotti, G; Iulini, B; Miniscalco, B; Casalone, C; Gianella, P; Cagnasso, A

    2015-01-01

    Large animals have a relatively high seizure threshold, and in most cases seizures are acquired. No published case series have described this syndrome in cattle. To describe clinical findings and outcomes in cattle referred to the Veterinary Teaching Hospital of the University of Turin (Italy) because of seizures. Client-owned cattle with documented evidence of seizures. Medical records of cattle with episodes of seizures reported between January 2002 and February 2014 were reviewed. Evidence of seizures was identified based on the evaluation of seizure episodes by the referring veterinarian or 1 of the authors. Animals were recruited if physical and neurologic examinations were performed and if diagnostic laboratory test results were available. Forty-three of 49 cases fulfilled the inclusion criteria. The mean age was 8 months. Thirty-one animals were male and 12 were female. Piedmontese breed accounted for 39/43 (91%) animals. Seizures were etiologically classified as reactive in 30 patients (70%) and secondary or structural in 13 (30%). Thirty-six animals survived, 2 died naturally, and 5 were euthanized for reasons of animal welfare. The definitive cause of reactive seizures was diagnosed as hypomagnesemia (n = 2), hypocalcemia (n = 12), and hypomagnesemia-hypocalcemia (n = 16). The cause of structural seizures was diagnosed as cerebrocortical necrosis (n = 8), inflammatory diseases (n = 4), and lead (Pb) intoxication (n = 1). The study results indicate that seizures largely are reported in beef cattle and that the cause can be identified and successfully treated in most cases. Copyright © 2015 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

  19. The probability of seizures during EEG monitoring in critically ill adults.

    Science.gov (United States)

    Westover, M Brandon; Shafi, Mouhsin M; Bianchi, Matt T; Moura, Lidia M V R; O'Rourke, Deirdre; Rosenthal, Eric S; Chu, Catherine J; Donovan, Samantha; Hoch, Daniel B; Kilbride, Ronan D; Cole, Andrew J; Cash, Sydney S

    2015-03-01

    To characterize the risk for seizures over time in relation to EEG findings in hospitalized adults undergoing continuous EEG monitoring (cEEG). Retrospective analysis of cEEG data and medical records from 625 consecutive adult inpatients monitored at a tertiary medical center. Using survival analysis methods, we estimated the time-dependent probability that a seizure will occur within the next 72-h, if no seizure has occurred yet, as a function of EEG abnormalities detected so far. Seizures occurred in 27% (168/625). The first seizure occurred early (monitoring) in 58% (98/168). In 527 patients without early seizures, 159 (30%) had early epileptiform abnormalities, versus 368 (70%) without. Seizures were eventually detected in 25% of patients with early epileptiform discharges, versus 8% without early discharges. The 72-h risk of seizures declined below 5% if no epileptiform abnormalities were present in the first two hours, whereas 16h of monitoring were required when epileptiform discharges were present. 20% (74/388) of patients without early epileptiform abnormalities later developed them; 23% (17/74) of these ultimately had seizures. Only 4% (12/294) experienced a seizure without preceding epileptiform abnormalities. Seizure risk in acute neurological illness decays rapidly, at a rate dependent on abnormalities detected early during monitoring. This study demonstrates that substantial risk stratification is possible based on early EEG abnormalities. These findings have implications for patient-specific determination of the required duration of cEEG monitoring in hospitalized patients. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  20. Suppression of seizures based on the multi-coupled neural mass model.

    Science.gov (United States)

    Cao, Yuzhen; Ren, Kaili; Su, Fei; Deng, Bin; Wei, Xile; Wang, Jiang

    2015-10-01

    Epilepsy is one of the most common serious neurological disorders, which affects approximately 1% of population in the world. In order to effectively control the seizures, we propose a novel control methodology, which combines the feedback linearization control (FLC) with the underlying mechanism of epilepsy, to achieve the suppression of seizures. The three coupled neural mass model is constructed to study the property of the electroencephalographs (EEGs). Meanwhile, with the model we research on the propagation of epileptiform waves and the synchronization of populations, which are taken as the foundation of our control method. Results show that the proposed approach not only yields excellent performances in clamping the pathological spiking patterns to the reference signals derived under the normal state but also achieves the normalization of the pathological parameter, where the parameters are estimated from EEGs with Unscented Kalman Filter. The specific contribution of this paper is to treat the epilepsy from its pathogenesis with the FLC, which provides critical theoretical basis for the clinical treatment of neurological disorders.

  1. 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…

  2. 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.

  3. 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.

  4. Feasibility of recording high frequency oscillations with tripolar concentric ring electrodes during pentylenetetrazole-induced seizures in rats.

    Science.gov (United States)

    Makeyev, Oleksandr; Liu, Xiang; Wang, Liling; Zhu, Zhenghan; Taveras, Aristides; Troiano, Derek; Medvedev, Andrei V; Besio, Walter G

    2012-01-01

    As epilepsy remains a refractory condition in about 30% of patients with complex partial seizures, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Previously, we applied noninvasive transcranial focal stimulation via novel tripolar concentric ring electrodes (TCREs) on the scalp of rats after inducing seizures with pentylenetetrazole (PTZ). We developed a close-loop system to detect seizures and automatically trigger the stimulation and evaluated its effect on the electrographic activity recorded by TCREs in rats. In our previous work the detectors of seizure onset were based on seizure-induced changes in signal power in the frequency range up to 100 Hz, while in this preliminary study we assess the feasibility of recording high frequency oscillations (HFOs) in the range up to 300 Hz noninvasively with scalp TCREs during PTZ-induced seizures. Grand average power spectral density estimate and generalized likelihood ratio tests were used to compare power of electrographic activity at different stages of seizure development in a group of rats (n= 8). The results suggest that TCREs have the ability to record HFOs from the scalp as well as that scalp-recorded HFOs can potentially be used as features for seizure onset detection.

  5. 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.

  6. Seizure Disorders in Pregnancy

    Science.gov (United States)

    ... If I have a seizure disorder, can it cause problems during pregnancy? • What risks are associated with having a seizure ... If I have a seizure disorder, can it cause problems during pregnancy? Seizure disorders can affect pregnancy in several ways: • ...

  7. 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

  8. Seizure prognosis of patients with low-grade tumors.

    Science.gov (United States)

    Kahlenberg, Cynthia A; Fadul, Camilo E; Roberts, David W; Thadani, Vijay M; Bujarski, Krzysztof A; Scott, Rod C; Jobst, Barbara C

    2012-09-01

    Seizures frequently impact the quality of life of patients with low grade tumors. Management is often based on best clinical judgment. We examined factors that correlate with seizure outcome to optimize seizure management. Patients with supratentorial low-grade tumors evaluated at a single institution were retrospectively reviewed. Using multiple regression analysis the patient characteristics and treatments were correlated with seizure outcome using Engel's classification. Of the 73 patients with low grade tumors and median follow up of 3.8 years (range 1-20 years), 54 (74%) patients had a seizure ever and 46 (63%) had at least one seizure before tumor surgery. The only factor significantly associated with pre-surgical seizures was tumor histology. Of the 54 patients with seizures ever, 25 (46.3%) had a class I outcome at last follow up. There was no difference in seizure outcome between grade II gliomas (astrocytoma grade II, oligodendroglioma grade II, mixed oligo-astrocytoma grade II) and other pathologies (pilocytic astrocytoma, ependymomas, DNET, gangliocytoma and ganglioglioma). Once seizures were established seizure prognosis was similar between different pathologies. Chemotherapy (p=0.03) and radiation therapy (p=0.02) had a positive effect on seizure outcome. No other parameter including significant tumor growth during the follow up period predicted seizure outcome. Only three patients developed new-onset seizures after tumor surgery that were non-perioperative. Anticonvulsant medication was tapered in 14 patients with seizures and 10 had no further seizures. Five patients underwent additional epilepsy surgery with a class I outcome in four. Two patients received a vagal nerve stimulator with >50% seizure reduction. Seizures at presentation are the most important factor associated with continued seizures after tumor surgery. Pathology does not influence seizure outcome. Use of long term prophylactic anticonvulsants is unwarranted. Chemotherapy and

  9. 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

  10. 27 CFR 555.186 - Seizure or forfeiture.

    Science.gov (United States)

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 3 2010-04-01 2010-04-01 false Seizure or forfeiture. 555... Seizure or forfeiture. Any plastic explosive that does not contain a detection agent in violation of 18 U.S.C. 842(l)-(n) is subject to seizure and forfeiture, and all provisions of 19 U.S.C. 1595a...

  11. Controlled-release oxycodone-induced seizures.

    Science.gov (United States)

    Klein, Moti; Rudich, Zvia; Gurevich, Boris; Lifshitz, Matityahu; Brill, Silviu; Lottan, Michael; Weksler, Natan

    2005-11-01

    The use of the opioid oxycodone hydrochloride in the management of chronic pain is gaining popularity principally because of its tolerability. However, opioid-related seizure in patients with epilepsy or other conditions that may decrease seizure threshold has been described in the literature; in particular, oxycodone has been associated with seizure in a patient with acute renal failure. The aim of this article was to report a patient with a history of seizures but normal renal and hepatic function who developed seizure on 2 occasions after oxycodone ingestion. A 54-year-old male patient presented with a history of tonic-clonic seizures that developed immediately after intracranial surgery. Long-term treatment with carbamazepine 400 mg QD was started, and the patient was free of convulsions for approximately 7 years. The patient presented to us with severe headache that was nonresponsive to an NSAID and the opiate agonist tramadol. Treatment with controlled-release (CR) oxycodone and tramadol drops (50 mg QID if necessary) was started, and tonic-clonic seizures developed 3 days later. Based on laboratory analysis, the patient had normal renal and hepatic function. On discontinuation of oxycodone treatment, the seizures resolved. However, due to effective pain relief with oxycodone, the patient decided to continue treatment, and seizures recurred. Carbamazepine was then administered 4 hours before oxycodone dosing, which allowed continuation of treatment without seizure. A patient with a history of seizures controlled with long-term carbamazepine therapy developed seizures when he started treatment with oxycodone CR at recommended doses. Oxycodone CR should be used with extreme caution in patients with epilepsy or other conditions that may decrease seizure threshold.

  12. 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

  13. Grand Mal Seizure

    Science.gov (United States)

    ... grand mal seizures include: A family history of seizure disorders Any injury to the brain from trauma, a ... the risk of birth defects. If you have epilepsy and plan to become pregnant, work with your ...

  14. Frontal Lobe Seizures

    Science.gov (United States)

    ... cause of frontal lobe epilepsy remains unknown. Complications Status epilepticus. Frontal lobe seizures tend to occur in clusters and may provoke a dangerous condition called status epilepticus — in which seizure activity lasts much longer than ...

  15. 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.)

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

    International Nuclear Information System (INIS)

    Paldino, Michael J.; Jones, Jeremy Y.; Mahmood, Nadia; Sher, Andrew; Hayatghaibi, Shireen; Seghers, Victor; Yang, Erica; Zhang, Wei; Krishnamurthy, Ramkumar

    2017-01-01

    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.)

  17. 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

  18. Towards an Online Seizure Advisory System—An Adaptive Seizure Prediction Framework Using Active Learning Heuristics

    NARCIS (Netherlands)

    Karuppiah Ramachandran, Vignesh Raja; Alblas, Huibert J.; Le Viet Duc, Duc Viet; Meratnia, Nirvana

    2018-01-01

    In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the

  19. 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.

  20. Seizure development after stroke.

    Science.gov (United States)

    Misirli, H; Ozge, A; Somay, G; Erdoğan, N; Erkal, H; Erenoğlu, N Y

    2006-12-01

    Although there have been many studies on seizures following stroke, there is still much we do not know about them. In this study, we evaluated the characteristics of seizures in stroke patients. There were 2267 patients with a first-ever stroke, and after excluding 387 patients, 1880 were available for analysis. Of these 1880 patients, we evaluated 200 patients with seizures and 400 patients without seizures. We investigated the seizures according to age, gender, stroke type, the aetiology of ischaemic stroke and the localisation of the lesion. The seizures were classified as early onset and late onset and the seizure type as partial, generalised or secondarily generalised. Seizures occurred in 200 (10.6%) of 1880 strokes. The number of patients with seizures were 138 (10.6%) in ischaemic stroke group and 62 (10.7%) in haemorrhagic stroke group. Patients with ischaemic strokes had 41 embolic (29.7%) and 97 thrombotic (70.3%) origin, and these were not statistically significant in comparison with controls. Cortical involvement for the development of seizures was the most important risk factor (odds ratios = 4.25, p < 0.01). It was concluded that embolic strokes, being younger than 65 years old, and cortical localisation of stroke were important risks for developing seizures.

  1. Neurodevelopmental comorbidities and seizure control 24 months after a first unprovoked seizure in children.

    Science.gov (United States)

    Jason, Eva Åndell; Tomson, Torbjörn; Carlsson, Sofia; Tedroff, Kristina; Åmark, Per

    2018-07-01

    To follow children with newly diagnosed unprovoked seizures to determine (1) whether the prevalence of neurodevelopmental comorbidities and cerebral palsy (CP) changed after the initial seizure, and (2) the association between studied comorbidities and seizures 13-24 months after seizure onset or initiation of treatment. Analyses were based on 750 children (28 days-18 years) with a first unprovoked seizure (index) included in a population-based Incidence Registry in Stockholm between 2001 and 2006. The children were followed for two years and their medical records were examined for a priori defined neurodevelopmental/psychiatric comorbidities and CP and seizure frequency. Baseline information was collected from medical records from before, and up to six months after, the index seizure. Odds ratios (OR) of repeated seizures 13-24 months after the first seizure or after initiation of anti-epileptic drug treatment was calculated by logistic regression and adjusted for age and sex. At baseline, 32% of the children had neurodevelopmental/psychiatric comorbidities or CP compared to 35%, 24 months later. Children with such comorbidities more often experienced seizures 13-24 months after the index seizure (OR 2.87, CI 2.07-3.99) with the highest OR in those with CP or attention deficit hyperactivity disorder (ADHD). Children diagnosed at age neurodevelopmental comorbidities and CP in children with epilepsy tend to be present already at seizure onset and that such comorbidities are strong indicators of poor outcome regarding seizure control with or without treatment. Copyright © 2018 Elsevier B.V. All rights reserved.

  2. 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.

  3. Evidence-based guideline: Management of an unprovoked first seizure in adults: Report of the Guideline Development Subcommittee of the American Academy of Neurology and the American Epilepsy Society.

    Science.gov (United States)

    Krumholz, Allan; Wiebe, Samuel; Gronseth, Gary S; Gloss, David S; Sanchez, Ana M; Kabir, Arif A; Liferidge, Aisha T; Martello, Justin P; Kanner, Andres M; Shinnar, Shlomo; Hopp, Jennifer L; French, Jacqueline A

    2015-04-21

    To provide evidence-based recommendations for treatment of adults with an unprovoked first seizure. We defined relevant questions and systematically reviewed published studies according to the American Academy of Neurology's classification of evidence criteria; we based recommendations on evidence level. Adults with an unprovoked first seizure should be informed that their seizure recurrence risk is greatest early within the first 2 years (21%-45%) (Level A), and clinical variables associated with increased risk may include a prior brain insult (Level A), an EEG with epileptiform abnormalities (Level A), a significant brain-imaging abnormality (Level B), and a nocturnal seizure (Level B). Immediate antiepileptic drug (AED) therapy, as compared with delay of treatment pending a second seizure, is likely to reduce recurrence risk within the first 2 years (Level B) but may not improve quality of life (Level C). Over a longer term (>3 years), immediate AED treatment is unlikely to improve prognosis as measured by sustained seizure remission (Level B). Patients should be advised that risk of AED adverse events (AEs) may range from 7% to 31% (Level B) and that these AEs are likely predominantly mild and reversible. Clinicians' recommendations whether to initiate immediate AED treatment after a first seizure should be based on individualized assessments that weigh the risk of recurrence against the AEs of AED therapy, consider educated patient preferences, and advise that immediate treatment will not improve the long-term prognosis for seizure remission but will reduce seizure risk over the subsequent 2 years. © 2015 American Academy of Neurology.

  4. 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.

  5. 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...

  6. 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)

  7. A seizuring alagille syndrome

    Directory of Open Access Journals (Sweden)

    Jomon Mathew John

    2017-01-01

    Full Text Available Alagille syndrome is a rare autosomal dominant inherited disorder with incidence of one in 100,000 live births. This syndrome with seizure as a presentation has been rarely reported in Indian studies. We present a 3-month-old infant who presented to us with seizures was found to have a dysmorphic face, jaundice, hepatomegaly, and soft systolic murmur. Infant was stabilized and remained seizure free. A detailed clinical evaluation of a common presentation may reveal a rare syndrome.

  8. Epilepsy after Febrile Seizures

    DEFF Research Database (Denmark)

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

    2016-01-01

    to evaluate genetic associations of different febrile seizure subtypes. Results Histories of febrile seizures were validated in 1051 twins in 900 pairs. The febrile seizure type was classified as simple, complex, or febrile status epilepticus. There were 61% simple, 12% complex, and 7% febrile status...... 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...

  9. 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 ...

  10. Seizures in multiple sclerosis

    NARCIS (Netherlands)

    Koch, Marcus; Uyttenboogaart, Maarten; Polman, Susan; De Keyser, Jacques

    Seizures have long been recognized to be part of the disease spectrum of multiple sclerosis (MS). While they occur in only a minority of patients with MS, epileptic seizures can have serious consequences. The treatment of MS can be epileptogenic, and antiepileptic treatment can conversely worsen the

  11. 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.

  12. Brand name to generic substitution of antiepileptic drugs does not lead to seizure-related hospitalization: a population-based case-crossover study.

    Science.gov (United States)

    Polard, Elisabeth; Nowak, Emmanuel; Happe, André; Biraben, Arnaud; Oger, Emmanuel

    2015-11-01

    There is still controversy on brand-to-generic (B-G) antiepileptic drugs (AEDs) substitution. To assess association between B-G AED substitution and seizure-related hospitalization, we designed a case crossover using the French National Health Insurance Database. We identified a cohort of adult patients who filled a prescription in 2009-2011 for AEDs with at least one brand name and one generic form. The outcome date was defined as the date of hospitalization, coded G40.x or G41.x, with a G40/G41 hospitalization-free period of at least 1 year. Patients with a medical history of cancer and women who gave birth in 2009-2011 were excluded. We required individuals to have regular dispensations of AEDs within the year preceding the outcome date. Free patients were defined as patients who had only brand name dispensations before the control period. Eight thousand three hundred seventy nine patients (mean age ± standard deviation, 52.7 ± 18.8 years; sex ratio male/female, 1.27) were analyzed. Discordant pairs were 491 with B-G substitution in the control period only and 478 with B-G substitution in the case period only; odds ratio (95% confidence interval) 0.97 (0.86-1.10). No statistically significant interaction was detected among the four prespecified subgroup analyses (gender, age strata, free or non-free, and strict AED monotherapy or not). Controlling for non-seizure-related hospitalizations made no material difference. Sensitivity analyses yielded similar results. Brand-to-generic AED substitution was not associated with an elevated risk of seizure-related hospitalization. Copyright © 2015 John Wiley & Sons, Ltd.

  13. Predictability of uncontrollable multifocal seizures - towards new treatment options

    Science.gov (United States)

    Lehnertz, Klaus; Dickten, Henning; Porz, Stephan; Helmstaedter, Christoph; Elger, Christian E.

    2016-04-01

    Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels of sensitivity and specificity, even in an ambulatory setting, has been repeatedly shown, however, in patients with a single seizure focus only. We did a study to assess feasibility of state-of-the-art, electroencephalogram-based seizure-prediction techniques in patients with uncontrollable multifocal seizures. We obtained significant predictive information about upcoming seizures in more than two thirds of patients. Unexpectedly, the emergence of seizure precursors was confined to non-affected brain areas. Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures. Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

  14. 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

  15. Domain similarity based orthology detection.

    Science.gov (United States)

    Bitard-Feildel, Tristan; Kemena, Carsten; Greenwood, Jenny M; Bornberg-Bauer, Erich

    2015-05-13

    Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationally feasible in a reasonable amount of time. We propose to speed up the detection of orthologous proteins by using strings of domains to characterize the proteins. We present two new protein similarity measures, a cosine and a maximal weight matching score based on domain content similarity, and new software, named porthoDom. The qualities of the cosine and the maximal weight matching similarity measures are compared against curated datasets. The measures show that domain content similarities are able to correctly group proteins into their families. Accordingly, the cosine similarity measure is used inside porthoDom, the wrapper developed for proteinortho. porthoDom makes use of domain content similarity measures to group proteins together before searching for orthologs. By using domains instead of amino acid sequences, the reduction of the search space decreases the computational complexity of an all-against-all sequence comparison. We demonstrate that representing and comparing proteins as strings of discrete domains, i.e. as a concatenation of their unique identifiers, allows a drastic simplification of search space. porthoDom has the advantage of speeding up orthology detection while maintaining a degree of accuracy similar to proteinortho. The implementation of porthoDom is released using python and C++ languages and is available under the GNU GPL licence 3 at http://www.bornberglab.org/pages/porthoda .

  16. 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.

  17. On the nature of seizure dynamics

    Science.gov (United States)

    Stacey, William C.; Quilichini, Pascale P.; Ivanov, Anton I.

    2014-01-01

    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

  18. 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

  19. 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.

  20. Epilepsy or seizures - discharge

    Science.gov (United States)

    ... and the people you work with about your seizure disorder. Driving your own car is generally safe and ... References Abou-Khalil BW, Gallagher MJ, Macdonald RL. Epilepsies. In: Daroff RB, Jankovic J, Mazziotta JC, Pomeroy ...

  1. Temporal Lobe Seizure

    Science.gov (United States)

    ... functions, including having odd feelings — such as euphoria, deja vu or fear. Temporal lobe seizures are sometimes called ... sudden sense of unprovoked fear or joy A deja vu experience — a feeling that what's happening has happened ...

  2. 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.

  3. 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.

  4. 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 %.

  5. 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.

  6. Automatic identification of epileptic seizures from EEG signals using linear programming boosting.

    Science.gov (United States)

    Hassan, Ahnaf Rashik; Subasi, Abdulhamit

    2016-11-01

    Computerized epileptic seizure detection is essential for expediting epilepsy diagnosis and research and for assisting medical professionals. Moreover, the implementation of an epilepsy monitoring device that has low power and is portable requires a reliable and successful seizure detection scheme. In this work, the problem of automated epilepsy seizure detection using singe-channel EEG signals has been addressed. At first, segments of EEG signals are decomposed using a newly proposed signal processing scheme, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Six spectral moments are extracted from the CEEMDAN mode functions and train and test matrices are formed afterward. These matrices are fed into the classifier to identify epileptic seizures from EEG signal segments. In this work, we implement an ensemble learning based machine learning algorithm, namely linear programming boosting (LPBoost) to perform classification. The efficacy of spectral features in the CEEMDAN domain is validated by graphical and statistical analyses. The performance of CEEMDAN is compared to those of its predecessors to further inspect its suitability. The effectiveness and the appropriateness of LPBoost are demonstrated as opposed to the commonly used classification models. Resubstitution and 10 fold cross-validation error analyses confirm the superior algorithm performance of the proposed scheme. The algorithmic performance of our epilepsy seizure identification scheme is also evaluated against state-of-the-art works in the literature. Experimental outcomes manifest that the proposed seizure detection scheme performs better than the existing works in terms of accuracy, sensitivity, specificity, and Cohen's Kappa coefficient. It can be anticipated that owing to its use of only one channel of EEG signal, the proposed method will be suitable for device implementation, eliminate the onus of clinicians for analyzing a large bulk of data manually, and

  7. Computed Tomography Findings in Patients with Seizure Disorder

    Directory of Open Access Journals (Sweden)

    Sumnima Acharya

    2016-06-01

    Full Text Available Introduction: Seizure occurs in up to 10% of the population, whereas epilepsy is a chronic disease characterized by recurrent seizures that may affect up to 2% of the population. Modern neuroimaging is useful in diagnosis of  abnormalities underlying the epilepsies, but the information provided by imaging techniques can also contribute to proper classification of certain epileptic disorders and can delineate the genetics of some underlying syndromes. Neuroimaging is even more important for those patients who have medically intractable seizures. This study was carried out to establish different etiologies of seizures, to correlate the clinical data and radiological findings in cases of seizure, and to identify the common etiologies in different types of seizures. Methods: This was a retrospective hospital-based study conducted in the Department of Radiodiagnosis of Lumbini Medical College Teaching Hospital. Records of patients of past two years, admitted in any department of the hospital with history of seizure disorder and underwent a Computed Tomography  (CT of brain were included. The CT patterns were assessed and the data were tabulated and statistically analyzed. Results: There were a total of 480 cases out of which 263 (55% were male and 217 (45% were female with M:F ratio of 1.2:1. Generalized seizure was more frequent than partial seizure in both gender. In 274 cases of generalized seizures, CT scan findings were abnormal in 151 cases and normal finding observed in 123 cases. In 206 cases of partial seizures, 125 cases were abnormal and 81 having normal CT scan findings. Age wise distribution showed highest number (n=218 of cases in young group (60 yr. The most common cause of seizure  was  calcified granuloma (n=79, 16.5% followed by neurocysticercosis (NCC, n=64, 13%. Diffuse cerebral edema, sub-arachnoid hemorrhage, and hydrocephalus was seen only in lower age group particularly among 1-20 years. Infarct and diffuse cortical

  8. USE OF STRUCTURAL MRI IN PATIENTS WITH MEDICALLY REFRACTORY SEIZURES

    Directory of Open Access Journals (Sweden)

    Ara G. Kaprelyan

    2012-12-01

    Full Text Available Introduction: Refractory epilepsy is common in patients with structural brain lesions including acquired disorders and genetic abnormalities. Recently, MRI is a precise diagnostic tool for recognition of different structural causes underlying medically intractable seizures.Objective: To evaluate the usefulness of MRI for detection of brain lesions associated with refractory epilepsy.Material and methods: 49 patients (20M and 29F; aged 48.6±24.7 years with refractory epilepsy were included in the study. They presented with partial (46.0%, secondary (31.0% or primary (23.0% generalized tonic-clonic seizures. Clinical diagnosis was based on the revised criteria of ILAE. Structural neuroimaging (MRI, EEG recording, and neurological examination were performed.Results: MRI detected different structural brain abnormalities totally in 36 (73.5% patients, including cerebral tumors (21p, cerebrovascular accidents (5p, hyppocampal sclerosis (3p, developmental malformations (2p, postencephalitic lesions (2p, arachnoid cysts (2p, and tuberous sclerosis (1p. Neuroimaging revealed normal findings in 13 (27.5% cases. EEG recordings showed focal epileptic activity in 38 (77.6% patients, including 33 cases with and 5 without structural brain abnormalities.Conclusion: This study revealed that structural brain lesions are commonly associated with refractory epilepsy. We suggested that MRI is a useful diagnostic method for assessment of patients with uncontrolled seizures or altered epileptic pattern.

  9. Microcontroller based driver alertness detection systems to detect drowsiness

    Science.gov (United States)

    Adenin, Hasibah; Zahari, Rahimi; Lim, Tiong Hoo

    2018-04-01

    The advancement of embedded system for detecting and preventing drowsiness in a vehicle is a major challenge for road traffic accident systems. To prevent drowsiness while driving, it is necessary to have an alert system that can detect a decline in driver concentration and send a signal to the driver. Studies have shown that traffc accidents usually occur when the driver is distracted while driving. In this paper, we have reviewed a number of detection systems to monitor the concentration of a car driver and propose a portable Driver Alertness Detection System (DADS) to determine the level of concentration of the driver based on pixelated coloration detection technique using facial recognition. A portable camera will be placed at the front visor to capture facial expression and the eye activities. We evaluate DADS using 26 participants and have achieved 100% detection rate with good lighting condition and a low detection rate at night.

  10. 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...

  11. 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.

  12. 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.

  13. 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.

  14. Vision-based Vehicle Detection Survey

    Directory of Open Access Journals (Sweden)

    Alex David S

    2016-03-01

    Full Text Available Nowadays thousands of drivers and passengers were losing their lives every year on road accident, due to deadly crashes between more than one vehicle. There are number of many research focuses were dedicated to the development of intellectual driver assistance systems and autonomous vehicles over the past decade, which reduces the danger by monitoring the on-road environment. In particular, researchers attracted towards the on-road detection of vehicles in recent years. Different parameters have been analyzed in this paper which includes camera placement and the various applications of monocular vehicle detection, common features and common classification methods, motion- based approaches and nighttime vehicle detection and monocular pose estimation. Previous works on the vehicle detection listed based on camera poisons, feature based detection and motion based detection works and night time detection.

  15. 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.

  16. Risk factor for febrile seizures

    Directory of Open Access Journals (Sweden)

    Odalović Dragica

    2014-01-01

    Full Text Available Febrile seizures are the most frequent neurological disorder in the childhood. According to American Academy of Pediatrics (AAP, they have been defined as seizures provoked by high temperature in children aged between 6 months and 5 years, without previous history of afebrile seizures, intracranial infections and other possible causes of seizures. Seizures can be typical and atypical, according to the characteristics. Pathogenesis of this disorder has not been clarified yet, and it is believed to be a combination of genetic factors, high body temperature and brain maturation. The risk factors for recurrence of febrile seizures are: age in which seizures appeared for the first time, epilepsy in the first degree relative, febrile seizures in the first degree relative, frequent diseases with fever and low body temperature on the beginning of seizures. The frequency of recurrent seizures The risk for occurrence of epilepsy in children with simple seizures is about 1-1.5%, which is slightly higher compared to general population, while it increases to 4-15% in patients with complex seizures. However, there is no evidence that therapy prevents occurrence of epilepsy. When the prevention of recurrent seizures is considered, it is necessary to separate simple from complex seizures. The aim of this paper was to analyze the most important risk factors for febrile seizures, and to evaluate their impact on occurrence of recurrent seizures. Our study included 125 children with febrile seizures, aged from 6 months to 5 years. The presence of febrile seizures and epilepsy in the first degree relative has been noted in 22% of children. Typical febrile seizures were observed in 76% of cases, and atypical in 24%. Most patients had only one seizure (73.6%. Children, who had seizure earlier in life, had more frequent recurrences. Both risk factors were present in 25% of patients, while 68% of patients had only one risk factor. For the children with febrile disease

  17. Frequent seizures are associated with a network of gray matter atrophy in temporal lobe epilepsy with or without hippocampal sclerosis.

    Directory of Open Access Journals (Sweden)

    Ana C Coan

    Full Text Available OBJECTIVE: Patients with temporal lobe epilepsy (TLE with hippocampal sclerosis (HS have diffuse subtle gray matter (GM atrophy detectable by MRI quantification analyses. However, it is not clear whether the etiology and seizure frequency are associated with this atrophy. We aimed to evaluate the occurrence of GM atrophy and the influence of seizure frequency in patients with TLE and either normal MRI (TLE-NL or MRI signs of HS (TLE-HS. METHODS: We evaluated a group of 172 consecutive patients with unilateral TLE-HS or TLE-NL as defined by hippocampal volumetry and signal quantification (122 TLE-HS and 50 TLE-NL plus a group of 82 healthy individuals. Voxel-based morphometry was performed with VBM8/SPM8 in 3T MRIs. Patients with up to three complex partial seizures and no generalized tonic-clonic seizures in the previous year were considered to have infrequent seizures. Those who did not fulfill these criteria were considered to have frequent seizures. RESULTS: Patients with TLE-HS had more pronounced GM atrophy, including the ipsilateral mesial temporal structures, temporal lobe, bilateral thalami and pre/post-central gyri. Patients with TLE-NL had more subtle GM atrophy, including the ipsilateral orbitofrontal cortex, bilateral thalami and pre/post-central gyri. Both TLE-HS and TLE-NL showed increased GM volume in the contralateral pons. TLE-HS patients with frequent seizures had more pronounced GM atrophy in extra-temporal regions than TLE-HS with infrequent seizures. Patients with TLE-NL and infrequent seizures had no detectable GM atrophy. In both TLE-HS and TLE-NL, the duration of epilepsy correlated with GM atrophy in extra-hippocampal regions. CONCLUSION: Although a diffuse network GM atrophy occurs in both TLE-HS and TLE-NL, this is strikingly more evident in TLE-HS and in patients with frequent seizures. These findings suggest that neocortical atrophy in TLE is related to the ongoing seizures and epilepsy duration, while thalamic

  18. Photogenic partial seizures.

    Science.gov (United States)

    Hennessy, M J; Binnie, C D

    2000-01-01

    To establish the incidence and symptoms of partial seizures in a cohort of patients investigated on account of known sensitivity to intermittent photic stimulation and/or precipitation of seizures by environmental visual stimuli such as television (TV) screens or computer monitors. We report 43 consecutive patients with epilepsy, who had exhibited a significant EEG photoparoxysmal response or who had seizures precipitated by environmental visual stimuli and underwent detailed assessment of their photosensitivity in the EEG laboratory, during which all were questioned concerning their ictal symptoms. All patients were considered on clinical grounds to have an idiopathic epilepsy syndrome. Twenty-eight (65%) patients reported visually precipitated attacks occurring initially with maintained consciousness, in some instances evolving to a period of confusion or to a secondarily generalized seizure. Visual symptoms were most commonly reported and included positive symptoms such as coloured circles or spots, but also blindness and subjective symptoms such as "eyes going funny." Other symptoms described included nonspecific cephalic sensations, deja-vu, auditory hallucinations, nausea, and vomiting. No patient reported any clear spontaneous partial seizures, and there were no grounds for supposing that any had partial epilepsy excepting the ictal phenomenology of some or all of the visually induced attacks. These findings provide clinical support for the physiological studies that indicate that the trigger mechanism for human photosensitivity involves binocularly innervated cells located in the visual cortex. Thus the visual cortex is the seat of the primary epileptogenic process, and the photically triggered discharges and seizures may be regarded as partial with secondary generalization.

  19. Seizure recurrence after a first febrile seizure: a multivariate approach

    NARCIS (Netherlands)

    Offringa, M.; Derksen-Lubsen, G.; Bossuyt, P. M.; Lubsen, J.

    1992-01-01

    The results are presented of a follow-up study of 155 Dutch children after the first febrile seizure. Of these initially untreated children 37 per cent had had at least one, 30 per cent at least two and 17 per cent at least three subsequent seizures. The vulnerable period for recurrent seizures

  20. 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

  1. Domain similarity based orthology detection

    OpenAIRE

    Bitard-Feildel, Tristan; Kemena, Carsten; Greenwood, Jenny M; Bornberg-Bauer, Erich

    2015-01-01

    Background Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationa...

  2. Widespread EEG changes precede focal seizures.

    Directory of Open Access Journals (Sweden)

    Piero Perucca

    Full Text Available The process by which the brain transitions into an epileptic seizure is unknown. In this study, we investigated whether the transition to seizure is associated with changes in brain dynamics detectable in the wideband EEG, and whether differences exist across underlying pathologies. Depth electrode ictal EEG recordings from 40 consecutive patients with pharmacoresistant lesional focal epilepsy were low-pass filtered at 500 Hz and sampled at 2,000 Hz. Predefined EEG sections were selected immediately before (immediate preictal, and 30 seconds before the earliest EEG sign suggestive of seizure activity (baseline. Spectral analysis, visual inspection and discrete wavelet transform were used to detect standard (delta, theta, alpha, beta and gamma and high-frequency bands (ripples and fast ripples. At the group level, each EEG frequency band activity increased significantly from baseline to the immediate preictal section, mostly in a progressive manner and independently of any modification in the state of vigilance. Preictal increases in each frequency band activity were widespread, being observed in the seizure-onset zone and lesional tissue, as well as in remote regions. These changes occurred in all the investigated pathologies (mesial temporal atrophy/sclerosis, local/regional cortical atrophy, and malformations of cortical development, but were more pronounced in mesial temporal atrophy/sclerosis. Our findings indicate that a brain state change with distinctive features, in the form of unidirectional changes across the entire EEG bandwidth, occurs immediately prior to seizure onset. We postulate that these changes might reflect a facilitating state of the brain which enables a susceptible region to generate seizures.

  3. Seizure ending signs in patients with dyscognitive focal seizures.

    Science.gov (United States)

    Gavvala, Jay R; Gerard, Elizabeth E; Macken, Mícheál; Schuele, Stephan U

    2015-09-01

    Signs indicating the end of a focal seizure with loss of awareness and/or responsiveness but without progression to focal or generalized motor symptoms are poorly defined and can be difficult to determine. Not recognizing the transition from ictal to postictal behaviour can affect seizure reporting accuracy by family members and may lead to delayed or a lack of examination during EEG monitoring, erroneous seizure localization and inadequate medical intervention for prolonged seizure duration. Our epilepsy monitoring unit database was searched for focal seizures without secondary generalization for the period from 2007 to 2011. The first focal seizure in a patient with loss of awareness and/or responsiveness and/or behavioural arrest, with or without automatisms, was included. Seizures without objective symptoms or inadequate video-EEG quality were excluded. A total of 67 patients were included, with an average age of 41.7 years. Thirty-six of the patients had seizures from the left hemisphere and 29 from the right. All patients showed an abrupt change in motor activity and resumed contact with the environment as a sign of clinical seizure ending. Specific ending signs (nose wiping, coughing, sighing, throat clearing, or laughter) were seen in 23 of 47 of temporal lobe seizures and 7 of 20 extra-temporal seizures. Seizure ending signs are often subtle and the most common finding is a sudden change in motor activity and resumption of contact with the environment. More distinct signs, such as nose wiping, coughing or throat clearing, are not specific to temporal lobe onset. A higher proportion of seizures during sleep went unexamined, compared to those during wakefulness. This demonstrates that seizure semiology can be very subtle and arousals from sleep during monitoring should alert staff. Patient accounts of seizure frequency appear to be unreliable and witness reports need to be taken into account. [Published with video sequences].

  4. 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

  5. Laser-based optical detection of explosives

    CERN Document Server

    Pellegrino, Paul M; Farrell, Mikella E

    2015-01-01

    Laser-Based Optical Detection of Explosives offers a comprehensive review of past, present, and emerging laser-based methods for the detection of a variety of explosives. This book: Considers laser propagation safety and explains standard test material preparation for standoff optical-based detection system evaluation Explores explosives detection using deep ultraviolet native fluorescence, Raman spectroscopy, laser-induced breakdown spectroscopy, reflectometry, and hyperspectral imaging Examines photodissociation followed by laser-induced fluorescence, photothermal methods, cavity-enhanced absorption spectrometry, and short-pulse laser-based techniques Describes the detection and recognition of explosives using terahertz-frequency spectroscopic techniques Each chapter is authored by a leading expert on the respective technology, and is structured to supply historical perspective, address current advantages and challenges, and discuss novel research and applications. Readers are left with an in-depth understa...

  6. 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....

  7. 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...

  8. 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.

  9. Photonic crystal fiber based antibody detection

    DEFF Research Database (Denmark)

    Duval, A; Lhoutellier, M; Jensen, J B

    2004-01-01

    An original approach for detecting labeled antibodies based on strong penetration photonic crystal fibers is introduced. The target antibody is immobilized inside the air-holes of a photonic crystal fiber and the detection is realized by the means of evanescent-wave fluorescence spectroscopy...

  10. Collaborative regression-based anatomical landmark detection

    International Nuclear Information System (INIS)

    Gao, Yaozong; Shen, Dinggang

    2015-01-01

    Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head and neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods. (paper)

  11. 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)

  12. 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.

  13. 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....

  14. VISION BASED OBSTACLE DETECTION IN UAV IMAGING

    Directory of Open Access Journals (Sweden)

    S. Badrloo

    2017-08-01

    Full Text Available 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.

  15. 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......-2002), including 49,857 persons with febrile seizures and 16,481 persons with epilepsy. Overall, for children with febrile seizures compared with those without such seizures, the rate ratio for epilepsy was 5.43 (95% confidence interval: 5.19, 5.69). The risk remained high during the entire follow.......3). In conclusion, persons with a history of febrile seizures had a higher rate of epilepsy that lasted into adult life, but less than 7 percent of children with febrile seizures developed epilepsy during 23 years of follow-up. The risk was higher for those who had a family history of epilepsy, cerebral palsy...

  16. Pedestrian detection based on redundant wavelet transform

    Science.gov (United States)

    Huang, Lin; Ji, Liping; Hu, Ping; Yang, Tiejun

    2016-10-01

    Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.

  17. Longitudinal changes in seizure outcomes after resection of cerebral cavernous malformations in patients presenting with seizures: a long-term follow-up of 46 patients.

    Science.gov (United States)

    Kim, Jiha; Kim, Chi Heon; Chung, Chun Kee

    2014-08-01

    Seizure is the most common presentation in patients with cerebral cavernous malformations (CCMs). Although many articles have documented seizure outcomes after resection of CCM, few have conducted long-term follow-ups; thus, the fluctuating seizure outcomes have been neglected. The purpose of this study is to describe long-term postoperative seizure outcomes in patients with CCM and to compare seizure outcomes between patients with sporadic seizures and those with chronic seizures. Forty-six patients with CCM presenting with seizures underwent surgery. The male-to-female ratio was 1:1, and the average age at initial seizure onset was 27.6 years. The mean preoperative seizure duration was 42.7 months. Patients were divided into two groups: a chronic group (N = 20) and a sporadic group (N = 26) according to seizure frequency and duration. The mean postoperative follow-up duration was 96.3 months, and the postoperative seizure outcomes were checked annually based upon Engel's classification. After the first year of follow-up, 80.8 % of the sporadic group and 75.0 % of the chronic group were evaluated as Engel class I. These rates increased to 100.0 % and 90.0 %, respectively, at the eighth year of follow-up. Overall, 29 (63.0 %) of the 46 patients experienced changes in seizure outcomes over the follow-up period. Despite their delayed improvements, the chronic group showed less favorable outcomes throughout follow-up (p = 0.025). Long-term follow-up is indispensable for accurately assessing postoperative seizure outcomes because these outcomes change continuously. We recommend earlier surgery to achieve seizure-free status in patients with CCM. However, even in the chronic group, surgery is recommended, considering the overall delayed improvement.

  18. Termination of seizure clusters is related to the duration of focal seizures.

    Science.gov (United States)

    Ferastraoaru, Victor; Schulze-Bonhage, Andreas; Lipton, Richard B; Dümpelmann, Matthias; Legatt, Alan D; Blumberg, Julie; Haut, Sheryl R

    2016-06-01

    Clustered seizures are characterized by shorter than usual interseizure intervals and pose increased morbidity risk. This study examines the characteristics of seizures that cluster, with special attention to the final seizure in a cluster. This is a retrospective analysis of long-term inpatient monitoring data from the EPILEPSIAE project. Patients underwent presurgical evaluation from 2002 to 2009. Seizure clusters were defined by the occurrence of at least two consecutive seizures with interseizure intervals of <4 h. Other definitions of seizure clustering were examined in a sensitivity analysis. Seizures were classified into three contextually defined groups: isolated seizures (not meeting clustering criteria), terminal seizure (last seizure in a cluster), and intracluster seizures (any other seizures within a cluster). Seizure characteristics were compared among the three groups in terms of duration, type (focal seizures remaining restricted to one hemisphere vs. evolving bilaterally), seizure origin, and localization concordance among pairs of consecutive seizures. Among 92 subjects, 77 (83%) had at least one seizure cluster. The intracluster seizures were significantly shorter than the last seizure in a cluster (p = 0.011), whereas the last seizure in a cluster resembled the isolated seizures in terms of duration. Although focal only (unilateral), seizures were shorter than seizures that evolved bilaterally and there was no correlation between the seizure type and the seizure position in relation to a cluster (p = 0.762). Frontal and temporal lobe seizures were more likely to cluster compared with other localizations (p = 0.009). Seizure pairs that are part of a cluster were more likely to have a concordant origin than were isolated seizures. Results were similar for the 2 h definition of clustering, but not for the 8 h definition of clustering. We demonstrated that intracluster seizures are short relative to isolated seizures and terminal seizures. Frontal

  19. Seizure Prediction and its Applications

    Science.gov (United States)

    Iasemidis, Leon D.

    2011-01-01

    Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity, that may remain localized and/or spread and severely disrupt the brain’s normal multi-task and multi-processing function. Epileptic seizures are the hallmarks of such activity and had been considered unpredictable. It is only recently that research on the dynamics of seizure generation by analysis of the brain’s electrographic activity (EEG) has shed ample light on the predictability of seizures, and illuminated the way to automatic, prospective, long-term prediction of seizures. The ability to issue warnings in real time of impending seizures (e.g., tens of minutes prior to seizure occurrence in the case of focal epilepsy), may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a simple warning to the patient, in order to avert seizure-associated injuries, to intervention by automatic timely administration of an appropriate stimulus, for example of a chemical nature like an anti-epileptic drug (AED), electromagnetic nature like vagus nerve stimulation (VNS), deep brain stimulation (DBS), transcranial direct current (TDC) or transcranial magnetic stimulation (TMS), and/or of another nature (e.g., ultrasonic, cryogenic, biofeedback operant conditioning). It is thus expected that seizure prediction could readily become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems. PMID:21939848

  20. Aborting Seizures by Painful Stimulation

    Directory of Open Access Journals (Sweden)

    R. L. Carasso

    1992-01-01

    Full Text Available It has been well established that serious consequences may result from allowing seizures to continue. The opportunities for early interruption of seizures by medication is often restricted to medical personnel, leaving non-trained bystanders unable to intervene. We were able to interrupt seizures (including status epilepticus by application of painful dorsiflexion. The mode of action that enables pain to elevate the seizure threshold remains to be elucidated, although the phenomenon is consistent with earlier laboratory studies in experimental epilepsy. The technique may be recommended as an effective and easily learned procedure that may have wide applicability.

  1. Power Consumption Based Android Malware Detection

    OpenAIRE

    Hongyu Yang; Ruiwen Tang

    2016-01-01

    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...

  2. 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....

  3. Why are seizures rare in rapid eye movement sleep? Review of the frequency of seizures in different sleep stages.

    Science.gov (United States)

    Ng, Marcus; Pavlova, Milena

    2013-01-01

    Since the formal characterization of sleep stages, there have been reports that seizures may preferentially occur in certain phases of sleep. Through ascending cholinergic connections from the brainstem, rapid eye movement (REM) sleep is physiologically characterized by low voltage fast activity on the electroencephalogram, REMs, and muscle atonia. Multiple independent studies confirm that, in REM sleep, there is a strikingly low proportion of seizures (~1% or less). We review a total of 42 distinct conventional and intracranial studies in the literature which comprised a net of 1458 patients. Indexed to duration, we found that REM sleep was the most protective stage of sleep against focal seizures, generalized seizures, focal interictal discharges, and two particular epilepsy syndromes. REM sleep had an additional protective effect compared to wakefulness with an average 7.83 times fewer focal seizures, 3.25 times fewer generalized seizures, and 1.11 times fewer focal interictal discharges. In further studies REM sleep has also demonstrated utility in localizing epileptogenic foci with potential translation into postsurgical seizure freedom. Based on emerging connectivity data in sleep, we hypothesize that the influence of REM sleep on seizures is due to a desynchronized EEG pattern which reflects important connectivity differences unique to this sleep stage.

  4. Neuropeptides and seizures.

    Science.gov (United States)

    Snead, O C

    1986-11-01

    There are four lines of evidence for or against a role of neuropeptides in epilepsy: Administration of a variety of opiate agonists into the ventricles or brain of animals produces a constellation of electrical and behavioral changes, seemingly receptor-specific, both sensitive to the specific opiate antagonist naloxone as well as certain anticonvulsant drugs. The primary reservation concerning these data in terms of their relevance to epilepsy regards the fact that the peptides are exogenously administered in relatively high doses. Hence, these data may reflect neurotoxic effects of peptides rather than physiologic function. A variety of opiate agonists are anticonvulsant and naloxone shortens the postictal state in some experimental seizure models. One could attempt to reconcile these data with those in No. 1 by hypothesizing that the spikes and behavioral changes examined in the latter experimental parodynes represented a sort of isolated model of the postictal state. Naloxone has little effect in clinical epilepsy. These data are far from conclusive for two reasons. First, few patients have been studied. Second, because of the issue of opiate receptor heterogeneity and the high doses of naloxone needed experimentally to block non-mu opiate effects, the doses of naloxone used clinically to date are too low to rule out possible delta- or epsilon-mediated effects. The negative clinical data are illustrative of the dangers and difficulties of extrapolating data generated in animal models of seizures to the human condition. ACTH, a peptide that is derived from the same precursor molecule as beta-endorphin, is clearly an effective anticonvulsant in certain childhood seizure states. However, whether this is due to a direct or indirect (that is, cortisol) effect on brain is far from clear. Paradoxically, in contradistinction to other data concerning pro- and anticonvulsant properties of various opioid peptides, there is no animal model of infantile spasms to help

  5. 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)

  6. Characteristics of the initial seizure in familial febrile seizures

    NARCIS (Netherlands)

    M. van Stuijvenberg (Margriet); E. van Beijeren; N.H. Wils; G. Derksen-Lubsen (Gerarda); C.M. van Duijn (Cornelia); H.A. Moll (Henriëtte)

    1999-01-01

    textabstractComplex seizure characteristics in patients with a positive family history were studied to define familial phenotype subgroups of febrile seizures. A total of 51 children with one or more affected first degree relatives and 177 without an affected first degree

  7. 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.

  8. 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.

  9. 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...

  10. Towards acute pediatric status epilepticus intervention teams: Do we need "Seizure Codes"?

    Science.gov (United States)

    Stredny, Coral M; Abend, Nicholas S; Loddenkemper, Tobias

    2018-05-01

    To identify areas of treatment delay and barriers to care in pediatric status epilepticus, review ongoing quality improvement initiatives, and provide suggestions for further innovations to improve and standardize these patient care processes. Narrative review of current status epilepticus management algorithms, anti-seizure medication administration and outcomes associated with delays, and initiatives to improve time to treatment. Articles reviewing or reporting quality improvement initiatives were identified through a PubMed search with keywords "status epilepticus," "quality improvement," "guideline adherence," and/or "protocol;" references of included articles were also reviewed. Rapid initiation and escalation of status epilepticus treatment has been associated with shortened seizure duration and more favorable outcomes. Current evidence-based guidelines for management of status epilepticus propose medication algorithms with suggested times for each management step. However, time to antiseizure medication administration for pediatric status epilepticus remains delayed in both the pre- and in-hospital settings. Barriers to timely treatment include suboptimal preventive care, inaccurate seizure detection, infrequent or restricted use of home rescue medications by caregivers and pre-hospital emergency personnel, delayed summoning and arrival of emergency personnel, and use of inappropriately dosed medications. Ongoing quality improvement initiatives in the pre- and in-hospital settings targeting these barriers are reviewed. Improved preventive care, seizure detection, and rescue medication education may advance pre-hospital management, and we propose the use of acute status epilepticus intervention teams to initiate and incorporate in-hospital interventions as time-sensitive "Seizure Code" emergencies. Copyright © 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  11. 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.

  12. Epileptic Seizure Prediction Using a New Similarity Index for Chaotic Signals

    Science.gov (United States)

    Niknazar, Hamid; Nasrabadi, Ali Motie

    Epileptic seizures are generated by abnormal activity of neurons. The prediction of epileptic seizures is an important issue in the field of neurology, since it may improve the quality of life of patients suffering from drug resistant epilepsy. In this study a new similarity index based on symbolic dynamic techniques which can be used for extracting behavior of chaotic time series is presented. Using Freiburg EEG dataset, it is found that the method is able to detect the behavioral changes of the neural activity prior to epileptic seizures, so it can be used for prediction of epileptic seizure. A sensitivity of 63.75% with 0.33 false positive rate (FPR) in all 21 patients and sensitivity of 96.66% with 0.33 FPR in eight patients were achieved using the proposed method. Moreover, the method was evaluated by applying on Logistic and Tent map with different parameters to demonstrate its robustness and ability in determining similarity between two time series with the same chaotic characterization.

  13. Types of Seizures Affecting Individuals with TSC

    Science.gov (United States)

    ... Policy Sitemap Learn Engage Donate About TSC Epilepsy/Seizure Disorders Seizures remain one of the most common neurological ... TSC Brain and Neurological Function Brain Abnormalities Epilepsy/Seizure Disorders Infantile Spasms Epilepsy in Adults with TSC Epilepsy ...

  14. Development of a validated clinical case definition of generalized tonic-clonic seizures for use by community-based health care providers.

    Science.gov (United States)

    Anand, Krishnan; Jain, Satish; Paul, Eldho; Srivastava, Achal; Sahariah, Sirazul A; Kapoor, Suresh K

    2005-05-01

    To develop and test a clinical case definition for identification of generalized tonic-clonic seizures (GTCSs) by community-based health care providers. To identify symptoms that can help identify GTCSs, patients with history of a jerky movements or rigidity in any part of the body ever in life were recruited from three sites: the community, secondary care hospital, and tertiary care hospital. These patients were administered a 14-item structured interview schedule focusing on the circumstances surrounding the seizure. Subsequently, a neurologist examined each patient and, based on available investigations, classified them as GTCS or non-GTCS cases. A logistic regression analysis was performed to select symptoms that were to be used for case definition of GTCSs. Validity parameters for the case definition at different cutoff points were calculated in another set of subjects. In total, 339 patients were enrolled in the first phase of the study. The tertiary care hospital contributed the maximal number of GTCS cases, whereas cases of non-GTCS were mainly from the community. At the end of phase I, the questionnaire was shortened from 14 to eight questions based on statistical association and clinical judgment. After phase II, which was conducted among 170 subjects, three variables were found to be significantly related to the presence of GTCSs by logistic regression: absence of stress (13.1; 4.1-41.3), presence of frothing (13.7; 4.0-47.3), and occurrence in sleep (8.3; 2.0-34.9). As a case definition using only three variables did not provide sufficient specificity, three more variables were added based on univariate analysis of the data (incontinence during the episode and unconsciousness) and review of literature (injury during episode). A case definition consisting of giving one point to an affirmative answer for each of the six questions was tested. At a cutoff point of four, sensitivity was 56.9 (47.4-66.0) and specificity, 96.3 (86.2-99.4). Among the 197 GTCS

  15. Seizure clusters: characteristics and treatment.

    Science.gov (United States)

    Haut, Sheryl R

    2015-04-01

    Many patients with epilepsy experience 'clusters' or flurries of seizures, also termed acute repetitive seizures (ARS). Seizure clustering has a significant impact on health and quality of life. This review summarizes recent advances in the definition and neurophysiologic understanding of clustering, the epidemiology and risk factors for clustering and both inpatient and outpatient clinical implications. New treatments for seizure clustering/ARS are perhaps the area of greatest recent progress. Efforts have focused on creating a uniform definition of a seizure cluster. In neurophysiologic studies of refractory epilepsy, seizures within a cluster appear to be self-triggering. Clinical progress has been achieved towards a more precise prevalence of clustering, and consensus guidelines for epilepsy monitoring unit safety. The greatest recent advances are in the study of nonintravenous route of benzodiazepines as rescue medications for seizure clusters/ARS. Rectal benzodiazepines have been very effective but barriers to use exist. New data on buccal, intramuscular and intranasal preparations are anticipated to lead to a greater number of approved treatments. Progesterone may be effective for women who experience catamenial clusters. Seizure clustering is common, particularly in the setting of medically refractory epilepsy. Clustering worsens health and quality of life, and the field requires greater focus on clarifying of definition and clinical implications. Progress towards the development of nonintravenous routes of benzodiazepines has the potential to improve care in this area.

  16. [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.

  17. 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.

  18. Intraoperative seizures and seizures outcome in patients underwent awake craniotomy.

    Science.gov (United States)

    Yuan, Yang; Peizhi, Zhou; Xiang, Wang; Yanhui, Liu; Ruofei, Liang; Shu, Jiang; Qing, Mao

    2016-11-25

    Awake craniotomies (AC) could reduce neurological deficits compared with patients under general anesthesia, however, intraoperative seizure is a major reason causing awake surgery failure. The purpose of the study was to give a comprehensive overview the published articles focused on seizure incidence in awake craniotomy. Bibliographic searches of the EMBASE, MEDLINE,were performed to identify articles and conference abstracts that investigated the intraoperative seizure frequency of patients underwent AC. Twenty-five studies were included in this meta-analysis. Among the 25 included studies, one was randomized controlled trials and 5 of them were comparable studies. The pooled data suggested the general intraoperative seizure(IOS) rate for patients with AC was 8%(fixed effect model), sub-group analysis identified IOS rate for glioma patients was 8% and low grade patients was 10%. The pooled data showed early seizure rates of AC patients was 11% and late seizure rates was 35%. This systematic review and meta-analysis shows that awake craniotomy is a safe technique with relatively low intraoperative seizure occurrence. However, few RCTs were available, and the acquisition of further evidence through high-quality RCTs is highly recommended.

  19. Treating acute seizures with benzodiazepines: does seizure duration matter?

    Science.gov (United States)

    Naylor, David E

    2014-10-01

    Several clinical trials have shown improved seizure control and outcome by early initiation of treatment with benzodiazepines, before arrival in the emergency department and before intravenous access can be established. Here, evidence is provided and reviewed for rapid treatment of acute seizures in order to avoid the development of benzodiazepine pharmacoresistance and the emergence of self-sustaining status epilepticus. Alterations in the physiology, pharmacology, and postsynaptic level of GABA-A receptors can develop within minutes to an hour and hinder the ability of synaptic inhibition to stop seizures while also impairing the efficacy of GABAergic agents, such as benzodiazepines, to boost impaired inhibition. In addition, heightened excitatory transmission further exacerbates the inhibitory/excitatory balance and makes seizure control even more resistant to treatment. The acute increase in the surface expression of NMDA receptors during prolonged seizures also may cause excitotoxic injury, cell death, and other pathological expressions and re-arrangements of receptor subunits that all contribute to long-term sequelae such as cognitive impairment and chronic epilepsy. In conclusion, a short window of opportunity exists when seizures are maximally controlled by first-line benzodiazepine treatment. After that, multiple pathological mechanisms quickly become engaged that make seizures increasingly more difficult to control with high risk for long-term harm.

  20. Nanomaterials based biosensors for cancer biomarker detection

    International Nuclear Information System (INIS)

    Malhotra, Bansi D; Kumar, Saurabh; Pandey, Chandra Mouli

    2016-01-01

    Biosensors have enormous potential to contribute to the evolution of new molecular diagnostic techniques for patients suffering with cancerous diseases. A major obstacle preventing faster development of biosensors pertains to the fact that cancer is a highly complex set of diseases. The oncologists currently rely on a few biomarkers and histological characterization of tumors. Some of the signatures include epigenetic and genetic markers, protein profiles, changes in gene expression, and post-translational modifications of proteins. These molecular signatures offer new opportunities for development of biosensors for cancer detection. In this context, conducting paper has recently been found to play an important role towards the fabrication of a biosensor for cancer biomarker detection. In this paper we will focus on results of some of the recent studies obtained in our laboratories relating to fabrication and application of nanomaterial modified paper based biosensors for cancer biomarker detection. (paper)

  1. 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.

  2. 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.

  3. 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%.

  4. 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.

  5. 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.

  6. IMAGE ANALYSIS BASED ON EDGE DETECTION TECHNIQUES

    Institute of Scientific and Technical Information of China (English)

    纳瑟; 刘重庆

    2002-01-01

    A method that incorporates edge detection technique, Markov Random field (MRF), watershed segmentation and merging techniques was presented for performing image segmentation and edge detection tasks. It first applies edge detection technique to obtain a Difference In Strength (DIS) map. An initial segmented result is obtained based on K-means clustering technique and the minimum distance. Then the region process is modeled by MRF to obtain an image that contains different intensity regions. The gradient values are calculated and then the watershed technique is used. DIS calculation is used for each pixel to define all the edges (weak or strong) in the image. The DIS map is obtained. This help as priority knowledge to know the possibility of the region segmentation by the next step (MRF), which gives an image that has all the edges and regions information. In MRF model,gray level l, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The segmentation results are improved by using watershed algorithm. After all pixels of the segmented regions are processed, a map of primitive region with edges is generated. The edge map is obtained using a merge process based on averaged intensity mean values. A common edge detectors that work on (MRF) segmented image are used and the results are compared. The segmentation and edge detection result is one closed boundary per actual region in the image.

  7. European Stroke Organisation guidelines for the management of post-stroke seizures and epilepsy

    DEFF Research Database (Denmark)

    Holtkamp, Martin; Beghi, Ettore; Benninger, Felix

    2017-01-01

    -based guidelines on the management of post-stroke seizures and epilepsy. Method A writing committee of six clinicians and researchers from five European countries and Israel identified seven questions relating to prevention of (further) post-stroke seizures and epilepsy and to amelioration of functional outcome......Background Following stroke, acute symptomatic seizures (manifestation within seven days) and epilepsy, i.e. occurrence of at least one unprovoked seizure (manifestation after more than seven days), are reported in 3–6% and up to 12% of patients, respectively. Incidence of acute symptomatic...... seizures is higher in intracranial haemorrhage (10–16%) than in ischaemic stroke (2–4%). Acute symptomatic seizures and unprovoked seizure may be associated with unfavourable functional outcome and increased mortality. In view of the clinical relevance, the European Stroke Organisation has issued evidence...

  8. 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.

  9. 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.

  10. Automated image based prominent nucleoli detection.

    Science.gov (United States)

    Yap, Choon K; Kalaw, Emarene M; Singh, Malay; Chong, Kian T; Giron, Danilo M; Huang, Chao-Hui; Cheng, Li; Law, Yan N; Lee, Hwee Kuan

    2015-01-01

    Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

  11. Automated image based prominent nucleoli detection

    Directory of Open Access Journals (Sweden)

    Choon K Yap

    2015-01-01

    Full Text Available Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

  12. Advances in neutron based bulk explosive detection

    Science.gov (United States)

    Gozani, Tsahi; Strellis, Dan

    2007-08-01

    Neutron based explosive inspection systems can detect a wide variety of national security threats. The inspection is founded on the detection of characteristic gamma rays emitted as the result of neutron interactions with materials. Generally these are gamma rays resulting from thermal neutron capture and inelastic scattering reactions in most materials and fast and thermal neutron fission in fissile (e.g.235U and 239Pu) and fertile (e.g.238U) materials. Cars or trucks laden with explosives, drugs, chemical agents and hazardous materials can be detected. Cargo material classification via its main elements and nuclear materials detection can also be accomplished with such neutron based platforms, when appropriate neutron sources, gamma ray spectroscopy, neutron detectors and suitable decision algorithms are employed. Neutron based techniques can be used in a variety of scenarios and operational modes. They can be used as stand alones for complete scan of objects such as vehicles, or for spot-checks to clear (or validate) alarms indicated by another inspection system such as X-ray radiography. The technologies developed over the last two decades are now being implemented with good results. Further advances have been made over the last few years that increase the sensitivity, applicability and robustness of these systems. The advances range from the synchronous inspection of two sides of vehicles, increasing throughput and sensitivity and reducing imparted dose to the inspected object and its occupants (if any), to taking advantage of the neutron kinetic behavior of cargo to remove systematic errors, reducing background effects and improving fast neutron signals.

  13. Body-Sensor-Network-Based Spasticity Detection.

    Science.gov (United States)

    Misgeld, Berno J E; Luken, Markus; Heitzmann, Daniel; Wolf, Sebastian I; Leonhardt, Steffen

    2016-05-01

    Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity Network by Medit Aachen body sensor network (BSN). For this, a new electromyography (EMG) sensor node was developed and employed in human locomotion. Following an analysis of the clinical gait data of patients with unilateral cerebral palsy, a novel algorithm was developed based on the idea to detect coactivation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated joint rigidity. The algorithm applies a cross-correlation function to the EMG signals of two antagonistically working muscles and subsequent weighting using a Blackman window. The result is a coactivation index which is also weighted by the signal equivalent energy to exclude positive detection of inactive muscles. Our experimental study indicates good performance in the detection of coactive muscles associated with spasticity from clinical data as well as measurements from a BSN in qualitative comparison with the Modified Ashworth Scale as classified by clinical experts. Possible applications of the new algorithm include (but are not limited to) use in robotic sensorimotor therapy to reduce the effect of spasticity.

  14. 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.

  15. HMPAO-SPECT in cerebral seizures

    International Nuclear Information System (INIS)

    Gruenwald, F.; Bockisch, A.; Reichmann, K.; Ammari, B.; Hotze, A.; Biersack, H.J.; Durwen, H.; Buelau, P.; Elger, C.E.; Rohde, A.; Penin, H.

    1988-01-01

    In nine patients with suspected psychogenic seizures and in three patients with proven epileptic seizures HMPAO-SPECT was performed prior to and during seizure. In the patients with lateron-proven psychogenic seizures no, or only slight, changes of regional cerebral blood flow were found. Patients with proven epilepsy revealed partly normal findings interictally but during seizure a markedly increased circumscript blood flow was found in all patients. Even though PET is superior to SPECT with respect to spatial resolution, in the diagnosis of seizures HMPAO-SPECT has the advantage of enabling injection of the tracer during the seizure and the performance of the SPECT study subsequently. (orig.) [de

  16. 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)

  17. Management of Reflex Anoxic Seizures

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2013-10-01

    Full Text Available Investigators at the Roald Dahl EEG Unit, Alder Hey Children’s NHS Foundation, Liverpool, UK, review the definition, pathophysiology, clinical presentation, and management of reflex anoxic seizures (RAS in children.

  18. 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.)

  19. 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.

  20. 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...... gearbox. Only the generator speed measurement which is available in even simple wind turbine control systems is used as input. Consequently this proposed scheme does not need additional sensors and computers for monitoring the condition of the wind gearbox. The scheme is evaluated on a wide-spread wind...

  1. 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...

  2. Unexpected marked seizure improvement in paediatric epilepsy surgery candidates

    DEFF Research Database (Denmark)

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

    2017-01-01

    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...... 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......: At the first evaluation point in 2012, 13 patients were withdrawn from the epilepsy surgery programme due to unexpected marked improvement. In 2015, 6 of them were still seizure free. They had unexpected seizure freedom due to change in AED treatment (n=3) or after a febrile episode (n=3). The mean number...

  3. Indomethacin treatment prior to pentylenetetrazole-induced seizures downregulates the expression of il1b and cox2 and decreases seizure-like behavior in zebrafish larvae.

    Science.gov (United States)

    Barbalho, Patrícia Gonçalves; Lopes-Cendes, Iscia; Maurer-Morelli, Claudia Vianna

    2016-03-09

    It has been demonstrated that the zebrafish model of pentylenetetrazole (PTZ)-evoked seizures and the well-established rodent models of epilepsy are similar pertaining to behavior, electrographic features, and c-fos expression. Although this zebrafish model is suitable for studying seizures, to date, inflammatory response after seizures has not been investigated using this model. Because a relationship between epilepsy and inflammation has been established, in the present study we investigated the transcript levels of the proinflammatory cytokines interleukin-1 beta (il1b) and cyclooxygenase-2 (cox2a and cox2b) after PTZ-induced seizures in the brain of zebrafish 7 days post fertilization. Furthermore, we exposed the fish to the nonsteroidal anti-inflammatory drug indomethacin prior to PTZ, and we measured its effect on seizure latency, number of seizure behaviors, and mRNA expression of il1b, cox2b, and c-fos. We used quantitative real-time PCR to assess the mRNA expression of il1b, cox2a, cox2b, and c-fos, and visual inspection was used to monitor seizure latency and the number of seizure-like behaviors. We found a short-term upregulation of il1b, and we revealed that cox2b, but not cox2a, was induced after seizures. Indomethacin treatment prior to PTZ-induced seizures downregulated the mRNA expression of il1b, cox2b, and c-fos. Moreover, we observed that in larvae exposed to indomethacin, seizure latency increased and the number of seizure-like behaviors decreased. This is the first study showing that il1b and cox-2 transcripts are upregulated following PTZ-induced seizures in zebrafish. In addition, we demonstrated the anticonvulsant effect of indomethacin based on (1) the inhibition of PTZ-induced c-fos transcription, (2) increase in seizure latency, and (3) decrease in the number of seizure-like behaviors. Furthermore, anti-inflammatory effect of indomethacin is clearly demonstrated by the downregulation of the mRNA expression of il1b and cox2b. Our results

  4. 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.

  5. Global contrast based salient region detection

    KAUST Repository

    Cheng, Ming-Ming; Zhang, Guo-Xin; Mitra, Niloy J.; Huang, Xiaolei; Hu, Shi-Min

    2011-01-01

    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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Population dose-response analysis of daily seizure count following vigabatrin therapy in adult and pediatric patients with refractory complex partial seizures.

    Science.gov (United States)

    Nielsen, Jace C; Hutmacher, Matthew M; Wesche, David L; Tolbert, Dwain; Patel, Mahlaqa; Kowalski, Kenneth G

    2015-01-01

    Vigabatrin is an irreversible inhibitor of γ-aminobutyric acid transaminase (GABA-T) and is used as an adjunctive therapy for adult patients with refractory complex partial seizures (rCPS). The purpose of this investigation was to describe the relationship between vigabatrin dosage and daily seizure rate for adults and children with rCPS and identify relevant covariates that might impact seizure frequency. This population dose-response analysis used seizure-count data from three pediatric and two adult randomized controlled studies of rCPS patients. A negative binomial distribution model adequately described daily seizure data. Mean seizure rate decreased with time after first dose and was described using an asymptotic model. Vigabatrin drug effects were best characterized by a quadratic model using normalized dosage as the exposure metric. Normalized dosage was an estimated parameter that allowed for individualized changes in vigabatrin exposure based on body weight. Baseline seizure rate increased with decreasing age, but age had no impact on vigabatrin drug effects after dosage was normalized for body weight differences. Posterior predictive checks indicated the final model was capable of simulating data consistent with observed daily seizure counts. Total normalized vigabatrin dosages of 1, 3, and 6 g/day were predicted to reduce seizure rates 23.2%, 45.6%, and 48.5%, respectively. © 2014, The American College of Clinical Pharmacology.

  11. 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.

  12. 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.

  13. Stress, anxiety, depression, and epilepsy: investigating the relationship between psychological factors and seizures.

    Science.gov (United States)

    Thapar, Ajay; Kerr, Michael; Harold, Gordon

    2009-01-01

    The goal of the study described here was to examine the interrelationship between psychological factors (anxiety, stress, and depression) and seizures. In this longitudinal cohort study, data on anxiety, depression, perceived stress, and seizure recency (time since last seizure) and frequency were collected at two time points using standard validated questionnaire measures. Empirically based models with psychological factors explaining change in (1) seizure recency and (2) seizure frequency scores across time were specified. We then tested how these psychological factors acted together in predicting seizure recency and frequency. Our data were used to test whether these models were valid for the study population. Latent variable structural equation modeling was used for the analysis. Four hundred thirty-three of the 558 individuals who initially consented to participate provided two waves of data for this analysis. Stress (beta=0.25, Panxiety (beta=0.30, Pdepression (beta=0.30, Pdepression that mediated the relationship of both anxiety and stress with modeled change in seizure recency (beta=0.19, PDepression mediates the relationship between stress and anxiety and change in seizure recency and seizure frequency. These findings highlight the importance of depression management in addition to seizure management in the assessment and treatment of epilepsy in an adult population.

  14. Hyponatraemia and seizures after ecstasy use

    Science.gov (United States)

    Holmes, S.; Banerjee, A.; Alexander, W.

    1999-01-01

    A patient presented to our unit with seizures and profound hyponatraemia after ingestion of a single tablet of ecstasy. The seizures proved resistant to therapy and ventilation on the intensive care unit was required. Resolution of the seizures occurred on correction of the metabolic abnormalities. The pathogenesis of seizures and hyponatraemia after ecstasy use is discussed. Ecstasy use should be considered in any young patient presenting with unexplained seizures and attention should be directed towards electrolyte levels, particularly sodium.


Keywords: ecstasy; seizures; hyponatraemia PMID:10396584

  15. 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)

  16. The effect of albendazole treatment on seizure outcomes in patients with symptomatic neurocysticercosis.

    Science.gov (United States)

    Romo, Matthew L; Wyka, Katarzyna; Carpio, Arturo; Leslie, Denise; Andrews, Howard; Bagiella, Emilia; Hauser, W Allen; Kelvin, Elizabeth A

    2015-11-01

    Randomized controlled trials have found an inconsistent effect of anthelmintic treatment on long-term seizure outcomes in neurocysticercosis. The objective of this study was to further explore the effect of albendazole treatment on long-term seizure outcomes and to determine if there is evidence for a differential effect by seizure type. In this trial, 178 patients with active or transitional neurocysticercosis cysts and new-onset symptoms were randomized to 8 days of treatment with albendazole (n=88) or placebo (n=90), both with prednisone, and followed for 24 months. We used negative binomial regression and logistic regression models to determine the effect of albendazole on the number of seizures and probability of recurrent or new-onset seizures, respectively, over follow-up. Treatment with albendazole was associated with a reduction in the number of seizures during 24 months of follow-up, but this was only significant for generalized seizures during months 1-12 (unadjusted rate ratio [RR] 0.19; 95% CI: 0.04-0.91) and months 1-24 (unadjusted RR 0.06; 95% CI: 0.01-0.57). We did not detect a significant effect of albendazole on reducing the number of focal seizures or on the probability of having a seizure, regardless of seizure type or time period. Albendazole treatment may be associated with some symptomatic improvement; however, this association seems to be specific to generalized seizures. Future research is needed to identify strategies to better reduce long-term seizure burden in patients with neurocysticercosis. © The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  17. Comic image understanding based on polygon detection

    Science.gov (United States)

    Li, Luyuan; Wang, Yongtao; Tang, Zhi; Liu, Dong

    2013-01-01

    Comic image understanding aims to automatically decompose scanned comic page images into storyboards and then identify the reading order of them, which is the key technique to produce digital comic documents that are suitable for reading on mobile devices. In this paper, we propose a novel comic image understanding method based on polygon detection. First, we segment a comic page images into storyboards by finding the polygonal enclosing box of each storyboard. Then, each storyboard can be represented by a polygon, and the reading order of them is determined by analyzing the relative geometric relationship between each pair of polygons. The proposed method is tested on 2000 comic images from ten printed comic series, and the experimental results demonstrate that it works well on different types of comic images.

  18. Low complexity pixel-based halftone detection

    Science.gov (United States)

    Ok, Jiheon; Han, Seong Wook; Jarno, Mielikainen; Lee, Chulhee

    2011-10-01

    With the rapid advances of the internet and other multimedia technologies, the digital document market has been growing steadily. Since most digital images use halftone technologies, quality degradation occurs when one tries to scan and reprint them. Therefore, it is necessary to extract the halftone areas to produce high quality printing. In this paper, we propose a low complexity pixel-based halftone detection algorithm. For each pixel, we considered a surrounding block. If the block contained any flat background regions, text, thin lines, or continuous or non-homogeneous regions, the pixel was classified as a non-halftone pixel. After excluding those non-halftone pixels, the remaining pixels were considered to be halftone pixels. Finally, documents were classified as pictures or photo documents by calculating the halftone pixel ratio. The proposed algorithm proved to be memory-efficient and required low computation costs. The proposed algorithm was easily implemented using GPU.

  19. Aptamer Based Microsphere Biosensor for Thrombin Detection

    Directory of Open Access Journals (Sweden)

    Xudong Fan

    2006-08-01

    Full Text Available We have developed an optical microsphere resonator biosensor using aptamer asreceptor for the measurement of the important biomolecule thrombin. The sphere surface ismodified with anti-thrombin aptamer, which has excellent binding affinity and selectivityfor thrombin. Binding of the thrombin at the sphere surface is monitored by the spectralposition of the microsphere’s whispering gallery mode resonances. A detection limit on theorder of 1 NIH Unit/mL is demonstrated. Control experiments with non-aptameroligonucleotide and BSA are also carried out to confirm the specific binding betweenaptamer and thrombin. We expect that this demonstration will lead to the development ofhighly sensitive biomarker sensors based on aptamer with lower cost and higher throughputthan current technology.

  20. 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.

  1. 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 ...

  2. Single photon emission computed tomography in children with idiopathic seizures

    International Nuclear Information System (INIS)

    Hara, Masafumi; Takahashi, Mutsumasa; Kojima, Akihiro; Shimomura, Osamu; Kinoshita, Rumi; Tomiguchi, Seiji; Taku, Keiichi; Miike, Teruhisa

    1991-01-01

    Single photon emission computed tomography (SPECT) with N-isoprophyl-p [ 123 I]-iodoamphetamine (IMP), X-ray computed tomography (X-CT), and magnetic resonance imaging (MRI) were performed in 20 children with idiopathic seizures. In children with idiopathic seizures, SPECT could detect the abnormal sites at the highest rate (45%) compared with CT (10%) and MRI (12%), but the abnormal sites on SPECT correlated poorly with the foci on electroencephalograph (EEG). Idiopathic epilepsy with hypoperfusion on SPECT was refractory to treatment and was frequently associated with mental and/or developmental retardation. Perfusion defects on SPECT scans probably affect the development and maturation of the brain in children. (author)

  3. Exploring the time-frequency content of high frequency oscillations for automated identification of seizure onset zone in epilepsy.

    Science.gov (United States)

    Liu, Su; Sha, Zhiyi; Sencer, Altay; Aydoseli, Aydin; Bebek, Nerse; Abosch, Aviva; Henry, Thomas; Gurses, Candan; Ince, Nuri Firat

    2016-04-01

    High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are considered as promising clinical biomarkers of epileptogenic regions in the brain. The aim of this study is to improve and automatize the detection of HFOs by exploring the time-frequency content of iEEG and to investigate the seizure onset zone (SOZ) detection accuracy during the sleep, awake and pre-ictal states in patients with epilepsy, for the purpose of assisting the localization of SOZ in clinical practice. Ten-minute iEEG segments were defined during different states in eight patients with refractory epilepsy. A three-stage algorithm was implemented to detect HFOs in these segments. First, an amplitude based initial detection threshold was used to generate a large pool of HFO candidates. Then distinguishing features were extracted from the time and time-frequency domain of the raw iEEG and used with a Gaussian mixture model clustering to isolate HFO events from other activities. The spatial distribution of HFO clusters was correlated with the seizure onset channels identified by neurologists in seven patient with good surgical outcome. The overlapping rates of localized channels and seizure onset locations were high in all states. The best result was obtained using the iEEG data during sleep, achieving a sensitivity of 81%, and a specificity of 96%. The channels with maximum number of HFOs identified epileptogenic areas where the seizures occurred more frequently. The current study was conducted using iEEG data collected in realistic clinical conditions without channel pre-exclusion. HFOs were investigated with novel features extracted from the entire frequency band, and were correlated with SOZ in different states. The results indicate that automatic HFO detection with unsupervised clustering methods exploring the time-frequency content of raw iEEG can be efficiently used to identify the epileptogenic zone with an accurate and efficient manner.

  4. 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

  5. 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.)

  6. Galanin gene transfer curtails generalized seizures in kindled rats without altering hippocampal synaptic plasticity

    DEFF Research Database (Denmark)

    Kanter-Schlifke, I; Toft Sørensen, Andreas; Ledri, M

    2007-01-01

    Gene therapy-based overexpression of endogenous seizure-suppressing molecules represents a promising treatment strategy for epilepsy. Viral vector-based overexpression of the neuropeptide galanin has been shown to effectively suppress generalized seizures in various animal models of epilepsy...

  7. Aminophylline increases seizure length during electroconvulsive therapy.

    Science.gov (United States)

    Stern, L; Dannon, P N; Hirschmann, S; Schriber, S; Amytal, D; Dolberg, O T; Grunhaus, L

    1999-12-01

    Electroconvulsive therapy (ECT) is considered to be one of the most effective treatments for patients with major depression and persistent psychosis. Seizure characteristics probably determine the therapeutic effect of ECT; as a consequence, short seizures are accepted as one of the factors of poor outcome. During most ECT courses seizure threshold increases and seizure duration decreases. Methylxanthine preparations, caffeine, and theophylline have been used to prolong seizure duration. The use of aminophylline, more readily available than caffeine, has not been well documented. The objective of this study was to test the effects of aminophylline on seizure length. Fourteen drug-free patients with diagnoses of affective disorder or psychotic episode receiving ECT participated in this study. Seizure length was assessed clinically and per EEG. Statistical comparisons were done using paired t tests. A significant increase (p < 0.04) in seizure length was achieved and maintained on three subsequent treatments with aminophylline. No adverse events were noted from the addition of aminophylline.

  8. Multiple Sclerosis: Can It Cause Seizures?

    Science.gov (United States)

    ... multiple sclerosis and epilepsy? Answers from B Mark Keegan, M.D. Epileptic seizures are more common in ... controlled with anti-seizure medication. With B Mark Keegan, M.D. Lund C, et al. Multiple sclerosis ...

  9. 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.

  10. Temperature, age, and recurrence of febrile seizure

    NARCIS (Netherlands)

    M. van Stuijvenberg (Margriet); E.W. Steyerberg (Ewout); G. Derksen-Lubsen (Gerarda); H.A. Moll (Henriëtte)

    1998-01-01

    textabstractOBJECTIVE: Prediction of a recurrent febrile seizure during subsequent episodes of fever. DESIGN: Study of the data of the temperatures, seizure recurrences, and baseline patient characteristics that were collected at a randomized placebo controlled trial of ibuprofen

  11. 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.

  12. Different ketogenesis strategies lead to disparate seizure outcomes.

    Science.gov (United States)

    Dolce, Alison; Santos, Polan; Chen, Weiran; Hoke, Ahmet; Hartman, Adam L

    2018-07-01

    Despite the introduction of new medicines to treat epilepsy over the last 50 years, the number of patients with poorly-controlled seizures remains unchanged. Metabolism-based therapies are an underutilized treatment option for this population. We hypothesized that two different means of systemic ketosis, the ketogenic diet and intermittent fasting, would differ in their acute seizure test profiles and mitochondrial respiration. Male NIH Swiss mice (aged 3-4 weeks) were fed for 12-13 days using one of four diet regimens: ketogenic diet (KD), control diet matched to KD for protein content and micronutrients (CD), or CD with intermittent fasting (24 h feed/24 h fast) (CD-IF), tested post-feed or post-fast. Mice were subject to the 6 Hz threshold test or, in separate cohorts, after injection of kainic acid in doses based on their weight (Cohort I) or a uniform dose regardless of weight (Cohort II). Mitochondrial respiration was tested in brain tissue isolated from similarly-fed seizure-naïve mice. KD mice were protected against 6 Hz-induced seizures but had more severe seizure scores in the kainic acid test (Cohorts I & II), the opposite of CD-IF mice. No differences were noted in mitochondrial respiration between diet regimens. KD and CD-IF do not share identical antiseizure mechanisms. These differences were not explained by differences in mitochondrial respiration. Nevertheless, both KD and CD-IF regimens protected against different types of seizures, suggesting that mechanisms underlying CD-IF seizure protection should be explored further. Published by Elsevier B.V.

  13. Cellular telephone-based radiation detection instrument

    Science.gov (United States)

    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.

  14. Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

    Science.gov (United States)

    Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

    2014-09-01

    The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

  15. Morphine potentiates seizures induced by GABA antagonists and attenuates seizures induced by electroshock in the rat.

    Science.gov (United States)

    Foote, F; Gale, K

    1983-11-25

    In a naloxone-reversible, dose-dependent manner, morphine (10-50 mg/kg i.p.) protected against seizures induced by maximal electroshock and increased the incidence and severity of seizures induced by bicuculline, in rats. Morphine also potentiated seizures induced by isoniazid and by picrotoxin. Thus, opiate activity influences the expression of seizures in contrasting ways depending upon the mode of seizure induction. Since morphine consistently potentiated seizures induced by interference with GABA transmission, it appears that GABAergic systems may be of particular significance for the elucidation of the varied effects of morphine on seizure susceptibility.

  16. Lagrangian based methods for coherent structure detection

    Energy Technology Data Exchange (ETDEWEB)

    Allshouse, Michael R., E-mail: mallshouse@chaos.utexas.edu [Center for Nonlinear Dynamics and Department of Physics, University of Texas at Austin, Austin, Texas 78712 (United States); Peacock, Thomas, E-mail: tomp@mit.edu [Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States)

    2015-09-15

    There has been a proliferation in the development of Lagrangian analytical methods for detecting coherent structures in fluid flow transport, yielding a variety of qualitatively different approaches. We present a review of four approaches and demonstrate the utility of these methods via their application to the same sample analytic model, the canonical double-gyre flow, highlighting the pros and cons of each approach. Two of the methods, the geometric and probabilistic approaches, are well established and require velocity field data over the time interval of interest to identify particularly important material lines and surfaces, and influential regions, respectively. The other two approaches, implementing tools from cluster and braid theory, seek coherent structures based on limited trajectory data, attempting to partition the flow transport into distinct regions. All four of these approaches share the common trait that they are objective methods, meaning that their results do not depend on the frame of reference used. For each method, we also present a number of example applications ranging from blood flow and chemical reactions to ocean and atmospheric flows.

  17. Intelligent-based Structural Damage Detection Model

    International Nuclear Information System (INIS)

    Lee, Eric Wai Ming; Yu, K.F.

    2010-01-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  18. Intelligent-based Structural Damage Detection Model

    Science.gov (United States)

    Lee, Eric Wai Ming; Yu, Kin Fung

    2010-05-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  19. Hippocampal Abnormalities and Seizure Recurrence

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2006-08-01

    Full Text Available Hippocampal volumetry and T2 relaxometry were performed on 84 consecutive patients (adolescents and adults with partial epilepsy submitted to antiepileptic drug (AED withdrawal after at least 2 years of seizure control, in a study at State University of Campinas-UNICAMP, Brazil.

  20. 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.

  1. Childhood Epilepsy, Febrile Seizures, and Subsequent Risk of ADHD.

    Science.gov (United States)

    Bertelsen, Elin Næs; Larsen, Janne Tidselbak; Petersen, Liselotte; Christensen, Jakob; Dalsgaard, Søren

    2016-08-01

    Epilepsy, febrile seizures, and attention-deficit/hyperactivity disorder (ADHD) are disorders of the central nervous system and share common risk factors. Our goal was to examine the association in a nationwide cohort study with prospective follow-up and adjustment for selected confounders. We hypothesized that epilepsy and febrile seizures were associated with subsequent ADHD. A population-based cohort of all children born in Denmark from 1990 through 2007 was followed up until 2012. Incidence rate ratios (IRRs) and 95% confidence intervals (95% CIs) for ADHD were estimated by using Cox regression analysis, comparing children with epilepsy and febrile seizure with those without these disorders, adjusted for socioeconomic and perinatal risk factors, as well as family history of neurologic and psychiatric disorders. A total of 906 379 individuals were followed up for 22 years (∼10 million person-years of observation); 21 079 individuals developed ADHD. Children with epilepsy had a fully adjusted IRR of ADHD of 2.72 (95% CI, 2.53-2.91) compared with children without epilepsy. Similarly, in children with febrile seizure, the fully adjusted IRR of ADHD was 1.28 (95% CI, 1.20-1.35). In individuals with both epilepsy and febrile seizure, the fully adjusted IRR of ADHD was 3.22 (95% CI, 2.72-3.83). Our findings indicate a strong association between epilepsy in childhood and, to a lesser extent, febrile seizure and subsequent development of ADHD, even after adjusting for socioeconomic and perinatal risk factors, and family history of epilepsy, febrile seizures, or psychiatric disorders. Copyright © 2016 by the American Academy of Pediatrics.

  2. Weather as a risk factor for epileptic seizures: A case-crossover study.

    Science.gov (United States)

    Rakers, Florian; Walther, Mario; Schiffner, Rene; Rupprecht, Sven; Rasche, Marius; Kockler, Michael; Witte, Otto W; Schlattmann, Peter; Schwab, Matthias

    2017-07-01

    Most epileptic seizures occur unexpectedly and independently of known risk factors. We aimed to evaluate the clinical significance of patients' perception that weather is a risk factor for epileptic seizures. Using a hospital-based, bidirectional case-crossover study, 604 adult patients admitted to a large university hospital in Central Germany for an unprovoked epileptic seizure between 2003 and 2010 were recruited. The effect of atmospheric pressure, relative air humidity, and ambient temperature on the onset of epileptic seizures under temperate climate conditions was estimated. We found a close-to-linear negative correlation between atmospheric pressure and seizure risk. For every 10.7 hPa lower atmospheric pressure, seizure risk increased in the entire study population by 14% (odds ratio [OR] 1.14, 95% confidence interval [CI] 1.01-1.28). In patients with less severe epilepsy treated with one antiepileptic medication, seizure risk increased by 36% (1.36, 1.09-1.67). A high relative air humidity of >80% increased seizure risk in the entire study population by up to 48% (OR 1.48, 95% CI 1.11-1.96) 3 days after exposure in a J-shaped association. High ambient temperatures of >20°C decreased seizure risk by 46% in the overall study population (OR 0.54, 95% CI 0.32-0.90) and in subgroups, with the greatest effects observed in male patients (OR 0.33, 95% CI 0.14-0.74). Low atmospheric pressure and high relative air humidity are associated with an increased risk for epileptic seizures, whereas high ambient temperatures seem to decrease seizure risk. Weather-dependent seizure risk may be accentuated in patients with less severe epilepsy. Our results require further replication across different climate regions and cohorts before reliable clinical recommendations can be made. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

  3. Age-dependent susceptibility to phenobarbital-resistant neonatal seizures: role of chloride co-transporters

    Directory of Open Access Journals (Sweden)

    Seok Kyu eKang

    2015-05-01

    Full Text Available Ischemia in the immature brain is an important cause of neonatal seizures. Temporal evolution of acquired neonatal seizures and their response to anticonvulsants are of great interest, given the unreliability of the clinical correlates and poor efficacy of first-line anti-seizure drugs. The expression and function of the electroneutral chloride co-transporters KCC2 and NKCC1 influence the anti-seizure efficacy of GABAA-agonists. To investigate ischemia-induced seizure susceptibility and efficacy of the GABAA-agonist phenobarbital (PB, with NKCC1 antagonist bumetanide (BTN as an adjunct treatment, we utilized permanent unilateral carotid-ligation to produce acute ischemic-seizures in postnatal day 7, 10 and 12 CD1 mice. Immediate post-ligation video-electroencephalograms (EEGs quantitatively evaluated baseline and post-treatment seizure burdens. Brains were examined for stroke-injury and western blot analyses to evaluate the expression of KCC2 and NKCC1. Severity of acute ischemic seizures post-ligation was highest at P7. PB was an efficacious anti-seizure agent at P10 and P12, but not at P7. BTN failed as an adjunct, at all ages tested and significantly blunted PB-efficacy at P10. Significant acute post-ischemic downregulation of KCC2 was detected at all ages. At P7, males displayed higher age-dependent seizure susceptibility, associated with a significant developmental lag in their KCC2 expression. This study established a novel neonatal mouse model of PB-resistant seizures that demonstrates age/sex-dependent susceptibility. The age-dependent profile of KCC2 expression and its post-insult downregulation may underlie the PB-resistance reported in this model. Blocking NKCC1 with low-dose BTN following PB treatment failed to improve PB-efficacy.

  4. The prevalence of thyrotoxicosis-related seizures.

    Science.gov (United States)

    Song, Tae-Jin; Kim, Sun-Jung; Kim, Gyu Sik; Choi, Young-Chul; Kim, Won-Joo

    2010-09-01

    Central nervous system dysfunction, such as hyperexcitation, irritability, and disturbance of consciousness, may occur in patients with thyrotoxicosis. There are also a few case reports of seizures attributed to thyrotoxicosis. The objective of the present study was to determine the prevalence of seizures that appeared to be related to the thyrotoxic state in patients with thyrotoxicosis. We retrospectively determined the prevalence and clinical features of seizures in 3382 patients with hyperthyroidism. Among patients with seizures, we excluded those with other causes of seizures or a history of epilepsy. We did not exclude two patients in whom later work-up showed an abnormal magnetic resonance imaging, as their seizures resolved after they became euthyroid. Among the 3382 patients with hyperthyroidism, there were seven patients (0.2%) with seizures who met our criteria. Primary generalized tonic-clonic seizures occurred in four patients (57%), complex partial seizures with secondary generalized tonic-clonic seizures occurred in two patients (29%), and one patient had a focal seizure (14%). The initial electroencephalography (EEG) was normal in two patients (29%), had generalized slow activity in four patients (57%), and had diffuse generalized beta activity in one patient (14%). On magnetic resonance imaging, one patient had diffuse brain atrophy, and one had an old basal ganglia infarct. After the patients became euthyroid, the EEG was repeated and was normal in all patients. During follow-up periods ranging from 18 to 24 months, none of the patients had seizures. Hyperthyroidism is the precipitating cause of seizures in a small percentage of these patients. In these patients, the prognosis is good if they become euthyroid. The prevalence of thyrotoxicosis-related seizures reported here can be used in conjunction with the prevalence of thyrotoxicosis in the population to estimate the prevalence of thyrotoxicosis-related seizures in populations.

  5. 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.

  6. Inter-modality comparisons of seizure focus lateralization in complex partial seizures

    International Nuclear Information System (INIS)

    Meyer, P.T.; Cortes-Blanco, A.; Pourdehnad, M.; Desiderio, L.; Jang, S.; Alavi, A.; Levy-Reis, I.

    2001-01-01

    Anterior temporal lobectomy offers a high chance of seizure-free outcome in patients suffering from drug-refractory complex partial seizure (CPS) originating from the temporal lobe. Other than EEG, several functional and morphologic imaging methods are used to define the spatial seizure origin. The present study was undertaken to compare the merits of fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET), magnetic resonance imaging (MRI) and single-voxel proton MR spectroscopy (MRS) for the lateralization of temporal lobe seizure foci. The clinical charts and imaging data of 43 consecutive CPS patients were reviewed. Based on surface EEG, 31 patients were classified with temporal lobe epilepsy (TLE; 25 lateralized, 6 not lateralized) and 12 with non-temporal lobe epilepsy. All were examined by FDG-PET, MRS and MRI within 6 weeks. FDG-PET and MRI were interpreted visually, while the N-acetyl-aspartate to creatine ratio was used for MRS interpretation. One FDG-PET scan was invalid due to seizure activity post injection. The MR spectra could not be evaluated in five cases bilaterally and three cases unilaterally for technical reasons. A total of 15 patients underwent anterior temporal lobectomy. All showed a beneficial postoperative outcome. When the proportions of agreement between FDG-PET (0.77), MRI (0.58) and MRS (0.56) and surface EEG in TLE cases were compared, there were no significant differences (P>0.10). However, FDG-PET showed a significantly higher agreement (0.93) than MRI (0.60; P=0.03) with the side of successful temporal lobectomy. The concordance of MRS with the side of successful temporal lobectomy was intermediate (0.75). When the results of functional and morphologic imaging were combined, no significant differences were found between the rates of agreement of FDG-PET/MRI and MRS/MRI with EEG (0.80 vs 0.68; P=0.50) and with the side of successful temporal lobectomy (0.87 vs 0.92; P=0.50) in TLE cases. However, MRS/MRI showed

  7. Inter-modality comparisons of seizure focus lateralization in complex partial seizures

    Energy Technology Data Exchange (ETDEWEB)

    Meyer, P.T.; Cortes-Blanco, A.; Pourdehnad, M.; Desiderio, L.; Jang, S.; Alavi, A. [Pennsylvania Univ., Philadelphia, PA (United States). Dept. of Radiology; Levy-Reis, I. [Pennsylvania Univ., Philadelphia, PA (United States). Dept. of Neurology

    2001-10-01

    Anterior temporal lobectomy offers a high chance of seizure-free outcome in patients suffering from drug-refractory complex partial seizure (CPS) originating from the temporal lobe. Other than EEG, several functional and morphologic imaging methods are used to define the spatial seizure origin. The present study was undertaken to compare the merits of fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET), magnetic resonance imaging (MRI) and single-voxel proton MR spectroscopy (MRS) for the lateralization of temporal lobe seizure foci. The clinical charts and imaging data of 43 consecutive CPS patients were reviewed. Based on surface EEG, 31 patients were classified with temporal lobe epilepsy (TLE; 25 lateralized, 6 not lateralized) and 12 with non-temporal lobe epilepsy. All were examined by FDG-PET, MRS and MRI within 6 weeks. FDG-PET and MRI were interpreted visually, while the N-acetyl-aspartate to creatine ratio was used for MRS interpretation. One FDG-PET scan was invalid due to seizure activity post injection. The MR spectra could not be evaluated in five cases bilaterally and three cases unilaterally for technical reasons. A total of 15 patients underwent anterior temporal lobectomy. All showed a beneficial postoperative outcome. When the proportions of agreement between FDG-PET (0.77), MRI (0.58) and MRS (0.56) and surface EEG in TLE cases were compared, there were no significant differences (P>0.10). However, FDG-PET showed a significantly higher agreement (0.93) than MRI (0.60; P=0.03) with the side of successful temporal lobectomy. The concordance of MRS with the side of successful temporal lobectomy was intermediate (0.75). When the results of functional and morphologic imaging were combined, no significant differences were found between the rates of agreement of FDG-PET/MRI and MRS/MRI with EEG (0.80 vs 0.68; P=0.50) and with the side of successful temporal lobectomy (0.87 vs 0.92; P=0.50) in TLE cases. However, MRS/MRI showed

  8. MANAGEMENT OF A REEVE'S MUNTJAC ( MUNTIACUS REEVESI) WITH SEIZURES USING LEVETIRACETAM.

    Science.gov (United States)

    Blatt, Emily R; Seeley, Kathryn E; Lovett, Mathew C; Junge, Randall E

    2017-12-01

    This report describes the diagnosis and management of idiopathic epilepsy in a 4-yr-old intact female Reeve's muntjac ( Muntiacus reevesi). The patient was initially witnessed to have isolated paroxysmal events consistent with epileptic seizures (altered consciousness, lateral recumbency, tonic/clonic movement of limbs) lasting less than 3 min with an immediate return to normal consciousness. The seizure frequency increased to >3 seizures within 24 hr and phenobarbital 3 mg/kg orally every 12 hr was started. Because of continued epileptic seizures and low serum phenobarbital levels, the dose was increased until significant elevations of aspartate aminotransferase (AST) and alkaline phosphatase (ALP) were detected. Levetiracetam 40 mg/kg orally every 12 hr was initiated and the phenobarbital was weaned and discontinued. One breakthrough seizure has been witnessed in the 10 mo since starting levetiracetam.

  9. 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.

  10. Dangerous gas detection based on infrared video

    Science.gov (United States)

    Ding, Kang; Hong, Hanyu; Huang, Likun

    2018-03-01

    As the gas leak infrared imaging detection technology has significant advantages of high efficiency and remote imaging detection, in order to enhance the detail perception of observers and equivalently improve the detection limit, we propose a new type of gas leak infrared image detection method, which combines background difference methods and multi-frame interval difference method. Compared to the traditional frame methods, the multi-frame interval difference method we proposed can extract a more complete target image. By fusing the background difference image and the multi-frame interval difference image, we can accumulate the information of infrared target image of the gas leak in many aspect. The experiment demonstrate that the completeness of the gas leakage trace information is enhanced significantly, and the real-time detection effect can be achieved.

  11. Network Anomaly Detection Based on Wavelet Analysis

    Science.gov (United States)

    Lu, Wei; Ghorbani, Ali A.

    2008-12-01

    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.

  12. Improving Seroreactivity-Based Detection of Glioma

    Directory of Open Access Journals (Sweden)

    Nicole Ludwig

    2009-12-01

    Full Text Available Seroreactivity profiling emerges as valuable technique for minimal invasive cancer detection. Recently, we provided first evidence for the applicability of serum profiling of glioma using a limited number of immunogenic antigens. Here, we screened 57 glioma and 60 healthy sera for autoantibodies against 1827 Escherichia coli expressed clones, including 509 in-frame peptide sequences. By a linear support vector machine approach, we calculated mean specificity, sensitivity, and accuracy of 100 repetitive classifications. We were able to differentiate glioma sera from sera of the healthy controls with a specificity of 90.28%, a sensitivity of 87.31% and an accuracy of 88.84%. We were also able to differentiate World Health Organization grade IV glioma sera from healthy sera with a specificity of 98.45%, a sensitivity of 80.93%, and an accuracy of 92.88%. To rank the antigens according to their information content, we computed the area under the receiver operator characteristic curve value for each clone. Altogether, we found 46 immunogenic clones including 16 in-frame clones that were informative for the classification of glioma sera versus healthy sera. For the separation of glioblastoma versus healthy sera, we found 91 informative clones including 26 in-frame clones. The best-suited in-frame clone for the classification glioma sera versus healthy sera corresponded to the vimentin gene (VIM that was previously associated with glioma. In the future, autoantibody signatures in glioma not only may prove useful for diagnosis but also offer the prospect for a personalized immune-based therapy.

  13. Absence of early epileptiform abnormalities predicts lack of seizures on continuous EEG.

    Science.gov (United States)

    Shafi, Mouhsin M; Westover, M Brandon; Cole, Andrew J; Kilbride, Ronan D; Hoch, Daniel B; Cash, Sydney S

    2012-10-23

    To determine whether the absence of early epileptiform abnormalities predicts absence of later seizures on continuous EEG monitoring of hospitalized patients. We retrospectively reviewed 242 consecutive patients without a prior generalized convulsive seizure or active epilepsy who underwent continuous EEG monitoring lasting at least 18 hours for detection of nonconvulsive seizures or evaluation of unexplained altered mental status. The findings on the initial 30-minute screening EEG, subsequent continuous EEG recordings, and baseline clinical data were analyzed. We identified early EEG findings associated with absence of seizures on subsequent continuous EEG. Seizures were detected in 70 (29%) patients. A total of 52 patients had their first seizure in the initial 30 minutes of continuous EEG monitoring. Of the remaining 190 patients, 63 had epileptiform discharges on their initial EEG, 24 had triphasic waves, while 103 had no epileptiform abnormalities. Seizures were later detected in 22% (n = 14) of studies with epileptiform discharges on their initial EEG, vs 3% (n = 3) of the studies without epileptiform abnormalities on initial EEG (p monitoring is necessary.

  14. Factors Predictive of Seizure Outcome in New-Onset Temporal Lobe Epilepsy

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2007-01-01

    Full Text Available A community-based cohort of 77 children with new-onset temporal lobe epilepsy (TLE were followed prospectively and reviewed at 7 and 14 years after seizure onset, and clinical, EEG, and neuroimaging findings and seizure outcome are reported from the Royal Children's Hospital and University of Melbourne, Australia, and Starship Children's Hospital, Auckland, New Zealand.

  15. How many neurologists/epileptologists are needed to provide reliable descriptions of seizure types?

    NARCIS (Netherlands)

    van Ast, J. F.; Talmon, J. L.; Renier, W. O.; Hasman, A.

    2003-01-01

    We are developing seizure descriptions as a basis for decision support. Based on an existing dataset we used the Spearman-Brown prophecy formula to estimate how many neurologist/epileptologists are needed to obtain reliable seizure descriptions (rho = 0.9). By extending the number of participants to

  16. 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…

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

    Science.gov (United States)

    Frye, Richard E; Sreenivasula, Swapna; Adams, James B

    2011-05-18

    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. 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.

  18. Oxidative Stress in Patients with Drug Resistant Partial Complex Seizure

    Directory of Open Access Journals (Sweden)

    Lourdes Lorigados Pedre

    2018-06-01

    Full Text Available Oxidative stress (OS has been implicated as a pathophysiological mechanism of drug-resistant epilepsy, but little is known about the relationship between OS markers and clinical parameters, such as the number of drugs, age onset of seizure and frequency of seizures per month. The current study’s aim was to evaluate several oxidative stress markers and antioxidants in 18 drug-resistant partial complex seizure (DRPCS patients compared to a control group (age and sex matched, and the results were related to clinical variables. We examined malondialdehyde (MDA, advanced oxidation protein products (AOPP, advanced glycation end products (AGEs, nitric oxide (NO, uric acid, superoxide dismutase (SOD, glutathione, vitamin C, 4-hydroxy-2-nonenal (4-HNE and nitrotyrosine (3-NT. All markers except 4-HNE and 3-NT were studied by spectrophotometry. The expressions of 4-HNE and 3-NT were evaluated by Western blot analysis. MDA levels in patients were significantly increased (p ≤ 0.0001 while AOPP levels were similar to the control group. AGEs, NO and uric acid concentrations were significantly decreased (p ≤ 0.004, p ≤ 0.005, p ≤ 0.0001, respectively. Expressions of 3-NT and 4-HNE were increased (p ≤ 0.005 similarly to SOD activity (p = 0.0001, whereas vitamin C was considerably diminished (p = 0.0001. Glutathione levels were similar to the control group. There was a positive correlation between NO and MDA with the number of drugs. The expression of 3-NT was positively related with the frequency of seizures per month. There was a negative relationship between MDA and age at onset of seizures, as well as vitamin C with seizure frequency/month. We detected an imbalance in the redox state in patients with DRCPS, supporting oxidative stress as a relevant mechanism in this pathology. Thus, it is apparent that some oxidant and antioxidant parameters are closely linked with clinical variables.

  19. 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

  20. 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.

  1. Trojan detection model based on network behavior analysis

    International Nuclear Information System (INIS)

    Liu Junrong; Liu Baoxu; Wang Wenjin

    2012-01-01

    Based on the analysis of existing Trojan detection technology, this paper presents a Trojan detection model based on network behavior analysis. First of all, we abstract description of the Trojan network behavior, then according to certain rules to establish the characteristic behavior library, and then use the support vector machine algorithm to determine whether a Trojan invasion. Finally, through the intrusion detection experiments, shows that this model can effectively detect Trojans. (authors)

  2. Change in illness perception is associated with short-term seizure burden outcome following video-EEG confirmation of psychogenic nonepileptic seizures.

    Science.gov (United States)

    Chen, David K; Majmudar, Shirine; Ram, Aarthi; Rutherford, Holly C; Fadipe, Melissa; Dunn, Callie B; Collins, Robert L

    2018-04-27

    We aimed to evaluate whether potential changes in the patient's illness perception can significantly influence short-term seizure burden following video-electroencephalography (EEG) confirmation/explanation of psychogenic nonepileptic seizures (PNES). Patients with PNES were dichotomized to two groups based on a five-point Symptom Attribution Scale: (a) those who prior to diagnosis perceived their seizures to be solely ("5") or mainly ("4") physical in origin (physical group) and (b) the remainder of patients with PNES (psychological group). The physical group (n=32), psychological group (n=40), and group with epilepsy (n=26) also completed the Brief Illness Perception Questionnaire (BIPQ) prior to diagnosis, and were followed up at 3months as well as at 6months postdiagnosis. At 3months postdiagnosis, the physical group experienced significantly greater improvement in seizure intensity (p=0.002) and seizure frequency (p=0.016) when compared with the psychological group. The physical group was significantly more likely to have modified their symptom attribution toward a greater psychological role to their seizures (p=0.002), and their endorsement on the BIPQ item addressing "consequences" (How much do your seizures affect your life?) was significantly less severe (p'=0.014) when compared with that of the psychological group and the group with epilepsy. At 6months postdiagnosis, the physical group continued to experience significantly greater improvement in seizure intensity (p=0.007) while their seizure frequency no longer reached significant difference (p=0.078) when compared with the psychological group. The physical group continued to be significantly more likely to have modified their symptom attribution toward a greater psychological role to their seizures (p=0.005), and their endorsement on the BIPQ item addressing "consequences" remained significantly less severe (p'=0.037) when compared with the psychological group and the group with epilepsy. Among

  3. QRS Detection Based on Improved Adaptive Threshold

    Directory of Open Access Journals (Sweden)

    Xuanyu Lu

    2018-01-01

    Full Text Available Cardiovascular disease is the first cause of death around the world. In accomplishing quick and accurate diagnosis, automatic electrocardiogram (ECG analysis algorithm plays an important role, whose first step is QRS detection. The threshold algorithm of QRS complex detection is known for its high-speed computation and minimized memory storage. In this mobile era, threshold algorithm can be easily transported into portable, wearable, and wireless ECG systems. However, the detection rate of the threshold algorithm still calls for improvement. An improved adaptive threshold algorithm for QRS detection is reported in this paper. The main steps of this algorithm are preprocessing, peak finding, and adaptive threshold QRS detecting. The detection rate is 99.41%, the sensitivity (Se is 99.72%, and the specificity (Sp is 99.69% on the MIT-BIH Arrhythmia database. A comparison is also made with two other algorithms, to prove our superiority. The suspicious abnormal area is shown at the end of the algorithm and RR-Lorenz plot drawn for doctors and cardiologists to use as aid for diagnosis.

  4. Characteristic phasic evolution of convulsive seizure in PCDH19-related epilepsy.

    Science.gov (United States)

    Ikeda, Hiroko; Imai, Katsumi; Ikeda, Hitoshi; Shigematsu, Hideo; Takahashi, Yukitoshi; Inoue, Yushi; Higurashi, Norimichi; Hirose, Shinichi

    2016-03-01

    PCDH19-related epilepsy is a genetic disorder that was first described in 1971, then referred to as "epilepsy and mental retardation limited to females". PCDH19 has recently been identified as the responsible gene, but a detailed characterization of the seizure manifestation based on video-EEG recording is still limited. The purpose of this study was to elucidate features of the seizure semiology in children with PCDH19-related epilepsy. To do this, ictal video-EEG recordings of 26 convulsive seizures in three girls with PCDH19-related epilepsy were analysed. All seizures occurred in clusters, mainly during sleep accompanied by fever. The motor manifestations consisted of six sequential phases: "jerk", "reactive", "mild tonic", "fluttering", "mild clonic", and "postictal". Some phases were brief or lacking in some seizures, whereas others were long or pronounced. In the reactive phase, the patients looked fearful or startled with sudden jerks and turned over reactively. The tonic and clonic components were less intense compared with those of typical tonic-clonic seizures in other types of epilepsy. The fluttering phase was characterised initially by asymmetric, less rhythmic, and less synchronous tremulous movement and was then followed by the subtle clonic phase. Subtle oral automatism was observed in the postictal phase. The reactive, mild tonic, fluttering and mild clonic phases were most characteristic of seizures of PCDH19-related epilepsy. Ictal EEG started bilaterally and was symmetric in some patients but asymmetric in others. It showed asymmetric rhythmic discharges in some seizures at later phases. The electroclinical pattern of the phasic evolution of convulsive seizure suggests a focal onset seizure with secondary generalisation. Based on our findings, we propose that the six unique sequential phases in convulsive seizures suggest the diagnosis of PCDH19-related epilepsy when occurring in clusters with or without high fever in girls. [Published with

  5. Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology.

    Science.gov (United States)

    Fürbass, F; Hartmann, M M; Halford, J J; Koren, J; Herta, J; Gruber, A; Baumgartner, C; Kluge, T

    2015-09-01

    Continuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports. First of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report. In three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures. The use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  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. Serum cardiac troponin I in canine syncope and seizures.

    Science.gov (United States)

    Dutton, E; Dukes-McEwan, J; Cripps, P J

    2017-02-01

    To determine if serum cardiac troponin I (cTnI) concentration distinguishes between cardiogenic syncope and collapsing dogs presenting with either generalized epileptic seizures (both with and without cardiac disease) or vasovagal syncope. Seventy-nine prospectively recruited dogs, grouped according to aetiology of collapse: generalized epileptic seizures (group E), cardiogenic syncope (group C), dogs with both epileptic seizures and cardiac disease (group B), vasovagal syncope (group V) or unclassified (group U). Most patients had ECG (n = 78), echocardiography (n = 78) and BP measurement (n = 74) performed. Dogs with a history of intoxications, trauma, evidence of metabolic disorders or renal insufficiency (based on serum creatinine concentrations >150 μmol/L and urine specific gravity disease) or vasovagal syncope. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. 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

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Occipital lobe seizures and epilepsies.

    Science.gov (United States)

    Adcock, Jane E; Panayiotopoulos, Chrysostomos P

    2012-10-01

    Occipital lobe epilepsies (OLEs) manifest with occipital seizures from an epileptic focus within the occipital lobes. Ictal clinical symptoms are mainly visual and oculomotor. Elementary visual hallucinations are common and characteristic. Postictal headache occurs in more than half of patients (epilepsy-migraine sequence). Electroencephalography (EEG) is of significant diagnostic value, but certain limitations should be recognized. Occipital spikes and/or occipital paroxysms either spontaneous or photically induced are the main interictal EEG abnormalities in idiopathic OLE. However, occipital epileptiform abnormalities may also occur without clinical relationship to seizures particularly in children. In cryptogenic/symptomatic OLE, unilateral posterior EEG slowing is more common than occipital spikes. In neurosurgical series of symptomatic OLE, interictal EEG abnormalities are rarely strictly occipital. The most common localization is in the posterior temporal regions and less than one-fifth show occipital spikes. In photosensitive OLE, intermittent photic stimulation elicits (1) spikes/polyspikes confined in the occipital regions or (2) generalized spikes/polyspikes with posterior emphasis. In ictal EEG, a well-localized unifocal rhythmic ictal discharge during occipital seizures is infrequent. A bioccipital field spread to the temporal regions is common. Frequency, severity, and response to treatment vary considerably from good to intractable and progressive mainly depending on underlying causes.

  17. 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.

  18. Flow-Based Detection of DNS Tunnels

    NARCIS (Netherlands)

    Ellens, W.; Żuraniewski, P.; Sperotto, A.; Schotanus, H.; Mandjes, M.; Meeuwissen, E.

    2013-01-01

    DNS tunnels allow circumventing access and security policies in firewalled networks. Such a security breach can be misused for activities like free web browsing, but also for command & control traffic or cyber espionage, thus motivating the search for effective automated DNS tunnel detection

  19. Flow-based detection of DNS tunnels

    NARCIS (Netherlands)

    Ellens, W.; Zuraniewski, P.; Schotanus, H.; Mandjes, M.R.H.; Meeuwissen, E.; Doyen, Guillaume; Waldburger, Martin; Celeda, Pavel; Sperotto, Anna; Stiller, Burkhard

    DNS tunnels allow circumventing access and security policies in firewalled networks. Such a security breach can be misused for activities like free web browsing, but also for command & control traffic or cyber espionage, thus motivating the search for effective automated DNS tunnel detection

  20. Flow-based detection of DNS tunnels

    NARCIS (Netherlands)

    Ellens, W.; Zuraniewski, P.W.; Sperotto, A.; Schotanus, H.A.; Mandjes, M.; Meeuwissen, H.B.

    2013-01-01

    DNS tunnels allow circumventing access and security policies in firewalled networks. Such a security breach can be misused for activities like free web browsing, but also for command & control traffic or cyber espionage, thus motivating the search for effective automated DNS tunnel detection

  1. Clinical Profile and Electroencephalogram Findings in Children with Seizure Presenting to Dhulikhel Hospital.

    Science.gov (United States)

    Poudyal, P; Shrestha, R Pb; Shrestha, P S; Dangol, S; Shrestha, N C; Joshi, A; Shrestha, A

    Background Seizure disorder is the most common childhood neurologic condition and a major public health concern. Identification of the underlying seizure etiology helps to identify appropriate treatment options and the prognosis for the child. Objective This study was conducted to investigate the clinical profile, causes and electroencephalogram findings in children with seizure presenting to a tertiary center in Kavre district. Method This was a hospital based prospective study carried out in the Department of Pediatrics, Dhulikhel Hospital, Kavre from 1st April 2015 to 31st March 2016. Variables collected were demographics, clinical presentations, laboratory tests, brain imaging studies, electroencephalography, diagnosis and outcome. Result Study included 120 (age 1 month to 16 years) children attending Dhulikhel Hospital. Majority of the patients were male (60.84%). Age at first seizure was less than 5 years in 75.83% of children. Seizure was generalized in 62.50%, focal in 31.67% and unclassified in 5.83%. Common causes of seizure were - Primary generalized epilepsy (26.66%), neurocysticercosis (10%) and hypoxic injury (6.6%) which was diagnosed in the perinatal period. Febrile seizure (26.66%) was the most common cause of seizure in children between 6 months to 5 years of age. Neurological examination, electroencephalography and Computed Tomography were abnormal in 71.66%, 68.92% and 58.14% cases respectively. Seizure was controlled by monotherapy in 69.16% cases and was resistant in 7.50% of the cases. Conclusion Primary generalized epilepsy and febrile seizure were the most common causes of seizures in children attending Dhulikhel Hospital. Electroencephalogram findings help to know the pattern of neuronal activity. Response to monotherapy was good and valproic acid was the most commonly used drug.

  2. Neonatal Seizure Models to Study Epileptogenesis

    Directory of Open Access Journals (Sweden)

    Yuka Kasahara

    2018-04-01

    Full Text Available Current therapeutic strategies for epilepsy include anti-epileptic drugs and surgical treatments that are mainly focused on the suppression of existing seizures rather than the occurrence of the first spontaneous seizure. These symptomatic treatments help a certain proportion of patients, but these strategies are not intended to clarify the cellular and molecular mechanisms underlying the primary process of epilepsy development, i.e., epileptogenesis. Epileptogenic changes include reorganization of neural and glial circuits, resulting in the formation of an epileptogenic focus. To achieve the goal of developing “anti-epileptogenic” drugs, we need to clarify the step-by-step mechanisms underlying epileptogenesis for patients whose seizures are not controllable with existing “anti-epileptic” drugs. Epileptogenesis has been studied using animal models of neonatal seizures because such models are useful for studying the latent period before the occurrence of spontaneous seizures and the lowering of the seizure threshold. Further, neonatal seizure models are generally easy to handle and can be applied for in vitro studies because cells in the neonatal brain are suitable for culture. Here, we review two animal models of neonatal seizures for studying epileptogenesis and discuss their features, specifically focusing on hypoxia-ischemia (HI-induced seizures and febrile seizures (FSs. Studying these models will contribute to identifying the potential therapeutic targets and biomarkers of epileptogenesis.

  3. 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.

  4. 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.

  5. Underwater electric field detection system based on weakly electric fish

    Science.gov (United States)

    Xue, Wei; Wang, Tianyu; Wang, Qi

    2018-04-01

    Weakly electric fish sense their surroundings in complete darkness by their active electric field detection system. However, due to the insufficient detection capacity of the electric field, the detection distance is not enough, and the detection accuracy is not high. In this paper, a method of underwater detection based on rotating current field theory is proposed to improve the performance of underwater electric field detection system. First of all, we built underwater detection system based on the theory of the spin current field mathematical model with the help of the results of previous researchers. Then we completed the principle prototype and finished the metal objects in the water environment detection experiments, laid the foundation for the further experiments.

  6. 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 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.

  7. 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%.

  8. 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...

  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. 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.

  12. 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

  13. 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.

  14. Electroencephalographic characterization of seizure activity in the synapsin I/II double knockout mouse

    DEFF Research Database (Denmark)

    Etholm, Lars; Lindén, Henrik; Eken, Torsten

    2011-01-01

    We present a detailed comparison of the behavioral and electrophysiological development of seizure activity in mice genetically depleted of synapsin I and synapsin II (SynDKO mice), based on combined video and surface EEG recordings. SynDKO mice develop handling-induced epileptic seizures...... at the age of 2months. The seizures show a very regular behavioral pattern, where activity is initially dominated by truncal muscle contractions followed by various myoclonic elements. Whereas seizure behavior goes through clearly defined transitions, cortical activity as reflected by EEG recordings shows...... a more gradual development with respect to the emergence of different EEG components and the frequency of these components. No EEG pattern was seen to define a particular seizure behavior. However, myoclonic activity was characterized by more regular patterns of combined sharp waves and spikes. Where...

  15. Temporal lobe origin is common in patients who have undergone epilepsy surgery for hypermotor seizures.

    Science.gov (United States)

    Arain, Amir M; Azar, Nabil J; Lagrange, Andre H; McLean, Michael; Singh, Pradumna; Sonmezturk, Hasan; Konrad, Peter; Neimat, Joseph; Abou-Khalil, Bassel

    2016-11-01

    Hypermotor seizures are most often reported from the frontal lobe but may also have temporal, parietal, or insular origin. We noted a higher proportion of patients with temporal lobe epilepsy in our surgical cohort who had hypermotor seizures. We evaluated the anatomic localization and surgical outcome in patient with refractory hypermotor seizures who had epilepsy surgery in our center. We identified twenty three patients with refractory hypermotor seizures from our epilepsy surgery database. We analyzed demographics, presurgical evaluation including semiology, MRI, PET scan, interictal/ictal scalp video-EEG, intracranial recording, and surgical outcomes. We evaluated preoperative variables as predictors of outcome. Most patients (65%) had normal brain MRI. Intracranial EEG was required in 20 patients (86.9%). Based on the presurgical evaluation, the resection was anterior temporal in fourteen patients, orbitofrontal in four patients, cingulate in four patients, and temporoparietal in one patient. The median duration of follow-up after surgery was 76.4months. Fourteen patients (60%) had been seizure free at the last follow up while 3 patients had rare disabling seizures. Hypermotor seizures often originated from the temporal lobe in this series of patients who had epilepsy surgery. This large proportion of temporal lobe epilepsy may be the result of a selection bias, due to easier localization and expected better outcome in temporal lobe epilepsy. With extensive presurgical evaluation, including intracranial EEG when needed, seizure freedom can be expected in the majority of patients. Copyright © 2016. Published by Elsevier Inc.

  16. 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

  17. Epidemiology of early stages of epilepsy: Risk of seizure recurrence after a first seizure.

    Science.gov (United States)

    Rizvi, Syed; Ladino, Lady Diana; Hernandez-Ronquillo, Lizbeth; Téllez-Zenteno, José F

    2017-07-01

    A single unprovoked seizure is a frequent phenomenon in the general population and the rate of seizure recurrence can vary widely. Individual risk prognostication is crucial in predicting patient outcomes and guiding treatment decisions. In this article, we review the most important risk factors associated with an increased likelihood of seizure recurrence after a single unprovoked seizure. In summary, the presence of focal seizure, nocturnal seizure, history of prior brain injury, family history of epilepsy, abnormal neurological exam, epileptiform discharges on electroencephalography and neuroimaging abnormalities, portend increased risk of seizure recurrence. Elucidation of these risk factors in patient assessment will augment clinical decision-making and may help determine the appropriateness of instituting anti-epilepsy treatment. We also discuss the Canadian model of single seizure clinics and the potential use to assess these patients. Copyright © 2017. Published by Elsevier Ltd.

  18. 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 ...

  19. Approaches in anomaly-based network intrusion detection systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, S.; Di Pietro, R.; Mancini, L.V.

    2008-01-01

    Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at

  20. Approaches in Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, Sandro

    Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at

  1. Seizure Recognition and Observation: A Guide for Allied Health Professionals.

    Science.gov (United States)

    Epilepsy Foundation of America, Landover, MD.

    Intended for allied health professionals, this guide provides information on seizure recognition and classification to help them assist the patient, the family, and the treating physician in obtaining control of epileptic seizures. A section on seizure recognition describes epilepsy and seizures, covering seizure classification and the causes of…

  2. 19 CFR 162.22 - Seizure of conveyances.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 2 2010-04-01 2010-04-01 false Seizure of conveyances. 162.22 Section 162.22... TREASURY (CONTINUED) INSPECTION, SEARCH, AND SEIZURE Seizures § 162.22 Seizure of conveyances. (a) General applicability. If it shall appear to any officer authorized to board conveyances and make seizures that there...

  3. SEIZURE PREDICTION: THE FOURTH INTERNATIONAL WORKSHOP

    Science.gov (United States)

    Zaveri, Hitten P.; Frei, Mark G.; Arthurs, Susan; Osorio, Ivan

    2010-01-01

    The recently convened Fourth International Workshop on Seizure Prediction (IWSP4) brought together a diverse international group of investigators, from academia and industry, including epileptologists, neurosurgeons, neuroscientists, computer scientists, engineers, physicists, and mathematicians who are conducting interdisciplinary research on the prediction and control of seizures. IWSP4 allowed the presentation and discussion of results, an exchange of ideas, an assessment of the status of seizure prediction, control and related fields and the fostering of collaborative projects. PMID:20674508

  4. DNA & Protein detection based on microbead agglutination

    KAUST Repository

    Kodzius, Rimantas; Castro, David; Foulds, Ian G.; Parameswaran, Ash M.; Sumanpreet, K. Chhina

    2012-01-01

    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

  5. Analysis of absence seizure generation using EEG spatial-temporal regularity measures.

    Science.gov (United States)

    Mammone, Nadia; Labate, Domenico; Lay-Ekuakille, Aime; Morabito, Francesco C

    2012-12-01

    Epileptic seizures are thought to be generated and to evolve through an underlying anomaly of synchronization in the activity of groups of neuronal populations. The related dynamic scenario of state transitions is revealed by detecting changes in the dynamical properties of Electroencephalography (EEG) signals. The recruitment procedure ending with the crisis can be explored through a spatial-temporal plot from which to extract suitable descriptors that are able to monitor and quantify the evolving synchronization level from the EEG tracings. In this paper, a spatial-temporal analysis of EEG recordings based on the concept of permutation entropy (PE) is proposed. The performance of PE are tested on a database of 24 patients affected by absence (generalized) seizures. The results achieved are compared to the dynamical behavior of the EEG of 40 healthy subjects. Being PE a feature which is dependent on two parameters, an extensive study of the sensitivity of the performance of PE with respect to the parameters' setting was carried out on scalp EEG. Once the optimal PE configuration was determined, its ability to detect the different brain states was evaluated. According to the results here presented, it seems that the widely accepted model of "jump" transition to absence seizure should be in some cases coupled (or substituted) by a gradual transition model characteristic of self-organizing networks. Indeed, it appears that the transition to the epileptic status is heralded before the preictal state, ever since the interictal stages. As a matter of fact, within the limits of the analyzed database, the frontal-temporal scalp areas appear constantly associated to PE levels higher compared to the remaining electrodes, whereas the parieto-occipital areas appear associated to lower PE values. The EEG of healthy subjects neither shows any similar dynamic behavior nor exhibits any recurrent portrait in PE topography.

  6. Wireless Falling Detection System Based on Community.

    Science.gov (United States)

    Xia, Yun; Wu, Yanqi; Zhang, Bobo; Li, Zhiyang; He, Nongyue; Li, Song

    2015-06-01

    The elderly are more likely to suffer the aches or pains from the accidental falls, and both the physiology and psychology of patients would subject to a long-term disturbance, especially when the emergency treatment was not given timely and properly. Although many methods and devices have been developed creatively and shown their efficiency in experiments, few of them are suitable for commercial applications routinely. Here, we design a wearable falling detector as a mobile terminal, and utilize the wireless technology to transfer and monitor the activity data of the host in a relatively small community. With the help of the accelerometer sensor and the Google Mapping service, information of the location and the activity data will be send to the remote server for the downstream processing. The experimental result has shown that SA (Sum-vector of all axes) value of 2.5 g is the threshold value to distinguish the falling from other activities. A three-stage detection algorithm was adopted to increase the accuracy of the real alarm, and the accuracy rate of our system was more than 95%. With the further improvement, the falling detecting device which is low-cost, accurate and user-friendly would become more and more common in everyday life.

  7. Treating seizures in Creutzfeldt-Jakob disease.

    Science.gov (United States)

    Ng, Marcus C; Westover, M Brandon; Cole, Andrew J

    2014-01-01

    Seizures are known to occur in Creutzfeldt-Jakob disease (CJD). In the setting of a rapidly progressive condition with no effective therapy, determining appropriate treatment for seizures can be difficult if clinical morbidity is not obvious yet the electroencephalogram (EEG) demonstrates a worrisome pattern such as status epilepticus. Herein, we present the case of a 39-year-old man with CJD and electrographic seizures, discuss how this case challenges conventional definitions of seizures, and discuss a rational approach toward treatment. Coincidentally, our case is the first report of CJD in a patient with Stickler syndrome.

  8. Genetics Home Reference: benign familial neonatal seizures

    Science.gov (United States)

    ... Additional NIH Resources (1 link) National Institute of Neurological Disorders and Stroke: Epilepsy Educational Resources (7 links) Boston Children's Hospital: My Child Has...Seizures and Epilepsy Centers ...

  9. 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.

  10. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2015-01-01

    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.

  11. 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.

  12. 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.

  13. 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.

  14. Gamma knife treatment for refractory epilepsy in seizure focus localized by positron emission tomography/CT★

    Science.gov (United States)

    Bai, Xia; Wang, Xuemei; Wang, Hongwei; Zhao, Shigang; Han, Xiaodong; Hao, Linjun; Wang, Xiangcheng

    2012-01-01

    A total of 80 patients with refractory epilepsy were recruited from the Inner Mongolia Medical College Affiliated Hospital. The foci of 60% of the patients could be positioned using a combined positron emission tomography/CT imaging modality. Hyper- and hypometabolism foci were examined as part of this study. Patients who had abnormal metabolism in positron emission tomography/CT imaging were divided into intermittent-phase group and the seizure-phase group. The intermittent-phase group was further divided into a single-focus group and a multiple-foci group according to the number of seizure foci detected by imaging. Following gamma knife treatment, seizure frequency was significantly lower in the intermittent-phase group and the seizure-phase group. Wieser’s classification reached Grade I or II in nearly 40% of patients. Seizure frequency was significantly lower following treatment, but Wieser’s classification score was significantly higher in the seizure-phase group compared with the intermittent-phase group. Seizure frequency was significantly lower following treatment in the single-focus group, but Wieser’s classification score was significantly higher in the single-focus group as compared with the multiple-foci group. PMID:25317147

  15. Control of epileptic seizures in WAG/Rij rats by means of brain-computer interface

    Science.gov (United States)

    Makarov, Vladimir V.; Maksimenko, Vladimir A.; van Luijtelaar, Gilles; Lüttjohann, Annika; Hramov, Alexander E.

    2018-02-01

    The main issue of epileptology is the elimination of epileptic events. This can be achieved by a system that predicts the emergence of seizures in conjunction with a system that interferes with the process that leads to the onset of seizure. The prediction of seizures remains, for the present, unresolved in the absence epilepsy, due to the sudden onset of seizures. We developed an algorithm for predicting seizures in real time, evaluated it and implemented it into an online closed-loop brain stimulation system designed to prevent typical for the absence of epilepsy of spike waves (SWD) in the genetic rat model. The algorithm correctly predicts more than 85% of the seizures and the rest were successfully detected. Unlike the old beliefs that SWDs are unpredictable, current results show that they can be predicted and that the development of systems for predicting and preventing closed-loop capture is a feasible step on the way to intervention to achieve control and freedom from epileptic seizures.

  16. Bipolar electrocoagulation on cortex after AVMs lesionectomy for seizure control.

    Science.gov (United States)

    Cao, Yong; Wang, Rong; Yang, Lijun; Bai, Qin; Wang, Shuo; Zhao, Jizong

    2011-01-01

    The findings of previous studies remain controversial on the optimal management required for effective seizure control after surgical excision of arteriovenous malformations (AVMs). We evaluated the efficacy of additional bipolar electrocoagulation on the electrically positive cortex guided by intraoperative electrocorticography (ECoG) for controlling cerebral AVMs-related epilepsy. Sixty consecutive patients with seizure due to cerebral AVMs, who underwent surgical excision of cerebral AVMs and intraoperative ECoG, were assessed. The AVMs and surrounding hemosiderin stained tissue were completely removed, and bipolar electrocoagulation was applied on the surrounding cerebral cortex where epileptic discharges were monitored via intraoperative ECoG. Patients were followed up at three to six months after the surgery and then annually. We evaluated seizure outcome by using Engel's classification and postoperative complications. Forty-nine patients (81.6%) were detected of epileptic discharges before and after AVMs excision. These patients underwent the removal of AVMs plus bipolar electrocoagulation on spike-positive site cortex. After electrocoagulation, 45 patients' epileptic discharges disappeared, while four obviously diminished. Fifty-five of 60 patients (91.7%) had follow-up lasting at least 22 months (mean 51.1 months; range 22-93 months). Determined by the Engel Seizure Outcome Scale, 39 patients (70.9%) were Class I, seven (12.7%) Class II, five (9.0%) Class III, and four (7.2%) Class IV. Even after the complete removal of AVM and surrounding gliotic and hemosiderin stained tissue, a high-frequency residual spike remained on the surrounding cerebral cortex. Effective surgical seizure control can be achieved by carrying out additional bipolar electrocoagulation on the cortex guided by the intraoperative ECoG.

  17. Laser Spot Detection Based on Reaction Diffusion.

    Science.gov (United States)

    Vázquez-Otero, Alejandro; Khikhlukha, Danila; Solano-Altamirano, J M; Dormido, Raquel; Duro, Natividad

    2016-03-01

    Center-location of a laser spot is a problem of interest when the laser is used for processing and performing measurements. Measurement quality depends on correctly determining the location of the laser spot. Hence, improving and proposing algorithms for the correct location of the spots are fundamental issues in laser-based measurements. In this paper we introduce a Reaction Diffusion (RD) system as the main computational framework for robustly finding laser spot centers. The method presented is compared with a conventional approach for locating laser spots, and the experimental results indicate that RD-based computation generates reliable and precise solutions. These results confirm the flexibility of the new computational paradigm based on RD systems for addressing problems that can be reduced to a set of geometric operations.

  18. Laser Spot Detection Based on Reaction Diffusion

    Directory of Open Access Journals (Sweden)

    Alejandro Vázquez-Otero

    2016-03-01

    Full Text Available Center-location of a laser spot is a problem of interest when the laser is used for processing and performing measurements. Measurement quality depends on correctly determining the location of the laser spot. Hence, improving and proposing algorithms for the correct location of the spots are fundamental issues in laser-based measurements. In this paper we introduce a Reaction Diffusion (RD system as the main computational framework for robustly finding laser spot centers. The method presented is compared with a conventional approach for locating laser spots, and the experimental results indicate that RD-based computation generates reliable and precise solutions. These results confirm the flexibility of the new computational paradigm based on RD systems for addressing problems that can be reduced to a set of geometric operations.

  19. 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%; p<0.001) and significantly more often lateralizing seizure phenomena pointing to bilateral seizure onset compared to patients with unilateral TLE (67% vs. 11%; p<0.001). The sensitivity of identical vs. different seizure semiology 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.

  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. 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.

  2. 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.

  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. 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.

  5. 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.

  6. Anti-Seizure Medications: Relief from Nerve Pain

    Science.gov (United States)

    Anti-seizure medications: Relief from nerve pain Anti-seizure drugs often are used to help control the type of ... by damaged nerves. By Mayo Clinic Staff Anti-seizure medications were originally designed to treat people with ...

  7. Respiratory alkalosis in children with febrile seizures.

    Science.gov (United States)

    Schuchmann, Sebastian; Hauck, Sarah; Henning, Stephan; Grüters-Kieslich, Annette; Vanhatalo, Sampsa; Schmitz, Dietmar; Kaila, Kai

    2011-11-01

    Febrile seizures (FS) are the most common type of convulsive events in children. FS are suggested to result from a combination of genetic and environmental factors. However, the pathophysiologic mechanisms underlying FS remain unclear. Using an animal model of experimental FS, it was demonstrated that hyperthermia causes respiratory alkalosis with consequent brain alkalosis and seizures. Here we examine the acid-base status of children who were admitted to the hospital for FS. Children who were admitted because of gastroenteritis (GE), a condition known to promote acidosis, were examined to investigate a possible protective effect of acidosis against FS. We enrolled 433 age-matched children with similar levels of fever from two groups presented to the emergency department. One group was admitted for FS (n = 213) and the other for GE (n = 220). In the FS group, the etiology of fever was respiratory tract infection (74.2%), otitis media (7%), GE (7%), tonsillitis (4.2%), scarlet fever (2.3%) chickenpox (1.4%), urinary tract infection (1.4%), postvaccination reaction (0.9%), or unidentified (1.4%). In all patients, capillary pH and blood Pco(2) were measured immediately on admission to the hospital. Respiratory alkalosis was found in children with FS (pH 7.46 ± 0.04, [mean ± standard deviation] Pco(2) 29.5 ± 5.5 mmHg), whereas a metabolic acidosis was seen in all children admitted for GE (pH 7.31 ± 0.03, Pco(2) 37.7 ± 4.3 mmHg; p respiratory alkalosis, irrespective of the severity of the underlying infection as indicated by the level of fever. The lack of FS in GE patients is attributable to low pH, which also explains the fact that children with a susceptibility to FS do not have seizures when they have GE-induced fever that is associated with acidosis. The present demonstration of a close link between FS and respiratory alkalosis may pave the way for further clinical studies and attempts to design novel therapies for the treatment of FS by controlling the

  8. 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

  9. Laser Spot Detection Based on Reaction Diffusion

    OpenAIRE

    Alejandro Vázquez-Otero; Danila Khikhlukha; J. M. Solano-Altamirano; Raquel Dormido; Natividad Duro

    2016-01-01

    Center-location of a laser spot is a problem of interest when the laser is used for processing and performing measurements. Measurement quality depends on correctly determining the location of the laser spot. Hence, improving and proposing algorithms for the correct location of the spots are fundamental issues in laser-based measurements. In this paper we introduce a Reaction Diffusion (RD) system as the main computational framework for robustly finding laser spot centers. The method presente...

  10. Similarity-based Polymorphic Shellcode Detection

    Directory of Open Access Journals (Sweden)

    Denis Yurievich Gamayunov

    2013-02-01

    Full Text Available In the work the method for polymorphic shellcode dedection based on the set of known shellcodes is proposed. The method’s main idea is in sequential applying of deobfuscating transformations to a data analyzed and then recognizing similarity with malware samples. The method has been tested on the sets of shellcodes generated using Metasploit Framework v.4.1.0 and PELock Obfuscator and shows 87 % precision with zero false positives rate.

  11. 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.

  12. 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.

  13. 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

  14. [Dissociative seizures: a manual for neurologists for communicating the diagnosis].

    Science.gov (United States)

    Fritzsche, K; Baumann, K; Schulze-Bonhage, A

    2013-01-01

    The great physical resemblance between epileptic and dissociative seizures and a diagnosis of epilepsy that had been made years ago and usually had been treated unsuccessfully makes it difficult for both physician and patient to communicate the diagnosis of dissociative seizures. A direct referral to psychotherapy treatment is rarely accepted by patients. Intermediate steps, which are based on cooperation between neurologists and psychotherapists, are necessary. The approach that we use to communicate diagnosis and motivation for psychotherapeutic treatment includes eight steps: 1. Welcome and introduction; 2. Jointly watching a video of documented seizures; 3. The message that the seizures are not of epileptic origin, 4. Development of an alternative disease concept; 5. Motivation for a conversation with a representative from psychosomatics; 6. Responding to the fear of "going crazy"; 7. If necessary, briefly touching on the subject of sexual violence; 8. More recommendations and conclusion of the conversation. The manual was discussed and practiced with the attending neurologist in two sessions and is now being regularly used by two neurologists with concomitant supervision.

  15. Seizure Dynamics of Coupled Oscillators with Epileptor Field Model

    Science.gov (United States)

    Zhang, Honghui; Xiao, Pengcheng

    The focus of this paper is to investigate the dynamics of seizure activities by using the Epileptor coupled model. Based on the coexistence of seizure-like event (SLE), refractory status epilepticus (RSE), depolarization block (DB), and normal state, we first study the dynamical behaviors of two coupled oscillators in different activity states with Epileptor model by linking them with slow permittivity coupling. Our research has found that when one oscillator in normal states is coupled with any oscillator in SLE, RSE or DB states, these two oscillators can both evolve into SLE states under appropriate coupling strength. And then these two SLE oscillators can perform epileptiform synchronization or epileptiform anti-synchronization. Meanwhile, SLE can be depressed when considering the fast electrical or chemical coupling in Epileptor model. Additionally, a two-dimensional reduced model is also given to show the effect of coupling number on seizures. Those results can help to understand the dynamical mechanism of the initiation, maintenance, propagation and termination of seizures in focal epilepsy.

  16. 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.

  17. Seizure Disorders: A Review for School Psychologists.

    Science.gov (United States)

    Sachs, Henry T.; Barrett, Rowland P.

    1995-01-01

    Recognizing possible seizure disorders, medication side-effects, behavioral and cognitive effects of seizures, and their treatments are important skills for school psychologists because they affect 500,000 United States school-aged children attending regular education. A knowledgeable school professional serves a critical role in integrating…

  18. 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.

  19. 43 CFR 3.16 - Seizure.

    Science.gov (United States)

    2010-10-01

    ... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Seizure. 3.16 Section 3.16 Public Lands: Interior Office of the Secretary of the Interior PRESERVATION OF AMERICAN ANTIQUITIES § 3.16 Seizure. Any object of antiquity taken, or collection made, on lands owned or controlled by the United States, without...

  20. Orgasm Induced Seizures: A Rare Phenomenon

    African Journals Online (AJOL)

    testing of the brain revealed no structural abnormality. His blood examination findings were ... A variety of stimuli can cause reflex seizures, Some triggers include light, music and cognitive phenomenon. There are case reports ... seizures cause great personal distress and significantly affect marital relationships. Though ...

  1. 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.

  2. 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

  3. 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...

  4. On Emulation-Based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Abbasi, Ali; Wetzel, Jos; Bokslag, Wouter; Zambon, Emmanuele; Etalle, Sandro

    2014-01-01

    Emulation-based network intrusion detection systems have been devised to detect the presence of shellcode in network traffic by trying to execute (portions of) the network packet payloads in an in- strumented environment and checking the execution traces for signs of shellcode activity.

  5. On emulation-based network intrusion detection systems

    NARCIS (Netherlands)

    Abbasi, A.; Wetzels, J.; Bokslag, W.; Zambon, E.; Etalle, S.; Stavrou, A.; Bos, H.; Portokalidis, G.

    2014-01-01

    Emulation-based network intrusion detection systems have been devised to detect the presence of shellcode in network traffic by trying to execute (portions of) the network packet payloads in an instrumented environment and checking the execution traces for signs of shellcode activity.

  6. Active Fault Detection Based on a Statistical Test

    DEFF Research Database (Denmark)

    Sekunda, André Krabdrup; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2016-01-01

    In this paper active fault detection of closed loop systems using dual Youla-Jabr-Bongiorno-Kucera(YJBK) parameters is presented. Until now all detector design for active fault detection using the dual YJBK parameters has been based on CUSUM detectors. Here a method for design of a matched filter...

  7. Nonlinear Model-Based Fault Detection for a Hydraulic Actuator

    NARCIS (Netherlands)

    Van Eykeren, L.; Chu, Q.P.

    2011-01-01

    This paper presents a model-based fault detection algorithm for a specific fault scenario of the ADDSAFE project. The fault considered is the disconnection of a control surface from its hydraulic actuator. Detecting this type of fault as fast as possible helps to operate an aircraft more cost

  8. Efficacy of perampanel for controlling seizures and improving neurological dysfunction in a patient with dentatorubral-pallidoluysian atrophy (DRPLA

    Directory of Open Access Journals (Sweden)

    Hideaki Shiraishi

    2017-01-01

    Full Text Available We administered perampanel (PER to a bedridden 13-year-old male patient with dentatorubral-pallidoluysian atrophy (DRPLA. The DRPLA diagnosis was based on the presence of a CAG trinucleotide repeat in the ATN1 gene. The patient experienced continuous myoclonic seizures and weekly generalized tonic–clonic seizures (GTCs. PER stopped the patient's myoclonic seizures and reduced the GTCs to fragmented clonic seizures. The patient recovered his intellectual abilities and began to walk again with assistance. We suggest that PER be considered as one of the key drugs used to treat patients with DRPLA.

  9. 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.

  10. Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain.

    Science.gov (United States)

    Tauste Campo, Adrià; Principe, Alessandro; Ley, Miguel; Rocamora, Rodrigo; Deco, Gustavo

    2018-04-01

    Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide crossvalidated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial electroencephalography (iEEG) recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable ("degenerate"), and this phase is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to be particularly associated with the activity of the resected regions in patients with validated postsurgical outcome. Our approach characterizes preseizure network dynamics as a cascade of 2 sequential events providing new insights into seizure prediction and control.

  11. Lab-on-a-chip for rapid electrochemical detection of nerve agent Sarin

    DEFF Research Database (Denmark)

    Tan, Hsih-Yin; Loke, Weng Keong; Nguyen, Nam-Trung

    2014-01-01

    This paper reports a lab-on-a-chip for the detection of Sarin nerve agent based on rapid electrochemical detection. The chemical warfare agent Sarin (C4H10FO2P, O-isopropyl methylphosphonofluoridate) is a highly toxic organophosphate that induces rapid respiratory depression, seizures and death...

  12. The effects of lossy compression on diagnostically relevant seizure information in EEG signals.

    Science.gov (United States)

    Higgins, G; McGinley, B; Faul, S; McEvoy, R P; Glavin, M; Marnane, W P; Jones, E

    2013-01-01

    This paper examines the effects of compression on EEG signals, in the context of automated detection of epileptic seizures. Specifically, it examines the use of lossy compression on EEG signals in order to reduce the amount of data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to diagnosing epileptic seizures. Two popular compression methods, JPEG2000 and SPIHT, were used. A range of compression levels was selected for both algorithms in order to compress the signals with varying degrees of loss. This compression was applied to the database of epileptiform data provided by the University of Freiburg, Germany. The real-time EEG analysis for event detection automated seizure detection system was used in place of a trained clinician for scoring the reconstructed data. Results demonstrate that compression by a factor of up to 120:1 can be achieved, with minimal loss in seizure detection performance as measured by the area under the receiver operating characteristic curve of the seizure detection system.

  13. 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.

  14. 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...

  15. 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...

  16. 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...

  17. Exact, almost and delayed fault detection: An observer based approach

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Saberi, Ali; Stoorvogel, Anton A.

    1999-01-01

    This paper consider the problem of fault detection and isolation in continuous- and discrete-time systems while using zero or almost zero threshold. A number of different fault detections and isolation problems using exact or almost exact disturbance decoupling are formulated. Solvability...... conditions are given for the formulated design problems together with methods for appropriate design of observer based fault detectors. The l-step delayed fault detection problem is also considered for discrete-time systems . Moreover, certain indirect fault detection methods such as unknown input observers...

  18. Abnormal traffic flow data detection based on wavelet analysis

    Directory of Open Access Journals (Sweden)

    Xiao Qian

    2016-01-01

    Full Text Available In view of the traffic flow data of non-stationary, the abnormal data detection is difficult.proposed basing on the wavelet analysis and least squares method of abnormal traffic flow data detection in this paper.First using wavelet analysis to make the traffic flow data of high frequency and low frequency component and separation, and then, combined with least square method to find abnormal points in the reconstructed signal data.Wavelet analysis and least square method, the simulation results show that using wavelet analysis of abnormal traffic flow data detection, effectively reduce the detection results of misjudgment rate and false negative rate.

  19. 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

  20. LSTM-Based Hierarchical Denoising Network for Android Malware Detection

    OpenAIRE

    Yan, Jinpei; Qi, Yong; Rao, Qifan

    2018-01-01

    Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequence...

  1. 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.

  2. Fluorescence-Based Multiplex Protein Detection Using Optically Encoded Microbeads

    Directory of Open Access Journals (Sweden)

    Dae Hong Jeong

    2012-03-01

    Full Text Available Potential utilization of proteins for early detection and diagnosis of various diseases has drawn considerable interest in the development of protein-based multiplex detection techniques. Among the various techniques for high-throughput protein screening, optically-encoded beads combined with fluorescence-based target monitoring have great advantages over the planar array-based multiplexing assays. This review discusses recent developments of analytical methods of screening protein molecules on microbead-based platforms. These include various strategies such as barcoded microbeads, molecular beacon-based techniques, and surface-enhanced Raman scattering-based techniques. Their applications for label-free protein detection are also addressed. Especially, the optically-encoded beads such as multilayer fluorescence beads and SERS-encoded beads are successful for generating a large number of coding.

  3. CSI Frequency Domain Fingerprint-Based Passive Indoor Human Detection

    Directory of Open Access Journals (Sweden)

    Chong Han

    2018-04-01

    Full Text Available Passive indoor personnel detection technology is now a hot topic. Existing methods have been greatly influenced by environmental changes, and there are problems with the accuracy and robustness of detection. Passive personnel detection based on Wi-Fi not only solves the above problems, but also has the advantages of being low cost and easy to implement, and can be better applied to elderly care and safety monitoring. In this paper, we propose a passive indoor personnel detection method based on Wi-Fi, which we call FDF-PIHD (Frequency Domain Fingerprint-based Passive Indoor Human Detection. Through this method, fine-grained physical layer Channel State Information (CSI can be extracted to generate feature fingerprints so as to help determine the state in the scene by matching online fingerprints with offline fingerprints. In order to improve accuracy, we combine the detection results of three receiving antennas to obtain the final test result. The experimental results show that the detection rates of our proposed scheme all reach above 90%, no matter whether the scene is human-free, stationary or a moving human presence. In addition, it can not only detect whether there is a target indoors, but also determine the current state of the target.

  4. 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

  5. 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.

  6. Gold Nanoparticles-Based Barcode Analysis for Detection of Norepinephrine.

    Science.gov (United States)

    An, Jeung Hee; Lee, Kwon-Jai; Choi, Jeong-Woo

    2016-02-01

    Nanotechnology-based bio-barcode amplification analysis offers an innovative approach for detecting neurotransmitters. We evaluated the efficacy of this method for detecting norepinephrine in normal and oxidative-stress damaged dopaminergic cells. Our approach use a combination of DNA barcodes and bead-based immunoassays for detecting neurotransmitters with surface-enhanced Raman spectroscopy (SERS), and provides polymerase chain reaction (PCR)-like sensitivity. This method relies on magnetic Dynabeads containing antibodies and nanoparticles that are loaded both with DNA barcords and with antibodies that can sandwich the target protein captured by the Dynabead-bound antibodies. The aggregate sandwich structures are magnetically separated from the solution and treated to remove the conjugated barcode DNA. The DNA barcodes are then identified by SERS and PCR analysis. The concentration of norepinephrine in dopaminergic cells can be readily detected using the bio-barcode assay, which is a rapid, high-throughput screening tool for detecting neurotransmitters.

  7. 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.

  8. 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)

  9. 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).

  10. 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.

  11. 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,

  12. Cumulative Incidence of Seizures and Epilepsy in Ten-Year-Old Children Born Before 28 Weeks' Gestation.

    Science.gov (United States)

    Douglass, Laurie M; Heeren, Timothy C; Stafstrom, Carl E; DeBassio, William; Allred, Elizabeth N; Leviton, Alan; O'Shea, T Michael; Hirtz, Deborah; Rollins, Julie; Kuban, Karl

    2017-08-01

    We evaluated the incidence of seizures and epilepsy in the first decade of life among children born extremely premature (less than 28 weeks' gestation). In a prospective, multicenter, observational study, 889 of 966 eligible children born in 2002 to 2004 were evaluated at two and ten years for neurological morbidity. Complementing questionnaire data to determine a history of seizures, all caregivers were interviewed retrospectively for postneonatal seizures using a validated seizure screen followed by a structured clinical interview by a pediatric epileptologist. A second pediatric epileptologist established an independent diagnosis based on recorded responses of the interview. A third epileptologist determined the final diagnosis when evaluators disagreed (3%). Life table survival methods were used to estimate seizure incidence through ten years. By age ten years, 12.2% (95% confidence interval: 9.8, 14.5) of children had experienced one or more seizures, 7.6% (95% confidence interval: 5.7, 9.5) had epilepsy, 3.2% had seizure with fever, and 1.3% had a single, unprovoked seizure. The seizure incidence increased with decreasing gestational age. In more than 75% of children with seizures, onset was after one year of age. Seizure incidence was comparable in both sexes. Two-thirds of those with epilepsy had other neurological disorders. One third of children with epilepsy were not recorded on the medical history questionnaire. The incidence of epilepsy through age ten years among children born extremely premature is approximately 7- to 14-fold higher than the 0.5% to 1% lifetime incidence reported in the general pediatric population. Seizures in this population are under-recognized, and possibly underdiagnosed, by parents and providers. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. 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.

  14. 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.

  15. The SAFARI Score to Assess the Risk of Convulsive Seizure During Admission for Aneurysmal Subarachnoid Hemorrhage.

    Science.gov (United States)

    Jaja, Blessing N R; Schweizer, Tom A; Claassen, Jan; Le Roux, Peter; Mayer, Stephan A; Macdonald, R Loch

    2018-06-01

    Seizure is a significant complication in patients under acute admission for aneurysmal SAH and could result in poor outcomes. Treatment strategies to optimize management will benefit from methods to better identify at-risk patients. To develop and validate a risk score for convulsive seizure during acute admission for SAH. A risk score was developed in 1500 patients from a single tertiary hospital and externally validated in 852 patients. Candidate predictors were identified by systematic review of the literature and were included in a backward stepwise logistic regression model with in-hospital seizure as a dependent variable. The risk score was assessed for discrimination using the area under the receiver operator characteristics curve (AUC) and for calibration using a goodness-of-fit test. The SAFARI score, based on 4 items (age ≥ 60 yr, seizure occurrence before hospitalization, ruptured aneurysm in the anterior circulation, and hydrocephalus requiring cerebrospinal fluid diversion), had AUC = 0.77, 95% confidence interval (CI): 0.73-0.82 in the development cohort. The validation cohort had AUC = 0.65, 95% CI 0.56-0.73. A calibrated increase in the risk of seizure was noted with increasing SAFARI score points. The SAFARI score is a simple tool that adequately stratified SAH patients according to their risk for seizure using a few readily derived predictor items. It may contribute to a more individualized management of seizure following SAH.

  16. The prognostic value of amplitude-integrated EEG in full-term neonates with seizures.

    Directory of Open Access Journals (Sweden)

    Dandan Zhang

    Full Text Available Neonatal seizures pose a high risk for adverse outcome in survived infants. While the prognostic value of amplitude-integrated electroencephalogram (aEEG is well established in neonates with encephalopathy and asphyxia, neonatal seizure studies focusing on the direct correlation between early aEEG measurement and subsequent neurologic outcome are scarce. In this study, the prognostic value of aEEG features was systematically analyzed in 143 full-term neonates to identify prognostic indicators of neurodevelopmental outcome. Neonatal aEEG features of background pattern, cyclicity, and seizure activity, as well as the etiology of neonatal seizures, were significantly associated with neurodevelopmental outcome at one year of age. aEEG background pattern was highly associated with neurologic outcomes (χ² = 116.9, followed by aEEG cyclicity (χ² = 87.2 and seizure etiology (χ² = 79.3. Multiple linear regression showed that the four predictors explained 71.2% of the variation in neurological outcome, with standardized β coefficients of 0.44, 0.24, 0.22, and 0.14 for the predictors of aEEG background pattern, cyclicity, etiology, and aEEG seizure activity, respectively. This clinically applicable scoring system based on etiology and three aEEG indices would allow pediatricians to assess the risk for neurodevelopmental impairment and facilitate an early intervention in newborns developing seizures.

  17. Train integrity detection risk analysis based on PRISM

    Science.gov (United States)

    Wen, Yuan

    2018-04-01

    GNSS based Train Integrity Monitoring System (TIMS) is an effective and low-cost detection scheme for train integrity detection. However, as an external auxiliary system of CTCS, GNSS may be influenced by external environments, such as uncertainty of wireless communication channels, which may lead to the failure of communication and positioning. In order to guarantee the reliability and safety of train operation, a risk analysis method of train integrity detection based on PRISM is proposed in this article. First, we analyze the risk factors (in GNSS communication process and the on-board communication process) and model them. Then, we evaluate the performance of the model in PRISM based on the field data. Finally, we discuss how these risk factors influence the train integrity detection process.

  18. Learning-Based Detection of Harmful Data in Mobile Devices

    Directory of Open Access Journals (Sweden)

    Seok-Woo Jang

    2016-01-01

    Full Text Available The Internet has supported diverse types of multimedia content flowing freely on smart phones and tablet PCs based on its easy accessibility. However, multimedia content that can be emotionally harmful for children is also easily spread, causing many social problems. This paper proposes a method to assess the harmfulness of input images automatically based on an artificial neural network. The proposed method first detects human face areas based on the MCT features from the input images. Next, based on color characteristics, this study identifies human skin color areas along with the candidate areas of nipples, one of the human body parts representing harmfulness. Finally, the method removes nonnipple areas among the detected candidate areas using the artificial neural network. The experimental results show that the suggested neural network learning-based method can determine the harmfulness of various types of images more effectively by detecting nipple regions from input images robustly.

  19. Two-Stage Part-Based Pedestrian Detection

    DEFF Research Database (Denmark)

    Møgelmose, Andreas; Prioletti, Antonio; Trivedi, Mohan M.

    2012-01-01

    Detecting pedestrians is still a challenging task for automotive vision system due the extreme variability of targets, lighting conditions, occlusions, and high speed vehicle motion. A lot of research has been focused on this problem in the last 10 years and detectors based on classifiers has...... gained a special place among the different approaches presented. This work presents a state-of-the-art pedestrian detection system based on a two stages classifier. Candidates are extracted with a Haar cascade classifier trained with the DaimlerDB dataset and then validated through part-based HOG...... of several metrics, such as detection rate, false positives per hour, and frame rate. The novelty of this system rely in the combination of HOG part-based approach, tracking based on specific optimized feature and porting on a real prototype....

  20. Age- and sex-dependent susceptibility to phenobarbital-resistant neonatal seizures: role of chloride co-transporters.

    Science.gov (United States)

    Kang, Seok Kyu; Markowitz, Geoffrey J; Kim, Shin Tae; Johnston, Michael V; Kadam, Shilpa D

    2015-01-01

    Ischemia in the immature brain is an important cause of neonatal seizures. Temporal evolution of acquired neonatal seizures and their response to anticonvulsants are of great interest, given the unreliability of the clinical correlates and poor efficacy of first-line anti-seizure drugs. The expression and function of the electroneutral chloride co-transporters KCC2 and NKCC1 influence the anti-seizure efficacy of GABAA-agonists. To investigate ischemia-induced seizure susceptibility and efficacy of the GABAA-agonist phenobarbital (PB), with NKCC1 antagonist bumetanide (BTN) as an adjunct treatment, we utilized permanent unilateral carotid-ligation to produce acute ischemic-seizures in post-natal day 7, 10, and 12 CD1 mice. Immediate post-ligation video-electroencephalograms (EEGs) quantitatively evaluated baseline and post-treatment seizure burdens. Brains were examined for stroke-injury and western blot analyses to evaluate the expression of KCC2 and NKCC1. Severity of acute ischemic seizures post-ligation was highest at P7. PB was an efficacious anti-seizure agent at P10 and P12, but not at P7. BTN failed as an adjunct, at all ages tested and significantly blunted PB-efficacy at P10. Significant acute post-ischemic downregulation of KCC2 was detected at all ages. At P7, males displayed higher age-dependent seizure susceptibility, associated with a significant developmental lag in their KCC2 expression. This study established a novel neonatal mouse model of PB-resistant seizures that demonstrates age/sex-dependent susceptibility. The age-dependent profile of KCC2 expression and its post-insult downregulation may underlie the PB-resistance reported in this model. Blocking NKCC1 with low-dose BTN following PB treatment failed to improve PB-efficacy.

  1. 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)

  2. Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

    Science.gov (United States)

    Ge, Jing; Zhang, Guoping

    2015-01-01

    Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

  3. Do reflex seizures and spontaneous seizures form a continuum? - triggering factors and possible common mechanisms.

    Science.gov (United States)

    Irmen, Friederike; Wehner, Tim; Lemieux, Louis

    2015-02-01

    Recent changes in the understanding and classification of reflex seizures have fuelled a debate on triggering mechanisms of seizures and their conceptual organization. Previous studies and patient reports have listed extrinsic and intrinsic triggers, albeit their multifactorial and dynamic nature is poorly understood. This paper aims to review literature on extrinsic and intrinsic seizure triggers and to discuss common mechanisms among them. Among self-reported seizure triggers, emotional stress is most frequently named. Reflex seizures are typically associated with extrinsic sensory triggers; however, intrinsic cognitive or proprioceptive triggers have also been assessed. The identification of a trigger underlying a seizure may be more difficult if it is intrinsic and complex, and if triggering mechanisms are multifactorial. Therefore, since observability of triggers varies and triggers are also found in non-reflex seizures, the present concept of reflex seizures may be questioned. We suggest the possibility of a conceptual continuum between reflex and spontaneous seizures rather than a dichotomy and discuss evidence to the notion that to some extent most seizures might be triggered. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  4. 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.

  5. Trained neurons-based motion detection in optical camera communications

    Science.gov (United States)

    Teli, Shivani; Cahyadi, Willy Anugrah; Chung, Yeon Ho

    2018-04-01

    A concept of trained neurons-based motion detection (TNMD) in optical camera communications (OCC) is proposed. The proposed TNMD is based on neurons present in a neural network that perform repetitive analysis in order to provide efficient and reliable motion detection in OCC. This efficient motion detection can be considered another functionality of OCC in addition to two traditional functionalities of illumination and communication. To verify the proposed TNMD, the experiments were conducted in an indoor static downlink OCC, where a mobile phone front camera is employed as the receiver and an 8 × 8 red, green, and blue (RGB) light-emitting diode array as the transmitter. The motion is detected by observing the user's finger movement in the form of centroid through the OCC link via a camera. Unlike conventional trained neurons approaches, the proposed TNMD is trained not with motion itself but with centroid data samples, thus providing more accurate detection and far less complex detection algorithm. The experiment results demonstrate that the TNMD can detect all considered motions accurately with acceptable bit error rate (BER) performances at a transmission distance of up to 175 cm. In addition, while the TNMD is performed, a maximum data rate of 3.759 kbps over the OCC link is obtained. The OCC with the proposed TNMD combined can be considered an efficient indoor OCC system that provides illumination, communication, and motion detection in a convenient smart home environment.

  6. 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.

  7. The incidence of unprovoked seizures and occurrence of neurodevelopmental comorbidities in children at the time of their first epileptic seizure and during the subsequent six months.

    Science.gov (United States)

    Åndell, Eva; Tomson, Torbjörn; Carlsson, Sofia; Hellebro, Eva; Andersson, Tomas; Adelöw, Cecilia; Åmark, Per

    2015-07-01

    To evaluate the incidence of unprovoked seizures in children and the prevalence of related neurodevelopmental comorbidities at the time of the presumed first seizure and six months thereafter. The medical records of all children (0-18 years of age) seeking medical attention as the result of a first unprovoked seizure between September 1, 2001 and December 31, 2006, and registered in the population-based Stockholm Incidence Registry of Epilepsy (SIRE) were reviewed. Neurodevelopmental comorbidities were evaluated on the basis of the medical records from this first visit and from other healthcare during the following six months. The incidence of unprovoked seizures was between 30 and 204/100,000 person years (n=766) in the different age groups. It was highest among the youngest children and lowest among the 18-year-olds with small gender differences. The most common neurodevelopment comorbidities were developmental delay (22%, CI: 19-25%), speech/language and learning difficulties (23%, CI: 20-26%) and intellectual disability (16%, CI: 13-18%). The types of neurodevelopmental comorbidity varied by age at the time of seizure onset, with cerebral palsy being more common among the 0-5-year-olds, attention deficits among the 6-16-year-olds, and autism and psychiatric diagnosis among the older children. An associated neurodevelopmental comorbidity was more common among those experiencing recurrent than single seizures during follow-up six months from the index seizure (42% versus 66%). In 68% (CI: 64-71%) of the children there was no known or suspected neurodevelopmental comorbidity. The incidence of unprovoked, non-febrile seizures among 0-18-year-olds included in the SIRE was 67/100,000 person-years. Neurodevelopmental comorbidities were common already at the time of onset of the seizure disorder, indicating that neither seizure treatment nor seizures were the underlying cause of other neurodevelopmental symptoms in these patients during the period studied. Copyright

  8. Oxaliplatin-Induced Tonic-Clonic Seizures

    Directory of Open Access Journals (Sweden)

    Ahmad K. Rahal

    2015-01-01

    Full Text Available Oxaliplatin is a common chemotherapy drug used for colon and gastric cancers. Common side effects are peripheral neuropathy, hematological toxicity, and allergic reactions. A rare side effect is seizures which are usually associated with posterior reversible leukoencephalopathy syndrome (PRES. A 50-year-old male patient presented with severe abdominal pain. CT scan of the abdomen showed acute appendicitis. Appendectomy was done and pathology showed mixed adenoneuroendocrine carcinoma. Adjuvant chemotherapy was started with Folinic acid, Fluorouracil, and Oxaliplatin (FOLFOX. During the third cycle of FOLFOX, the patient developed tonic-clonic seizures. Laboratory workup was within normal limits. EEG and MRI of the brain showed no acute abnormality. The patient was rechallenged with FOLFOX but he had tonic-clonic seizures for the second time. His chemotherapy regimen was switched to Folinic acid, Fluorouracil, and Irinotecan (FOLFIRI. After 5 cycles of FOLFIRI, the patient did not develop any seizures, making Oxaliplatin the most likely culprit for his seizures. Oxaliplatin-induced seizures rarely occur in the absence of PRES. One case report has been described in the literature. We present a rare case of tonic-clonic seizures in a patient receiving Oxaliplatin in the absence of PRES.

  9. Image Registration-Based Bolt Loosening Detection of Steel Joints

    Science.gov (United States)

    2018-01-01

    Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts. PMID:29597264

  10. Image Registration-Based Bolt Loosening Detection of Steel Joints.

    Science.gov (United States)

    Kong, Xiangxiong; Li, Jian

    2018-03-28

    Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts.

  11. A Survey on Anomaly Based Host Intrusion Detection System

    Science.gov (United States)

    Jose, Shijoe; Malathi, D.; Reddy, Bharath; Jayaseeli, Dorathi

    2018-04-01

    An intrusion detection system (IDS) is hardware, software or a combination of two, for monitoring network or system activities to detect malicious signs. In computer security, designing a robust intrusion detection system is one of the most fundamental and important problems. The primary function of system is detecting intrusion and gives alerts when user tries to intrusion on timely manner. In these techniques when IDS find out intrusion it will send alert massage to the system administrator. Anomaly detection is an important problem that has been researched within diverse research areas and application domains. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. From the existing anomaly detection techniques, each technique has relative strengths and weaknesses. The current state of the experiment practice in the field of anomaly-based intrusion detection is reviewed and survey recent studies in this. This survey provides a study of existing anomaly detection techniques, and how the techniques used in one area can be applied in another application domain.

  12. Energy detection based on undecimated discrete wavelet transform and its application in magnetic anomaly detection.

    Directory of Open Access Journals (Sweden)

    Xinhua Nie

    Full Text Available Magnetic anomaly detection (MAD is a passive approach for detection of a ferromagnetic target, and its performance is often limited by external noises. In consideration of one major noise source is the fractal noise (or called 1/f noise with a power spectral density of 1/fa (0detection method based on undecimated discrete wavelet transform (UDWT is proposed in this paper. Firstly, the foundations of magnetic anomaly detection and UDWT are introduced in brief, while a possible detection system based on giant magneto-impedance (GMI magnetic sensor is also given out. Then our proposed energy detection based on UDWT is described in detail, and the probabilities of false alarm and detection for given the detection threshold in theory are presented. It is noticeable that no a priori assumptions regarding the ferromagnetic target or the magnetic noise probability are necessary for our method, and different from the discrete wavelet transform (DWT, the UDWT is shift invariant. Finally, some simulations are performed and the results show that the detection performance of our proposed detector is better than that of the conventional energy detector even utilized in the Gaussian white noise, especially when the spectral parameter α is less than 1.0. In addition, a real-world experiment was done to demonstrate the advantages of the proposed method.

  13. Risk of seizures and status epilepticus in older patients with liver disease.

    Science.gov (United States)

    Alkhachroum, Ayham M; Rubinos, Clio; Kummer, Benjamin R; Parikh, Neal S; Chen, Monica; Chatterjee, Abhinaba; Reynolds, Alexandra; Merkler, Alexander E; Claassen, Jan; Kamel, Hooman

    2018-06-06

    Seizures can be provoked by systemic diseases associated with metabolic derangements, but the association between liver disease and seizures remains unclear. We performed a retrospective cohort study using inpatient and outpatient claims between 2008 and 2015 from a nationally representative 5% sample of Medicare beneficiaries. The primary exposure variable was cirrhosis, and the secondary exposure was mild, noncirrhotic liver disease. The primary outcome was seizure, and the secondary outcome was status epilepticus. Diagnoses were ascertained using validated International Classification of Diseases, Ninth Edition, Clinical Modification codes. Survival statistics were used to calculate incidence rates, and Cox proportional hazards models were used to examine the association between exposures and outcomes while adjusting for seizure risk factors. Among 1 782 402 beneficiaries, we identified 10 393 (0.6%) beneficiaries with cirrhosis and 19 557 (1.1%) with mild, noncirrhotic liver disease. Individuals with liver disease were older and had more seizure risk factors than those without liver disease. Over 4.6 ± 2.2 years of follow-up, 49 843 (2.8%) individuals were diagnosed with seizures and 25 patients (0.001%) were diagnosed with status epilepticus. Cirrhosis was not associated with seizures (hazard ratio [HR] = 1.1, 95% confidence interval [CI] = 1.0-1.3), but there was an association with status epilepticus (HR = 1.9, 95% CI = 1.3-2.8). Mild liver disease was not associated with a higher risk of seizures (HR = 0.8, 95% CI = 0.6-0.9) or status epilepticus (HR = 1.1, 95% CI = 0.7-1.5). In a large, population-based cohort, we found an association between cirrhosis and status epilepticus, but no overall association between liver disease and seizures. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

  14. LSTM-Based Hierarchical Denoising Network for Android Malware Detection

    Directory of Open Access Journals (Sweden)

    Jinpei Yan

    2018-01-01

    Full Text Available Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN, a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method.

  15. Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

    Science.gov (United States)

    Housh, Mashor; Ohar, Ziv

    2017-03-01

    The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. 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%.

  17. 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%.

  18. 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

  19. 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 ...

  20. 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.

  1. 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.

  2. A measurement-based technique for incipient anomaly detection

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2016-01-01

    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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Cosmic String Detection with Tree-Based Machine Learning

    Science.gov (United States)

    Vafaei Sadr, A.; Farhang, M.; Movahed, S. M. S.; Bassett, B.; Kunz, M.

    2018-05-01

    We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies. The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of the processed CMB maps that boost cosmic string detectability. Our proposed classifiers, after training, give results similar to or better than claimed detectability levels from other methods for string tension, Gμ. They can make 3σ detection of strings with Gμ ≳ 2.1 × 10-10 for noise-free, 0.9΄-resolution CMB observations. The minimum detectable tension increases to Gμ ≳ 3.0 × 10-8 for a more realistic, CMB S4-like (II) strategy, improving over previous results.

  9. 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.

  10. 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.

  11. ABC optimized RBF network for classification of EEG signal for epileptic seizure identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    2017-03-01

    Full Text Available The brain signals usually generate certain electrical signals that can be recorded and analyzed for detection in several brain disorder diseases. These small signals are expressly called as Electroencephalogram (EEG signals. This research work analyzes the epileptic disorder in human brain through EEG signal analysis by integrating the best attributes of Artificial Bee Colony (ABC and radial basis function networks (RBFNNs. We have used Discrete Wavelet Transform (DWT technique for extraction of potential features from the signal. In our study, for classification of these signals, in this paper, the RBFNNs have been trained by a modified version of ABC algorithm. In the modified ABC, the onlooker bees are selected based on binary tournament unlike roulette wheel selection of ABC. Additionally, kernels such as Gaussian, Multi-quadric, and Inverse-multi-quadric are used for measuring the effectiveness of the method in numerous mixtures of healthy segments, seizure-free segments, and seizure segments. Our experimental outcomes confirm that RBFNN with inverse-multi-quadric kernel trained with modified ABC is significantly better than RBFNNs with other kernels trained by ABC and modified ABC.

  12. A dynamic bead-based microarray for parallel DNA detection

    International Nuclear Information System (INIS)

    Sochol, R D; Lin, L; Casavant, B P; Dueck, M E; Lee, L P

    2011-01-01

    A microfluidic system has been designed and constructed by means of micromachining processes to integrate both microfluidic mixing of mobile microbeads and hydrodynamic microbead arraying capabilities on a single chip to simultaneously detect multiple bio-molecules. The prototype system has four parallel reaction chambers, which include microchannels of 18 × 50 µm 2 cross-sectional area and a microfluidic mixing section of 22 cm length. Parallel detection of multiple DNA oligonucleotide sequences was achieved via molecular beacon probes immobilized on polystyrene microbeads of 16 µm diameter. Experimental results show quantitative detection of three distinct DNA oligonucleotide sequences from the Hepatitis C viral (HCV) genome with single base-pair mismatch specificity. Our dynamic bead-based microarray offers an effective microfluidic platform to increase parallelization of reactions and improve microbead handling for various biological applications, including bio-molecule detection, medical diagnostics and drug screening

  13. A universal DNA-based protein detection system.

    Science.gov (United States)

    Tran, Thua N N; Cui, Jinhui; Hartman, Mark R; Peng, Songming; Funabashi, Hisakage; Duan, Faping; Yang, Dayong; March, John C; Lis, John T; Cui, Haixin; Luo, Dan

    2013-09-25

    Protein immune detection requires secondary antibodies which must be carefully selected in order to avoid interspecies cross-reactivity, and is therefore restricted by the limited availability of primary/secondary antibody pairs. Here we present a versatile DNA-based protein detection system using a universal adapter to interface between IgG antibodies and DNA-modified reporter molecules. As a demonstration of this capability, we successfully used DNA nano-barcodes, quantum dots, and horseradish peroxidase enzyme to detect multiple proteins using our DNA-based labeling system. Our system not only eliminates secondary antibodies but also serves as a novel method platform for protein detection with modularity, high capacity, and multiplexed capability.

  14. Live face detection based on the analysis of Fourier spectra

    Science.gov (United States)

    Li, Jiangwei; Wang, Yunhong; Tan, Tieniu; Jain, Anil K.

    2004-08-01

    Biometrics is a rapidly developing technology that is to identify a person based on his or her physiological or behavioral characteristics. To ensure the correction of authentication, the biometric system must be able to detect and reject the use of a copy of a biometric instead of the live biometric. This function is usually termed "liveness detection". This paper describes a new method for live face detection. Using structure and movement information of live face, an effective live face detection algorithm is presented. Compared to existing approaches, which concentrate on the measurement of 3D depth information, this method is based on the analysis of Fourier spectra of a single face image or face image sequences. Experimental results show that the proposed method has an encouraging performance.

  15. 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.

  16. Random Valued Impulse Noise Removal Using Region Based Detection Approach

    Directory of Open Access Journals (Sweden)

    S. Banerjee

    2017-12-01

    Full Text Available Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level.

  17. 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.

  18. 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.

  19. 27 CFR 478.152 - Seizure and forfeiture.

    Science.gov (United States)

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 3 2010-04-01 2010-04-01 false Seizure and forfeiture... Exemptions, Seizures, and Forfeitures § 478.152 Seizure and forfeiture. (a) Any firearm or ammunition... demonstrated by clear and convincing evidence, shall be subject to seizure and forfeiture, and all provisions...

  20. 19 CFR 162.92 - Notice of seizure.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 2 2010-04-01 2010-04-01 false Notice of seizure. 162.92 Section 162.92 Customs... (CONTINUED) INSPECTION, SEARCH, AND SEIZURE Civil Asset Forfeiture Reform Act § 162.92 Notice of seizure. (a) Generally. Customs will send written notice of seizure as provided in this section to all known interested...

  1. 8 CFR 1280.21 - Seizure of aircraft.

    Science.gov (United States)

    2010-01-01

    ... 8 Aliens and Nationality 1 2010-01-01 2010-01-01 false Seizure of aircraft. 1280.21 Section 1280... REGULATIONS IMPOSITION AND COLLECTION OF FINES § 1280.21 Seizure of aircraft. Seizure of an aircraft under the... that its value is less than the amount of the fine which may be imposed. If seizure of an aircraft for...

  2. 8 CFR 280.21 - Seizure of aircraft.

    Science.gov (United States)

    2010-01-01

    ... 8 Aliens and Nationality 1 2010-01-01 2010-01-01 false Seizure of aircraft. 280.21 Section 280.21... OF FINES § 280.21 Seizure of aircraft. Seizure of an aircraft under the authority of section 239 of... than the amount of the fine which may be imposed. If seizure of an aircraft for violation of section...

  3. 50 CFR 12.11 - Notification of seizure.

    Science.gov (United States)

    2010-10-01

    ... 50 Wildlife and Fisheries 1 2010-10-01 2010-10-01 false Notification of seizure. 12.11 Section 12... SEIZURE AND FORFEITURE PROCEDURES Preliminary Requirements § 12.11 Notification of seizure. Except where the owner or consignee is personally notified or seizure is made pursuant to a search warrant, the...

  4. 50 CFR 12.5 - Seizure by other agencies.

    Science.gov (United States)

    2010-10-01

    ... 50 Wildlife and Fisheries 1 2010-10-01 2010-10-01 false Seizure by other agencies. 12.5 Section 12... SEIZURE AND FORFEITURE PROCEDURES General Provisions § 12.5 Seizure by other agencies. Any authorized... the laws listed in § 12.2 will, if so requested, deliver such seizure to the appropriate Special Agent...

  5. Local cerebral blood flow and glucose metabolism during seizure in spontaneously epileptic El mice

    International Nuclear Information System (INIS)

    Hosokawa, Chisa; Ochi, Hironobu; Yamagami, Sakae; Kawabe, Joji; Kobashi, Toshiko; Okamura, Terue; Yamada, Ryusaku

    1995-01-01

    Local cerebral blood flow and glucose metabolism were examined in spontaneously epileptic El mice using autoradiography with 125 I-IMP and 14 C-DG in the interictal phase and during seizure. El (+) mice that developed generalized tonic-clonic convulsions and El (-) mice that received no stimulation and had no history of epileptic seizures were examined. The seizure non-susceptible, maternal strain ddY mice were used as control. Uptake ratios for IMP and DG in mouse brain were calculated using the autoradiographic density. In the interictal phase, the pattern of local cerebral blood flow of El (+) mice was similar to that of ddY and El (-) mice, and glucose metabolism in the hippocampus was higher in El (+) mice than in El (-) and ddY mice, but flow and metabolism were nearly matched. During seizure, no significant changed blood flow and increased glucose metabolism in the hippocampus, the epileptic focus, and no markedly changed blood flow and depressed glucose metabolism in other brain regions were observed and considered to be flow-metabolism uncoupling. These observations have never been reported in clinical or experimental studies of epilepsy. Seizures did not cause large regional differences in cerebral blood flow. Therefore, only glucose metabolism is useful for detection of the focus of secondary generalized seizures in El mice, and appeared possibly to be related to the pathophysiology of secondary generalized epilepsy in El mice. (author)

  6. 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

  7. Epileptic seizure, as the first symptom of hypoparathyroidism in children, does not require antiepileptic drugs

    OpenAIRE

    Liu, Meng-Jia; Li, Jiu-Wei; Shi, Xiu-Yu; Hu, Lin-Yan; Zou, Li-Ping

    2016-01-01

    Objective 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. Method 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. Result A total of 42 pa...

  8. Comparison of efficacy of phenytoin and levetiractetam for prevention of early post traumatic seizures

    International Nuclear Information System (INIS)

    Khan, S.A.; Bhatti, S.N.; Afridi, E.A.K.; Zadran, K.K.; Shah, S.S.A.; Khan, A.A.

    2016-01-01

    The incidence of early post-traumatic seizures after civilian traumatic brain injury ranges 4-25%. The control of early post-traumatic seizure is mandatory because these acute insults may add secondary damage to the already damaged brain with poor outcome. Prophylactic use of anti-epileptic drugs have been found to be have variable efficacy against early post-traumatic seizures. The objective of this study was to compare the efficacy of Phenytion and Levetiracetam in prevention of early post-traumatic seizures in moderate to severe traumatic brain injury. Methods: This Randomized Controlled Trial was conducted in department of Neurosurgery, Ayub Medical College, Abbottabad from March, 2012 to March 2014. The patients with moderate to severe head injury were randomly allocated in two groups. Patients in group A were given phenytoin and patients in group B were given Levetiracetam. Patients were followed for one week to detect efficacy of drug in terms of early post traumatic seizures. Results: The 154 patients included in the study were equally divided into two groups. Out of 154 patients 115 (74.7%) were male while 29 (25.3%) were females. Age of patients ranges from 7-48 (24.15 ± 9.56) years. Ninety one (59.1%) patients had moderate head injury while 63 (40.9%) patients had severe head injury. Phenytoin was effective in preventing early post traumatic seizures in 73 (94.8%) patients whereas Levetiracetam effectively controlled seizures in 70 (90.95%) cases (p-value of .348). Conclusion: There is no statistically significant difference in the efficacy of Phenytoin and Levetiracetam in prophylaxis of early post-traumatic seizures in cases of moderate to severe traumatic brain injury. (author)

  9. 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.

  10. 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.

  11. 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.

  12. Multifractal detrended cross-correlation analysis for epileptic patient in seizure and seizure free status

    International Nuclear Information System (INIS)

    Ghosh, Dipak; Dutta, Srimonti; Chakraborty, Sayantan

    2014-01-01

    Highlights: • We analyze EEG of patients during seizure and in seizure free interval. • Data from different sections of the brain and seizure activity was analyzed. • Assessment of cross-correlation in seizure and seizure free interval using MF-DXA technique. - Abstract: This paper reports a study of EEG data of epileptic patients in terms of multifractal detrended cross-correlation analysis (MF-DXA). The EEG clinical data were obtained from the EEG Database available with the Clinic of Epileptology of the University Hospital of Bonn, Germany. The data sets (C, D, and E) were taken from five epileptic patients undergoing presurgical evaluations. The data sets consist of intracranial EEG recordings during seizure-free intervals (interictal periods) from within the epileptogenic zone (D) and from the hippocampal formation of the opposite hemisphere of the epileptic patients’ brain, respectively (C). The data set (E) was recorded during seizure activity (ictal periods). MF-DXA is a very rigorous and robust tool for assessment of cross-correlation among two nonlinear time series. The study reveals the degree of cross-correlation is more among seizure and seizure free interval in epileptogenic zone. These data are very significant for diagnosis, onset and prognosis of epileptic patients

  13. Influence of vigilance state on physiological consequences of seizures and seizure-induced death in mice.

    Science.gov (United States)

    Hajek, Michael A; Buchanan, Gordon F

    2016-05-01

    Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in patients with refractory epilepsy. SUDEP occurs more commonly during nighttime sleep. The details of why SUDEP occurs at night are not well understood. Understanding why SUDEP occurs at night during sleep might help to better understand why SUDEP occurs at all and hasten development of preventive strategies. Here we aimed to understand circumstances causing seizures that occur during sleep to result in death. Groups of 12 adult male mice were instrumented for EEG, EMG, and EKG recording and subjected to seizure induction via maximal electroshock (MES) during wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Seizure inductions were performed with concomitant EEG, EMG, and EKG recording and breathing assessment via whole body plethysmography. Seizures induced via MES during sleep were associated with more profound respiratory suppression and were more likely to result in death. Despite REM sleep being a time when seizures do not typically occur spontaneously, when seizures were forced to occur during REM sleep, they were invariably fatal in this model. An examination of baseline breathing revealed that mice that died following a seizure had increased baseline respiratory rate variability compared with those that did not die. These data demonstrate that sleep, especially REM sleep, can be a dangerous time for a seizure to occur. These data also demonstrate that there may be baseline respiratory abnormalities that can predict which individuals have higher risk for seizure-induced death.

  14. Distance Based Method for Outlier Detection of Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Haibin Zhang

    2016-01-01

    Full Text Available We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided as an outlier. Further, we formalize a sliding window based method to improve the outlier detection performance. Finally, to estimate the KDE by training sensor readings with errors, we introduce a Hidden Markov Model (HMM based method to estimate the most probable ground truth values which have the maximum probability to produce the training data. Simulation results show that the proposed method possesses a good detection accuracy with a low false alarm rate.

  15. Hemorrhagic Retinopathy after Spondylosis Surgery and Seizure.

    Science.gov (United States)

    Kord Valeshabad, Ali; Francis, Andrew W; Setlur, Vikram; Chang, Peter; Mieler, William F; Shahidi, Mahnaz

    2015-08-01

    To report bilateral hemorrhagic retinopathy in an adult female subject after lumbar spinal surgery and seizure. A 38-year-old woman presented with bilateral blurry vision and spots in the visual field. The patient had lumbar spondylosis surgery that was complicated by a dural tear with persistent cerebrospinal fluid leak. Visual symptoms started immediately after witnessed seizure-like activity. At presentation, visual acuity was 20/100 and 20/25 in the right and left eye, respectively. Dilated fundus examination demonstrated bilateral hemorrhagic retinopathy with subhyaloid, intraretinal, and subretinal involvement. At 4-month follow-up, visual acuity improved to 20/60 and 20/20 in the right and left eye, respectively. Dilated fundus examination and fundus photography showed resolution of retinal hemorrhages in both eyes. The first case of bilateral hemorrhagic retinopathy after lumbar spondylosis surgery and witnessed seizure in an adult was reported. Ophthalmic examination may be warranted after episodes of seizure in adults.

  16. 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.

  17. Aging models of acute seizures and epilepsy.

    Science.gov (United States)

    Kelly, Kevin M

    2010-01-01

    Aged animals have been used by researchers to better understand the differences between the young and the aged brain and how these differences may provide insight into the mechanisms of acute seizures and epilepsy in the elderly. To date, there have been relatively few studies dedicated to the modeling of acute seizures and epilepsy in aged, healthy animals. Inherent challenges to this area of research include the costs associated with the purchase and maintenance of older animals and, at times, the unexpected and potentially confounding comorbidities associated with aging. However, recent studies using a variety of in vivo and in vitro models of acute seizures and epilepsy in mice and rats have built upon early investigations in the field, all of which has provided an expanded vision of seizure generation and epileptogenesis in the aged brain. Results of these studies could potentially translate to new and tailored interventional approaches that limit or prevent the development of epilepsy in the elderly.

  18. Effect of prophylactic phenobarbital on seizures, encephalopathy ...

    African Journals Online (AJOL)

    cerebral metabolism and re-oxygenation, which lead to cerebral oedema .... their serum electrolytes and glucose, calcium and magnesium levels measured. ..... Dzhala V, Ben-Ari Y, Khazipov R. Seizures accelerate anoxia-induced neuronal.

  19. 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.

  20. The Epilepsies and Seizures: Hope Through Research

    Science.gov (United States)

    ... epilepticus and sudden unexpected death in epilepsy (SUDEP) . Status Epilepticus Status epilepticus is a potentially life-threatening condition ... otherwise experience good seizure control with their medication. status epilepticus – a potentially life-threatening condition in which a ...