Fetal ECG extraction using independent component analysis by Jade approach

Science.gov (United States)

Giraldo-Guzmán, Jader; Contreras-Ortiz, Sonia H.; Lasprilla, Gloria Isabel Bautista; Kotas, Marian

2017-11-01

Fetal ECG monitoring is a useful method to assess the fetus health and detect abnormal conditions. In this paper we propose an approach to extract fetal ECG from abdomen and chest signals using independent component analysis based on the joint approximate diagonalization of eigenmatrices approach. The JADE approach avoids redundancy, what reduces matrix dimension and computational costs. Signals were filtered with a high pass filter to eliminate low frequency noise. Several levels of decomposition were tested until the fetal ECG was recognized in one of the separated sources output. The proposed method shows fast and good performance.

Robust electrocardiogram (ECG) beat classification using discrete wavelet transform

International Nuclear Information System (INIS)

Minhas, Fayyaz-ul-Amir Afsar; Arif, Muhammad

2008-01-01

This paper presents a robust technique for the classification of six types of heartbeats through an electrocardiogram (ECG). Features extracted from the QRS complex of the ECG using a wavelet transform along with the instantaneous RR-interval are used for beat classification. The wavelet transform utilized for feature extraction in this paper can also be employed for QRS delineation, leading to reduction in overall system complexity as no separate feature extraction stage would be required in the practical implementation of the system. Only 11 features are used for beat classification with the classification accuracy of ∼99.5% through a KNN classifier. Another main advantage of this method is its robustness to noise, which is illustrated in this paper through experimental results. Furthermore, principal component analysis (PCA) has been used for feature reduction, which reduces the number of features from 11 to 6 while retaining the high beat classification accuracy. Due to reduction in computational complexity (using six features, the time required is ∼4 ms per beat), a simple classifier and noise robustness (at 10 dB signal-to-noise ratio, accuracy is 95%), this method offers substantial advantages over previous techniques for implementation in a practical ECG analyzer

A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Bohui Zhu

2013-01-01

Full Text Available This paper presents a novel maximum margin clustering method with immune evolution (IEMMC for automatic diagnosis of electrocardiogram (ECG arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.

ECG acquisition and automated remote processing

CERN Document Server

Gupta, Rajarshi; Bera, Jitendranath

2014-01-01

The book is focused on the area of remote processing of ECG in the context of telecardiology, an emerging area in the field of Biomedical Engineering Application. Considering the poor infrastructure and inadequate numbers of physicians in rural healthcare clinics in India and other developing nations, telemedicine services assume special importance. Telecardiology, a specialized area of telemedicine, is taken up in this book considering the importance of cardiac diseases, which is prevalent in the population under discussion. The main focus of this book is to discuss different aspects of ECG acquisition, its remote transmission and computerized ECG signal analysis for feature extraction. It also discusses ECG compression and application of standalone embedded systems, to develop a cost effective solution of a telecardiology system.

Disease Classification and Biomarker Discovery Using ECG Data

Directory of Open Access Journals (Sweden)

Rong Huang

2015-01-01

Full Text Available In the recent decade, disease classification and biomarker discovery have become increasingly important in modern biological and medical research. ECGs are comparatively low-cost and noninvasive in screening and diagnosing heart diseases. With the development of personal ECG monitors, large amounts of ECGs are recorded and stored; therefore, fast and efficient algorithms are called for to analyze the data and make diagnosis. In this paper, an efficient and easy-to-interpret procedure of cardiac disease classification is developed through novel feature extraction methods and comparison of classifiers. Motivated by the observation that the distributions of various measures on ECGs of the diseased group are often skewed, heavy-tailed, or multimodal, we characterize the distributions by sample quantiles which outperform sample means. Three classifiers are compared in application both to all features and to dimension-reduced features by PCA: stepwise discriminant analysis (SDA, SVM, and LASSO logistic regression. It is found that SDA applied to dimension-reduced features by PCA is the most stable and effective procedure, with sensitivity, specificity, and accuracy being 89.68%, 84.62%, and 88.52%, respectively.

A model-based Bayesian framework for ECG beat segmentation

International Nuclear Information System (INIS)

Sayadi, O; Shamsollahi, M B

2009-01-01

The study of electrocardiogram (ECG) waveform amplitudes, timings and patterns has been the subject of intense research, for it provides a deep insight into the diagnostic features of the heart's functionality. In some recent works, a Bayesian filtering paradigm has been proposed for denoising and compression of ECG signals. In this paper, it is shown that this framework may be effectively used for ECG beat segmentation and extraction of fiducial points. Analytic expressions for the determination of points and intervals are derived and evaluated on various real ECG signals. Simulation results show that the method can contribute to and enhance the clinical ECG beat segmentation performance

Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features.

Science.gov (United States)

Tripathy, Rajesh Kumar; Dandapat, Samarendra

2017-04-01

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques.

A 58 nW ECG ASIC With Motion-Tolerant Heartbeat Timing Extraction for Wearable Cardiovascular Monitoring.

Science.gov (United States)

Da He, David; Sodini, Charles G

2015-06-01

An ASIC for wearable cardiovascular monitoring is implemented using a topology that takes advantage of the electrocardiogram's (ECG) waveform to replace the traditional ECG instrumentation amplifier, ADC, and signal processor with a single chip solution. The ASIC can extract heartbeat timings in the presence of baseline drift, muscle artifact, and signal clipping. The circuit can operate with ECGs ranging from the chest location to remote locations where the ECG magnitude is as low as 30 μV. Besides heartbeat detection, a midpoint estimation method can accurately extract the ECG R-wave timing, enabling the calculations of heart rate variability. With 58 nW of power consumption at 0.8 V supply voltage and 0.76 mm (2) of active die area in standard 0.18 μm CMOS technology, the ECG ASIC is sufficiently low power and compact to be suitable for long term and wearable cardiovascular monitoring applications under stringent battery and size constraints.

A window-based time series feature extraction method.

Science.gov (United States)

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

ECG based Atrial Fibrillation detection using Sequency Ordered Complex Hadamard Transform and Hybrid Firefly Algorithm

Directory of Open Access Journals (Sweden)

Padmavathi Kora

2017-06-01

Full Text Available Electrocardiogram (ECG, a non-invasive diagnostic technique, used for detecting cardiac arrhythmia. From last decade industry dealing with biomedical instrumentation and research, demanding an advancement in its ability to distinguish different cardiac arrhythmia. Atrial Fibrillation (AF is an irregular rhythm of the human heart. During AF, the atrial moments are quicker than the normal rate. As blood is not completely ejected out of atria, chances for the formation of blood clots in atrium. These abnormalities in the heart can be identified by the changes in the morphology of the ECG. The first step in the detection of AF is preprocessing of ECG, which removes noise using filters. Feature extraction is the next key process in this research. Recent feature extraction methods, such as Auto Regressive (AR modeling, Magnitude Squared Coherence (MSC and Wavelet Coherence (WTC using standard database (MIT-BIH, yielded a lot of features. Many of these features might be insignificant containing some redundant and non-discriminatory features that introduce computational burden and loss of performance. This paper presents fast Conjugate Symmetric Sequency Ordered Complex Hadamard Transform (CS-SCHT for extracting relevant features from the ECG signal. The sparse matrix factorization method is used for developing fast and efficient CS-SCHT algorithm and its computational performance is examined and compared to that of the HT and NCHT. The applications of the CS-SCHT in the ECG-based AF detection is also discussed. These fast CS-SCHT features are optimized using Hybrid Firefly and Particle Swarm Optimization (FFPSO to increase the performance of the classifier.

Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.

Science.gov (United States)

Ahmadieh, Hajar; Asl, Babak Mohammadzadeh

2017-04-01

We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. In environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. The results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

Science.gov (United States)

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

Individual Identification Using Linear Projection of Heartbeat Features

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Yogendra Narain Singh

2014-01-01

Full Text Available This paper presents a novel method to use the electrocardiogram (ECG signal as biometrics for individual identification. The ECG characterization is performed using an automated approach consisting of analytical and appearance methods. The analytical method extracts the fiducial features from heartbeats while the appearance method extracts the morphological features from the ECG trace. We linearly project the extracted features into a subspace of lower dimension using an orthogonal basis that represent the most significant features for distinguishing heartbeats among the subjects. Result demonstrates that the proposed characterization of the ECG signal and subsequently derived eigenbeat features are insensitive to signal variations and nonsignal artifacts. The proposed system utilizing ECG biometric method achieves the best identification rates of 85.7% for the subjects of MIT-BIH arrhythmia database and 92.49% for the healthy subjects of our IIT (BHU database. These results are significantly better than the classification accuracies of 79.55% and 84.9%, reported using support vector machine on the tested subjects of MIT-BIH arrhythmia database and our IIT (BHU database, respectively.

ECG authentication in post-exercise situation.

Science.gov (United States)

Dongsuk Sung; Jeehoon Kim; Myungjun Koh; Kwangsuk Park

2017-07-01

Human authentication based on electrocardiogram (ECG) has been a remarkable issue for recent ten years. This paper proposed an authentication technology with the ECG data recorded after the harsh exercise. 55 subjects voluntarily attended to this experiment. A stepper was used as an exercise equipment. The subjects are asked to do stepper for 5 minutes and their ECG signals are acquired before and after the exercise in rest, sitting posture. Linear discriminant analysis (LDA) was used for both feature extraction and classification. Even though, within the first 1 minute recording, the subject recognition accuracy was 59.64%, which is too low to utilize, after one minute the accuracy was higher than 90% and it increased up to 96.22% within 5 minutes, which is plausible to use in authentication circumstances. Therefore, we have concluded that ECG authentication techniques will be able to be used after 1 minute of catching breath.

Application of eigen value expansion to feature extraction from MRI images

International Nuclear Information System (INIS)

Kinosada, Yasutomi; Takeda, Kan; Nakagawa, Tsuyoshi

1991-01-01

The eigen value expansion technique was utilized for feature extraction of magnetic resonance (MR) images. The eigen value expansion is an orthonormal transformation method which decomposes a set of images into some statistically uncorrelated images. The technique was applied to MR images obtained with various imaging parameters at the same anatomical site. It generated one mean image and another set of images called bases for the images. Each basis corresponds to a feature in the images. A basis is, therefore, utilized for the feature extraction from MR images and a weighted sum of bases is also used for the feature enhancement. Furthermore, any MR image with specific feature can be obtained from a linear combination of the mean image and all of the bases. Images of hemorrhaged brain with a spin echo sequence and a series of cinematic cerebro spinal fluid flow images with ECG gated gradient refocused echo sequence were employed to estimate the ability of the feature extraction and the contrast enhancement. Results showed us that proposed application of an eigen value expansion technique to the feature extraction of MR images is good enough to clinical use and superior to other feature extraction methods such as producing a calculated MR image with a given TR and TE or the matched-filter method in processing speed and reproducibility of results. (author)

Comparative study of T-amplitude features for fitness monitoring using the ePatch® ECG recorder

DEFF Research Database (Denmark)

Thorpe, Julia Rosemary; Saida, Trine; Mehlsen, Jesper

2014-01-01

This study investigates ECG features, focusing on T-wave amplitude, from a wearable ECG device as a potential method for fitness monitoring in exercise rehabilitation. An automatic T-peak detection algorithm is presented that uses local baseline detection to overcome baseline drift without the need...

FPGA-based electrocardiography (ECG signal analysis system using least-square linear phase finite impulse response (FIR filter

Directory of Open Access Journals (Sweden)

Mohamed G. Egila

2016-12-01

Full Text Available This paper presents a proposed design for analyzing electrocardiography (ECG signals. This methodology employs highpass least-square linear phase Finite Impulse Response (FIR filtering technique to filter out the baseline wander noise embedded in the input ECG signal to the system. Discrete Wavelet Transform (DWT was utilized as a feature extraction methodology to extract the reduced feature set from the input ECG signal. The design uses back propagation neural network classifier to classify the input ECG signal. The system is implemented on Xilinx 3AN-XC3S700AN Field Programming Gate Array (FPGA board. A system simulation has been done. The design is compared with some other designs achieving total accuracy of 97.8%, and achieving reduction in utilizing resources on FPGA implementation.

Unveiling the Biometric Potential of Finger-Based ECG Signals

Science.gov (United States)

Lourenço, André; Silva, Hugo; Fred, Ana

2011-01-01

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications. PMID:21837235

Unveiling the biometric potential of finger-based ECG signals.

Science.gov (United States)

Lourenço, André; Silva, Hugo; Fred, Ana

2011-01-01

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

Deep Learning for ECG Classification

Science.gov (United States)

Pyakillya, B.; Kazachenko, N.; Mikhailovsky, N.

2017-10-01

The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations.

Science.gov (United States)

Akhbari, Mahsa; Shamsollahi, Mohammad B; Jutten, Christian; Armoundas, Antonis A; Sayadi, Omid

2016-02-01

In this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of -8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 ms and 22 ms, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 ms and 29 ms, the proposed method achieves better accuracy and smaller variability with respect to other methods.

ECG signal processing

NARCIS (Netherlands)

2009-01-01

A system extracts an ECG signal from a composite signal (308) representing an electric measurement of a living subject. Identification means (304) identify a plurality of temporal segments (309) of the composite signal corresponding to a plurality of predetermined segments (202,204,206) of an ECG

Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces.

Science.gov (United States)

Casas, Manuel M; Avitia, Roberto L; Gonzalez-Navarro, Felix F; Cardenas-Haro, Jose A; Reyna, Marco A

2018-01-01

According to the American Heart Association, in its latest commission about Ventricular Arrhythmias and Sudden Death 2006, the epidemiology of the ventricular arrhythmias ranges from a series of risk descriptors and clinical markers that go from ventricular premature complexes and nonsustained ventricular tachycardia to sudden cardiac death due to ventricular tachycardia in patients with or without clinical history. The premature ventricular complexes (PVCs) are known to be associated with malignant ventricular arrhythmias and sudden cardiac death (SCD) cases. Detecting this kind of arrhythmia has been crucial in clinical applications. The electrocardiogram (ECG) is a clinical test used to measure the heart electrical activity for inferences and diagnosis. Analyzing large ECG traces from several thousands of beats has brought the necessity to develop mathematical models that can automatically make assumptions about the heart condition. In this work, 80 different features from 108,653 ECG classified beats of the gold-standard MIT-BIH database were extracted in order to classify the Normal, PVC, and other kind of ECG beats. Three well-known Bayesian classification algorithms were trained and tested using these extracted features. Experimental results show that the F1 scores for each class were above 0.95, giving almost the perfect value for the PVC class. This gave us a promising path in the development of automated mechanisms for the detection of PVC complexes.

Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification.

Science.gov (United States)

Arvanaghi, Roghayyeh; Daneshvar, Sabalan; Seyedarabi, Hadi; Goshvarpour, Atefeh

2017-11-01

Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused. These physiological signals have been used from MINIC physioNet database. ECG and ABP signals have been fused together on the basis of the proposed Discrete Wavelet Transformation fusion technique. Then, some frequency features were extracted from the fused signal. To classify the different types of cardiac arrhythmias, these features were given to a multi-layer perceptron neural network. In this study, the best results for the proposed fusion algorithm were obtained. In this case, the accuracy rates of 96.6%, 96.9%, 95.6% and 93.9% were achieved for two, three, four and five classes, respectively. However, the maximum classification rate of 89% was obtained for two classes on the basis of ECG features. It has been found that the higher accuracy rates were acquired by using the proposed fusion technique. The results confirmed the importance of fusing features from different physiological signals to gain more accurate assessments. Copyright © 2017 Elsevier B.V. All rights reserved.

Conditional Random Fields for Morphological Analysis of Wireless ECG Signals

Science.gov (United States)

Natarajan, Annamalai; Gaiser, Edward; Angarita, Gustavo; Malison, Robert; Ganesan, Deepak; Marlin, Benjamin

2015-01-01

Thanks to advances in mobile sensing technologies, it has recently become practical to deploy wireless electrocardiograph sensors for continuous recording of ECG signals. This capability has diverse applications in the study of human health and behavior, but to realize its full potential, new computational tools are required to effectively deal with the uncertainty that results from the noisy and highly non-stationary signals collected using these devices. In this work, we present a novel approach to the problem of extracting the morphological structure of ECG signals based on the use of dynamically structured conditional random field (CRF) models. We apply this framework to the problem of extracting morphological structure from wireless ECG sensor data collected in a lab-based study of habituated cocaine users. Our results show that the proposed CRF-based approach significantly out-performs independent prediction models using the same features, as well as a widely cited open source toolkit. PMID:26726321

A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

Science.gov (United States)

Liang, Wei; Zhang, Yinlong; Tan, Jindong; Li, Yang

2014-01-01

This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient's ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN) platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen. PMID:24681668

New approach to ECG's features recognition involving neural network

International Nuclear Information System (INIS)

Babloyantz, A.; Ivanov, V.V.; Zrelov, P.V.

2001-01-01

A new approach for the detection of slight changes in the form of the ECG signal is proposed. It is based on the approximation of raw ECG data inside each RR-interval by the expansion in polynomials of special type and on the classification of samples represented by sets of expansion coefficients using a layered feed-forward neural network. The transformation applied provides significantly simpler data structure, stability to noise and to other accidental factors. A by-product of the method is the compression of ECG data with factor 5

A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

Directory of Open Access Journals (Sweden)

Wei Liang

2014-03-01

Full Text Available This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient’s ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen.

A novel application of deep learning for single-lead ECG classification.

Science.gov (United States)

Mathews, Sherin M; Kambhamettu, Chandra; Barner, Kenneth E

2018-06-04

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG. The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63%) and of supraventricular ectopic beats (95.57%) at a low sampling rate of 114 Hz. Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies. Copyright © 2018. Published by Elsevier Ltd.

A novel biometric authentication approach using ECG and EMG signals.

Science.gov (United States)

Belgacem, Noureddine; Fournier, Régis; Nait-Ali, Amine; Bereksi-Reguig, Fethi

2015-05-01

Security biometrics is a secure alternative to traditional methods of identity verification of individuals, such as authentication systems based on user name and password. Recently, it has been found that the electrocardiogram (ECG) signal formed by five successive waves (P, Q, R, S and T) is unique to each individual. In fact, better than any other biometrics' measures, it delivers proof of subject's being alive as extra information which other biometrics cannot deliver. The main purpose of this work is to present a low-cost method for online acquisition and processing of ECG signals for person authentication and to study the possibility of providing additional information and retrieve personal data from an electrocardiogram signal to yield a reliable decision. This study explores the effectiveness of a novel biometric system resulting from the fusion of information and knowledge provided by ECG and EMG (Electromyogram) physiological recordings. It is shown that biometrics based on these ECG/EMG signals offers a novel way to robustly authenticate subjects. Five ECG databases (MIT-BIH, ST-T, NSR, PTB and ECG-ID) and several ECG signals collected in-house from volunteers were exploited. A palm-based ECG biometric system was developed where the signals are collected from the palm of the subject through a minimally intrusive one-lead ECG set-up. A total of 3750 ECG beats were used in this work. Feature extraction was performed on ECG signals using Fourier descriptors (spectral coefficients). Optimum-Path Forest classifier was used to calculate the degree of similarity between individuals. The obtained results from the proposed approach look promising for individuals' authentication.

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

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Dragoş-Daniel Ţarălungă

2014-01-01

Full Text Available Interference of power line (PLI (fundamental frequency and its harmonics is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG, electroencephalograms (EEG, and electrocardiograms (ECG. When analyzing the fetal ECG (fECG recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios, based on five quantitative performance indices.

Fetal ECG extraction from abdominal signals: a review on suppression of fundamental power line interference component and its harmonics.

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Ţarălungă, Dragoş-Daniel; Ungureanu, Georgeta-Mihaela; Gussi, Ilinca; Strungaru, Rodica; Wolf, Werner

2014-01-01

Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.

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

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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.

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

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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.

WAVELET ANALYSIS OF ABNORMAL ECGS

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Vasudha Nannaparaju

2014-02-01

Full Text Available Detection of the warning signals by the heart can be diagnosed from ECG. An accurate and reliable diagnosis of ECG is very important however which is cumbersome and at times ambiguous in time domain due to the presence of noise. Study of ECG in wavelet domain using both continuous Wavelet transform (CWT and discrete Wavelet transform (DWT, with well known wavelet as well as a wavelet proposed by the authors for this investigation is found to be useful and yields fairly reliable results. In this study, Wavelet analysis of ECGs of Normal, Hypertensive, Diabetic and Cardiac are carried out. The salient feature of the study is that detection of P and T phases in wavelet domain is feasible which are otherwise feeble or absent in raw ECGs.

A multichannel nonlinear adaptive noise canceller based on generalized FLANN for fetal ECG extraction

International Nuclear Information System (INIS)

Ma, Yaping; Wei, Guo; Sun, Jinwei; Xiao, Yegui

2016-01-01

In this paper, a multichannel nonlinear adaptive noise canceller (ANC) based on the generalized functional link artificial neural network (FLANN, GFLANN) is proposed for fetal electrocardiogram (FECG) extraction. A FIR filter and a GFLANN are equipped in parallel in each reference channel to respectively approximate the linearity and nonlinearity between the maternal ECG (MECG) and the composite abdominal ECG (AECG). A fast scheme is also introduced to reduce the computational cost of the FLANN and the GFLANN. Two (2) sets of ECG time sequences, one synthetic and one real, are utilized to demonstrate the improved effectiveness of the proposed nonlinear ANC. The real dataset is derived from the Physionet non-invasive FECG database (PNIFECGDB) including 55 multichannel recordings taken from a pregnant woman. It contains two subdatasets that consist of 14 and 8 recordings, respectively, with each recording being 90 s long. Simulation results based on these two datasets reveal, on the whole, that the proposed ANC does enjoy higher capability to deal with nonlinearity between MECG and AECG as compared with previous ANCs in terms of fetal QRS (FQRS)-related statistics and morphology of the extracted FECG waveforms. In particular, for the second real subdataset, the F1-measure results produced by the PCA-based template subtraction (TS pca ) technique and six (6) single-reference channel ANCs using LMS- and RLS-based FIR filters, Volterra filter, FLANN, GFLANN, and adaptive echo state neural network (ESN a ) are 92.47%, 93.70%, 94.07%, 94.22%, 94.90%, 94.90%, and 95.46%, respectively. The same F1-measure statistical results from five (5) multi-reference channel ANCs (LMS- and RLS-based FIR filters, Volterra filter, FLANN, and GFLANN) for the second real subdataset turn out to be 94.08%, 94.29%, 94.68%, 94.91%, and 94.96%, respectively. These results indicate that the ESN a and GFLANN perform best, with the ESN a being slightly better than the GFLANN but about four times

Enhancement of low sampling frequency recordings for ECG biometric matching using interpolation.

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Sidek, Khairul Azami; Khalil, Ibrahim

2013-01-01

Electrocardiogram (ECG) based biometric matching suffers from high misclassification error with lower sampling frequency data. This situation may lead to an unreliable and vulnerable identity authentication process in high security applications. In this paper, quality enhancement techniques for ECG data with low sampling frequency has been proposed for person identification based on piecewise cubic Hermite interpolation (PCHIP) and piecewise cubic spline interpolation (SPLINE). A total of 70 ECG recordings from 4 different public ECG databases with 2 different sampling frequencies were applied for development and performance comparison purposes. An analytical method was used for feature extraction. The ECG recordings were segmented into two parts: the enrolment and recognition datasets. Three biometric matching methods, namely, Cross Correlation (CC), Percent Root-Mean-Square Deviation (PRD) and Wavelet Distance Measurement (WDM) were used for performance evaluation before and after applying interpolation techniques. Results of the experiments suggest that biometric matching with interpolated ECG data on average achieved higher matching percentage value of up to 4% for CC, 3% for PRD and 94% for WDM. These results are compared with the existing method when using ECG recordings with lower sampling frequency. Moreover, increasing the sample size from 56 to 70 subjects improves the results of the experiment by 4% for CC, 14.6% for PRD and 0.3% for WDM. Furthermore, higher classification accuracy of up to 99.1% for PCHIP and 99.2% for SPLINE with interpolated ECG data as compared of up to 97.2% without interpolation ECG data verifies the study claim that applying interpolation techniques enhances the quality of the ECG data. Crown Copyright © 2012. Published by Elsevier Ireland Ltd. All rights reserved.

Hemodynamic, ventilator, and ECG changes in pediatric patients undergoing extraction

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Y K Sanadhya

2013-01-01

Full Text Available Background: Dental treatment induces pain anxiety and fear. This study was conducted to assess the changes in hemodynamic, ventilator, and electrocardiograph changes during extraction procedure among 12-15-year-old children and compare these changes with anxiety, fear, and pain. Materials and Methods: A purposive sample of 60 patients selected based on inclusion and exclusion criteria underwent study procedure in the dental OPD of a medical college and hospital. The anxiety, fear, and pain were recorded by dental anxiety scale, dental fear scale, and visual analogue scale, respectively, before the start of the procedure. The systolic blood pressure, diastolic blood pressure, heart rate, oxygen saturation, and electrocardiogram changes were monitored during the extraction procedure. The recording was taken four times (preinjection phase, injection, extraction, and postextraction and was analyzed. Results: At the preinjection phase the mean vales were systolic blood pressure (128 ± 11.2, diastolic blood pressure (85.7 ± 6.3, heart rate (79.7 ± 9.3, and oxygen saturation (97.9 ± 5.8. These values increased in injection phases and decreased in extraction phase and the least values were found after 10 min of procedure and this relation was significant for all parameters except oxygen saturation (P = 0.48, NS. ECG abnormalities were seen among 22 patients and were significant before and after injection of Local anesthetic (P = 0.0001, S. Conclusions: Anxiety, fear, and pain have an effect on hemodynamic, ventilator, and cardiovascular parameters during the extraction procedure and hence behavioral management has to be emphasized among children in dental clinics.

Extraction of the fetal ECG in noninvasive recordings by signal decompositions

International Nuclear Information System (INIS)

Christov, I; Simova, I; Abächerli, R

2014-01-01

No signal processing technique has been able to reliably deliver an undistorted fetal electrocardiographic (fECG) signal from electrodes placed on the maternal abdomen because of the low signal-to-noise ratio of the fECG recorded from the maternal body surface. As a result, this led to increased rates of Caesarean deliveries of healthy infants. In an attempt to solve the problem, Physionet/Computing in Cardiology announced the 2013 Challenge: noninvasive fetal ECG. We are suggesting a method for cancellation of the maternal ECG consisting of: maternal QRS detection, heart rate dependant P-QRS-T interval selection, location of the fiducial points inside this interval for best matching by cross correlation, superimposition of the intervals, calculation of the mean signal of the P-QRS-T interval, and sequential subtraction of the mean signal from the whole fECG recording. Three signal decomposition methods were further applied in order to enhance the fetal QRSs (fQRS): principal component analysis, root-mean-square and Hotelling’s T-squared. A combined lead of all decompositions was synthesized and fQRS detection was performed on it. The current research differs from the Challenge in that it uses three signal decomposition methods to enhance the fECG. The new results for 97 recordings of test set B are: 305.657 for Event 4: Fetal heart rate (FHR) and 23.062 for Event 5: Fetal RR interval (FRR). (paper)

Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis

International Nuclear Information System (INIS)

Mendez, M O; Cerutti, S; Bianchi, A M; Corthout, J; Van Huffel, S; Matteucci, M; Penzel, T

2010-01-01

This study analyses two different methods to detect obstructive sleep apnea (OSA) during sleep time based only on the ECG signal. OSA is a common sleep disorder caused by repetitive occlusions of the upper airways, which produces a characteristic pattern on the ECG. ECG features, such as the heart rate variability (HRV) and the QRS peak area, contain information suitable for making a fast, non-invasive and simple screening of sleep apnea. Fifty recordings freely available on Physionet have been included in this analysis, subdivided in a training and in a testing set. We investigated the possibility of using the recently proposed method of empirical mode decomposition (EMD) for this application, comparing the results with the ones obtained through the well-established wavelet analysis (WA). By these decomposition techniques, several features have been extracted from the ECG signal and complemented with a series of standard HRV time domain measures. The best performing feature subset, selected through a sequential feature selection (SFS) method, was used as the input of linear and quadratic discriminant classifiers. In this way we were able to classify the signals on a minute-by-minute basis as apneic or nonapneic with different best-subset sizes, obtaining an accuracy up to 89% with WA and 85% with EMD. Furthermore, 100% correct discrimination of apneic patients from normal subjects was achieved independently of the feature extractor. Finally, the same procedure was repeated by pooling features from standard HRV time domain, EMD and WA together in order to investigate if the two decomposition techniques could provide complementary features. The obtained accuracy was 89%, similarly to the one achieved using only Wavelet analysis as the feature extractor; however, some complementary features in EMD and WA are evident

Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings.

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Liu, Zhongming; de Zwart, Jacco A; van Gelderen, Peter; Kuo, Li-Wei; Duyn, Jeff H

2012-02-01

We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphasis is directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac timing markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable with the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use. Published by Elsevier Inc.

Are ECG abnormalities in Noonan syndrome characteristic for the syndrome?

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Raaijmakers, R; Noordam, C; Noonan, J A; Croonen, E A; van der Burgt, C J A M; Draaisma, J M T

2008-12-01

Of all patients with Noonan syndrome, 50-90% have one or more congenital heart defects. The most frequent occurring are pulmonary stenosis (PS) and hypertrophic cardiomyopathy. The electrocardiogram (ECG) of a patient with Noonan syndrome often shows a characteristic pattern, with a left axis deviation, abnormal R/S ratio over the left precordium, and an abnormal Q wave. The objective of this study was to determine if these ECG characteristics are an independent feature of the Noonan syndrome or if they are related to the congenital heart defect. A cohort study was performed with 118 patients from two university hospitals in the United States and in The Netherlands. All patients were diagnosed with definite Noonan syndrome and had had an ECG and echocardiography. Sixty-nine patients (58%) had characteristic abnormalities of the ECG. In the patient group without a cardiac defect (n = 21), ten patients had a characteristic ECG abnormality. There was no statistical relationship between the presence of a characteristic ECG abnormality and the presence of a cardiac defect (p = 0.33). Patients with hypertrophic cardiomyopathy had more ECG abnormalities in total (p = 0.05), without correlation with a specific ECG abnormality. We conclude that the ECG features in patients with Noonan syndrome are characteristic for the syndrome and are not related to a specific cardiac defect. An ECG is very useful in the diagnosis of Noonan syndrome; every child with a Noonan phenotype should have an ECG and echocardiogram for evaluation.

Deep Arm/Ear-ECG Image Learning for Highly Wearable Biometric Human Identification.

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Zhang, Qingxue; Zhou, Dian

2018-01-01

In this study, to advance smart health applications which have increasing security/privacy requirements, we propose a novel highly wearable ECG-based user identification system, empowered by both non-standard convenient ECG lead configurations and deep learning techniques. Specifically, to achieve a super wearability, we suggest situating all the ECG electrodes on the left upper-arm, or behind the ears, and successfully obtain weak but distinguishable ECG waveforms. Afterwards, to identify individuals from weak ECG, we further present a two-stage framework, including ECG imaging and deep feature learning/identification. In the former stage, the ECG heartbeats are projected to a 2D state space, to reveal heartbeats' trajectory behaviors and produce 2D images by a split-then-hit method. In the second stage, a convolutional neural network is introduced to automatically learn the intricate patterns directly from the ECG image representations without heavy feature engineering, and then perform user identification. Experimental results on two acquired datasets using our wearable prototype, show a promising identification rate of 98.4% (single-arm-ECG) and 91.1% (ear-ECG), respectively. To the best of our knowledge, it is the first study on the feasibility of using single-arm-ECG/ear-ECG for user identification purpose, which is expected to contribute to pervasive ECG-based user identification in smart health applications.

Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs.

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Daluwatte, C; Johannesen, L; Galeotti, L; Vicente, J; Strauss, D G; Scully, C G

2016-08-01

False and non-actionable alarms in critical care can be reduced by developing algorithms which assess the trueness of an arrhythmia alarm from a bedside monitor. Computational approaches that automatically identify artefacts in ECG signals are an important branch of physiological signal processing which tries to address this issue. Signal quality indices (SQIs) derived considering differences between artefacts which occur in ECG signals and normal QRS morphology have the potential to discriminate pathologically different arrhythmic ECG segments as artefacts. Using ECG signals from the PhysioNet/Computing in Cardiology Challenge 2015 training set, we studied previously reported ECG SQIs in the scientific literature to differentiate ECG segments with artefacts from arrhythmic ECG segments. We found that the ability of SQIs to discriminate between ECG artefacts and arrhythmic ECG varies based on arrhythmia type since the pathology of each arrhythmic ECG waveform is different. Therefore, to reduce the risk of SQIs classifying arrhythmic events as noise it is important to validate and test SQIs with databases that include arrhythmias. Arrhythmia specific SQIs may also minimize the risk of misclassifying arrhythmic events as noise.

Assessment of Extraction Parameters on Antioxidant Capacity, Polyphenol Content, Epigallocatechin Gallate (EGCG, Epicatechin Gallate (ECG and Iriflophenone 3-C-β-Glucoside of Agarwood (Aquilaria crassna Young Leaves

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Pei Yin Tay

2014-08-01

Full Text Available The effects of ethanol concentration (0%–100%, v/v, solid-to-solvent ratio (1:10–1:60, w/v and extraction time (30–180 min on the extraction of polyphenols from agarwood (Aquilaria crassna were examined. Total phenolic content (TPC, total flavonoid content (TFC and total flavanol (TF assays and HPLC-DAD were used for the determination and quantification of polyphenols, flavanol gallates (epigallocatechin gallate—EGCG and epicatechin gallate—ECG and a benzophenone (iriflophenone 3-C-β-glucoside from the crude polyphenol extract (CPE of A. crassna. 2,2'-Diphenyl-1-picrylhydrazyl (DPPH radical scavenging activity was used to evaluate the antioxidant capacity of the CPE. Experimental results concluded that ethanol concentration and solid-to-solvent ratio had significant effects (p < 0.05 on the yields of polyphenol and antioxidant capacity. Extraction time had an insignificant influence on the recovery of EGCG, ECG and iriflophenone 3-C-β-glucoside, as well as radical scavenging capacity from the CPE. The extraction parameters that exhibited maximum yields were 40% (v/v ethanol, 1:60 (w/v for 30 min where the TPC, TFC, TF, DPPH, EGCG, ECG and iriflophenone 3-C-β-glucoside levels achieved were 183.5 mg GAE/g DW, 249.0 mg QE/g DW, 4.9 mg CE/g DW, 93.7%, 29.1 mg EGCG/g DW, 44.3 mg ECG/g DW and 39.9 mg iriflophenone 3-C-β-glucoside/g DW respectively. The IC50 of the CPE was 24.6 mg/L.

Wearable technology and ECG processing for fall risk assessment, prevention and detection.

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Melillo, Paolo; Castaldo, Rossana; Sannino, Giovanna; Orrico, Ada; de Pietro, Giuseppe; Pecchia, Leandro

2015-01-01

Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection. Statistical and data-mining methods are adopted to develop classification and regression models, validated with the cross-validation approach. The first classifier based on HRV features enabled to identify future fallers among hypertensive patients with an accuracy of 72% (sensitivity: 51.1%, specificity: 80.2%). The regression model to predict falls due to orthostatic dropdown from HRV recorded before standing achieved an overall accuracy of 80% (sensitivity: 92%, specificity: 90%). Finally, the classifier to detect simulated falls using ECG achieved an accuracy of 77.3% (sensitivity: 81.8%, specificity: 72.7%). The evidence from these three studies showed that ECG monitoring and processing could achieve satisfactory performances compared to other system for risk assessment, fall prevention and detection. This is interesting as differently from other technologies actually employed to prevent falls, ECG is recommended for many other pathologies of later life and is more accepted by senior citizens.

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

Science.gov (United States)

Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

2018-02-08

Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

A New Method to Detect Driver Fatigue Based on EMG and ECG Collected by Portable Non-Contact Sensors

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Lin Wang

2017-11-01

Full Text Available Recently, detection and prediction on driver fatigue have become interest of research worldwide. In the present work, a new method is built to effectively evaluate driver fatigue based on electromyography (EMG and electrocardiogram (ECG collected by portable real-time and non-contact sensors. First, under the non-disturbance condition for driver’s attention, mixed physiological signals (EMG, ECG and artefacts are collected by non-contact sensors located in a cushion on the driver’s seat. EMG and ECG are effectively separated by FastICA, and de-noised by empirical mode decomposition (EMD. Then, three physiological features, complexity of EMG, complexity of ECG, and sample entropy (SampEn of ECG, are extracted and analysed. Principal components are obtained by principal components analysis (PCA and are used as independent variables. Finally, a mathematical model of driver fatigue is built, and the accuracy of the model is up to 91%. Moreover, based on the questionnaire, the calculation results of model are consistent with real fatigue felt by the participants. Therefore, this model can effectively detect driver fatigue.

Flexible Graphene Electrodes for Prolonged Dynamic ECG Monitoring

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Cunguang Lou

2016-11-01

Full Text Available This paper describes the development of a graphene-based dry flexible electrocardiography (ECG electrode and a portable wireless ECG measurement system. First, graphene films on polyethylene terephthalate (PET substrates and graphene paper were used to construct the ECG electrode. Then, a graphene textile was synthesized for the fabrication of a wearable ECG monitoring system. The structure and the electrical properties of the graphene electrodes were evaluated using Raman spectroscopy, scanning electron microscopy (SEM, and alternating current impedance spectroscopy. ECG signals were then collected from healthy subjects using the developed graphene electrode and portable measurement system. The results show that the graphene electrode was able to acquire the typical characteristics and features of human ECG signals with a high signal-to-noise (SNR ratio in different states of motion. A week-long continuous wearability test showed no degradation in the ECG signal quality over time. The graphene-based flexible electrode demonstrates comfortability, good biocompatibility, and high electrophysiological detection sensitivity. The graphene electrode also combines the potential for use in long-term wearable dynamic cardiac activity monitoring systems with convenience and comfort for use in home health care of elderly and high-risk adults.

Evaluation of a Multichannel Non-Contact ECG System and Signal Quality Algorithms for Sleep Apnea Detection and Monitoring

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Ivan D. Castro

2018-02-01

Full Text Available Sleep-related conditions require high-cost and low-comfort diagnosis at the hospital during one night or longer. To overcome this situation, this work aims to evaluate an unobtrusive monitoring technique for sleep apnea. This paper presents, for the first time, the evaluation of contactless capacitively-coupled electrocardiography (ccECG signals for the extraction of sleep apnea features, together with a comparison of different signal quality indicators. A multichannel ccECG system is used to collect signals from 15 subjects in a sleep environment from different positions. Reference quality labels were assigned for every 30-s segment. Quality indicators were calculated, and their signal classification performance was evaluated. Features for the detection of sleep apnea were extracted from capacitive and reference signals. Sleep apnea features related to heart rate and heart rate variability achieved high similarity to the reference values, with p-values of 0.94 and 0.98, which is in line with the more than 95% beat-matching obtained. Features related to signal morphology presented lower similarity with the reference, although signal similarity metrics of correlation and coherence were relatively high. Quality-based automatic classification of the signals had a maximum accuracy of 91%. Best-performing quality indicators were based on template correlation and beat-detection. Results suggest that using unobtrusive cardiac signals for the automatic detection of sleep apnea can achieve similar performance as contact signals, and indicates clinical value of ccECG. Moreover, signal segments can automatically be classified by the proposed quality metrics as a pre-processing step. Including contactless respiration signals is likely to improve the performance and provide a complete unobtrusive cardiorespiratory monitoring solution; this is a promising alternative that will allow the screening of more patients with higher comfort, for a longer time, and at

An ECG simulator for generating maternal-foetal activity mixtures on abdominal ECG recordings

International Nuclear Information System (INIS)

Behar, Joachim; Andreotti, Fernando; Li, Qiao; Oster, Julien; Clifford, Gari D; Zaunseder, Sebastian

2014-01-01

Accurate foetal electrocardiogram (FECG) morphology extraction from non-invasive sensors remains an open problem. This is partly due to the paucity of available public databases. Even when gold standard information (i.e derived from the scalp electrode) is present, the collection of FECG can be problematic, particularly during stressful or clinically important events. In order to address this problem we have introduced an FECG simulator based on earlier work on foetal and adult ECG modelling. The open source foetal ECG synthetic simulator, fecgsyn, is able to generate maternal-foetal ECG mixtures with realistic amplitudes, morphology, beat-to-beat variability, heart rate changes and noise. Positional (rotation and translation-related) movements in the foetal and maternal heart due to respiration, foetal activity and uterine contractions were also added to the simulator. The simulator was used to generate some of the signals that were part of the 2013 PhysioNet Computing in Cardiology Challenge dataset and has been posted on Physionet.org (together with scripts to generate realistic scenarios) under an open source license. The toolbox enables further research in the field and provides part of a standard for industry and regulatory testing of rare pathological scenarios. (paper)

ECG De-noising

DEFF Research Database (Denmark)

Kærgaard, Kevin; Jensen, Søren Hjøllund; Puthusserypady, Sadasivan

2015-01-01

Electrocardiogram (ECG) is a widely used noninvasive method to study the rhythmic activity of the heart and thereby to detect the abnormalities. However, these signals are often obscured by artifacts from various sources and minimization of these artifacts are of paramount important. This paper...... proposes two adaptive techniques, namely the EEMD-BLMS (Ensemble Empirical Mode Decomposition in conjunction with the Block Least Mean Square algorithm) and DWT-NN (Discrete Wavelet Transform followed by Neural Network) methods in minimizing the artifacts from recorded ECG signals, and compares...... their performance. These methods were first compared on two types of simulated noise corrupted ECG signals: Type-I (desired ECG+noise frequencies outside the ECG frequency band) and Type-II (ECG+noise frequencies both inside and outside the ECG frequency band). Subsequently, they were tested on real ECG recordings...

Iris recognition based on key image feature extraction.

Science.gov (United States)

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

Challenges of ECG monitoring and ECG interpretation in dialysis units.

Science.gov (United States)

Poulikakos, Dimitrios; Malik, Marek

Patients on hemodialysis (HD) suffer from high cardiovascular morbidity and mortality due to high rates of coronary artery disease and arrhythmias. Electrocardiography (ECG) is often performed in the dialysis units as part of routine clinical assessment. However, fluid and electrolyte changes have been shown to affect all ECG morphologies and intervals. ECG interpretation thus depends on the time of the recording in relation to the HD session. In addition, arrhythmias during HD are common, and dialysis-related ECG artifacts mimicking arrhythmias have been reported. Studies using advanced ECG analyses have examined the impact of the HD procedure on selected repolarization descriptors and heart rate variability indices. Despite the challenges related to the impact of the fluctuant fluid and electrolyte status on conventional and advanced ECG parameters, further research in ECG monitoring during dialysis has the potential to provide clinically meaningful and practically useful information for diagnostic and risk stratification purposes. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Audio feature extraction using probability distribution function

Science.gov (United States)

Suhaib, A.; Wan, Khairunizam; Aziz, Azri A.; Hazry, D.; Razlan, Zuradzman M.; Shahriman A., B.

2015-05-01

Voice recognition has been one of the popular applications in robotic field. It is also known to be recently used for biometric and multimedia information retrieval system. This technology is attained from successive research on audio feature extraction analysis. Probability Distribution Function (PDF) is a statistical method which is usually used as one of the processes in complex feature extraction methods such as GMM and PCA. In this paper, a new method for audio feature extraction is proposed which is by using only PDF as a feature extraction method itself for speech analysis purpose. Certain pre-processing techniques are performed in prior to the proposed feature extraction method. Subsequently, the PDF result values for each frame of sampled voice signals obtained from certain numbers of individuals are plotted. From the experimental results obtained, it can be seen visually from the plotted data that each individuals' voice has comparable PDF values and shapes.

WaveformECG: A Platform for Visualizing, Annotating, and Analyzing ECG Data.

Science.gov (United States)

Winslow, Raimond L; Granite, Stephen; Jurado, Christian

2016-01-01

The electrocardiogram (ECG) is the most commonly collected data in cardiovascular research because of the ease with which it can be measured and because changes in ECG waveforms reflect underlying aspects of heart disease. Accessed through a browser, WaveformECG is an open source platform supporting interactive analysis, visualization, and annotation of ECGs.

Biometric feature extraction using local fractal auto-correlation

International Nuclear Information System (INIS)

Chen Xi; Zhang Jia-Shu

2014-01-01

Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture descriptor. Three main steps are involved in the proposed scheme: (i) using two-dimensional Gabor filter to extract the texture features of biometric images; (ii) calculating the local fractal dimension of Gabor feature under different orientations and scales using fractal auto-correlation algorithm; and (iii) linking the local fractal dimension of Gabor feature under different orientations and scales into a big vector for matching. Experiments and analyses show our proposed scheme is an efficient biometric feature extraction approach. (condensed matter: structural, mechanical, and thermal properties)

A low-power portable ECG sensor interface with dry electrodes

International Nuclear Information System (INIS)

Pu Xiaofei; Wan Lei; Zhang Hui; Qin Yajie; Hong Zhiliang

2013-01-01

This paper describes a low-power portable sensor interface dedicated to sensing and processing electrocardiogram (ECG) signals. Dry electrodes were employed in this ECG sensor, which eliminates the need of conductive gel and avoids complicated and mandatory skin preparation before electrode attachment. This ECG sensor system consists of two ICs, an analog front-end (AFE) and a successive approximation register analog-to-digital converter (SAR ADC) containing a relaxation oscillator. This proposed design was fabricated in a 0.18 μm 1P6M standard CMOS process. The AFE for extracting the biopotential signals is essential in this ECG sensor. In measurements, the AFE obtains a mid-band gain of 45 dB, a bandwidth from 0.6 to 160 Hz, and a total input referred noise of 2.8 μV rms while consuming 1 μW from the 1.8 V supply. The noise efficiency factor (NEF) of our design is 3.4. After conditioning, the amplified ECG signal is digitized by a 12-bit SAR ADC with 61.8 dB SNDR and 220 fJ/conversion-step. Finally, a complete ECG sensor interface with three dry copper electrodes is demonstrated in real-word setting, showing successful recordings of a capture ECG waveform. (semiconductor integrated circuits)

Multistage principal component analysis based method for abdominal ECG decomposition

International Nuclear Information System (INIS)

Petrolis, Robertas; Krisciukaitis, Algimantas; Gintautas, Vladas

2015-01-01

Reflection of fetal heart electrical activity is present in registered abdominal ECG signals. However this signal component has noticeably less energy than concurrent signals, especially maternal ECG. Therefore traditionally recommended independent component analysis, fails to separate these two ECG signals. Multistage principal component analysis (PCA) is proposed for step-by-step extraction of abdominal ECG signal components. Truncated representation and subsequent subtraction of cardio cycles of maternal ECG are the first steps. The energy of fetal ECG component then becomes comparable or even exceeds energy of other components in the remaining signal. Second stage PCA concentrates energy of the sought signal in one principal component assuring its maximal amplitude regardless to the orientation of the fetus in multilead recordings. Third stage PCA is performed on signal excerpts representing detected fetal heart beats in aim to perform their truncated representation reconstructing their shape for further analysis. The algorithm was tested with PhysioNet Challenge 2013 signals and signals recorded in the Department of Obstetrics and Gynecology, Lithuanian University of Health Sciences. Results of our method in PhysioNet Challenge 2013 on open data set were: average score: 341.503 bpm 2 and 32.81 ms. (paper)

Text feature extraction based on deep learning: a review.

Science.gov (United States)

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

An Adaptive Particle Weighting Strategy for ECG Denoising Using Marginalized Particle Extended Kalman Filter: An Evaluation in Arrhythmia Contexts.

Science.gov (United States)

Hesar, Hamed Danandeh; Mohebbi, Maryam

2017-11-01

Model-based Bayesian frameworks have a common problem in processing electrocardiogram (ECG) signals with sudden morphological changes. This situation often happens in the case of arrhythmias where ECGs do not obey the predefined state models. To solve this problem, in this paper, a model-based Bayesian denoising framework is proposed using marginalized particle-extended Kalman filter (MP-EKF), variational mode decomposition, and a novel fuzzy-based adaptive particle weighting strategy. This strategy helps MP-EKF to perform well even when the morphology of signal does not comply with the predefined dynamic model. In addition, this strategy adapts MP-EKF's behavior to the acquired measurements in different input signal to noise ratios (SNRs). At low input SNRs, this strategy decreases the particles' trust level to the measurements while increasing their trust level to a synthetic ECG constructed with the feature parameters of ECG dynamic model. At high input SNRs, the particles' trust level to the measurements is increased and the trust level to synthetic ECG is decreased. The proposed method was evaluated on MIT-BIH normal sinus rhythm database and compared with EKF/EKS frameworks and previously proposed MP-EKF. It was also evaluated on ECG segments extracted from MIT-BIH arrhythmia database, which contained ventricular and atrial arrhythmia. The results showed that the proposed algorithm had a noticeable superiority over benchmark methods from both SNR improvement and multiscale entropy based weighted distortion (MSEWPRD) viewpoints at low input SNRs.

ECG-Based Detection of Early Myocardial Ischemia in a Computational Model: Impact of Additional Electrodes, Optimal Placement, and a New Feature for ST Deviation.

Science.gov (United States)

Loewe, Axel; Schulze, Walther H W; Jiang, Yuan; Wilhelms, Mathias; Luik, Armin; Dössel, Olaf; Seemann, Gunnar

2015-01-01

In case of chest pain, immediate diagnosis of myocardial ischemia is required to respond with an appropriate treatment. The diagnostic capability of the electrocardiogram (ECG), however, is strongly limited for ischemic events that do not lead to ST elevation. This computational study investigates the potential of different electrode setups in detecting early ischemia at 10 minutes after onset: standard 3-channel and 12-lead ECG as well as body surface potential maps (BSPMs). Further, it was assessed if an additional ECG electrode with optimized position or the right-sided Wilson leads can improve sensitivity of the standard 12-lead ECG. To this end, a simulation study was performed for 765 different locations and sizes of ischemia in the left ventricle. Improvements by adding a single, subject specifically optimized electrode were similar to those of the BSPM: 2-11% increased detection rate depending on the desired specificity. Adding right-sided Wilson leads had negligible effect. Absence of ST deviation could not be related to specific locations of the ischemic region or its transmurality. As alternative to the ST time integral as a feature of ST deviation, the K point deviation was introduced: the baseline deviation at the minimum of the ST-segment envelope signal, which increased 12-lead detection rate by 7% for a reasonable threshold.

Estimating actigraphy from motion artifacts in ECG and respiratory effort signals.

Science.gov (United States)

Fonseca, Pedro; Aarts, Ronald M; Long, Xi; Rolink, Jérôme; Leonhardt, Steffen

2016-01-01

Recent work in unobtrusive sleep/wake classification has shown that cardiac and respiratory features can help improve classification performance. Nevertheless, actigraphy remains the single most discriminative modality for this task. Unfortunately, it requires the use of dedicated devices in addition to the sensors used to measure electrocardiogram (ECG) or respiratory effort. This paper proposes a method to estimate actigraphy from the body movement artifacts present in the ECG and respiratory inductance plethysmography (RIP) based on the time-frequency analysis of those signals. Using a continuous wavelet transform to analyze RIP, and ECG and RIP combined, it provides a surrogate measure of actigraphy with moderate correlation (for ECG+RIP, ρ = 0.74, p  <  0.001) and agreement (mean bias ratio of 0.94 and 95% agreement ratios of 0.11 and 8.45) with reference actigraphy. More important, it can be used as a replacement of actigraphy in sleep/wake classification: after cross-validation with a data set comprising polysomnographic (PSG) recordings of 15 healthy subjects and 25 insomniacs annotated by an external sleep technician, it achieves a statistically non-inferior classification performance when used together with respiratory features (average κ of 0.64 for 15 healthy subjects, and 0.50 for a dataset with 40 healthy and insomniac subjects), and when used together with respiratory and cardiac features (average κ of 0.66 for 15 healthy subjects, and 0.56 for 40 healthy and insomniac subjects). Since this method eliminates the need for a dedicated actigraphy device, it reduces the number of sensors needed for sleep/wake classification to a single sensor when using respiratory features, and to two sensors when using respiratory and cardiac features without any loss in performance. It offers a major benefit in terms of comfort for long-term home monitoring and is immediately applicable for legacy ECG and RIP monitoring devices already used in clinical

Smartphone home monitoring of ECG

Science.gov (United States)

Szu, Harold; Hsu, Charles; Moon, Gyu; Landa, Joseph; Nakajima, Hiroshi; Hata, Yutaka

2012-06-01

A system of ambulatory, halter, electrocardiography (ECG) monitoring system has already been commercially available for recording and transmitting heartbeats data by the Internet. However, it enjoys the confidence with a reservation and thus a limited market penetration, our system was targeting at aging global villagers having an increasingly biomedical wellness (BMW) homecare needs, not hospital related BMI (biomedical illness). It was designed within SWaP-C (Size, Weight, and Power, Cost) using 3 innovative modules: (i) Smart Electrode (lowpower mixed signal embedded with modern compressive sensing and nanotechnology to improve the electrodes' contact impedance); (ii) Learnable Database (in terms of adaptive wavelets transform QRST feature extraction, Sequential Query Relational database allowing home care monitoring retrievable Aided Target Recognition); (iii) Smartphone (touch screen interface, powerful computation capability, caretaker reporting with GPI, ID, and patient panic button for programmable emergence procedure). It can provide a supplementary home screening system for the post or the pre-diagnosis care at home with a build-in database searchable with the time, the place, and the degree of urgency happened, using in-situ screening.

Classification of Textures Using Filter Based Local Feature Extraction

Directory of Open Access Journals (Sweden)

Bocekci Veysel Gokhan

2016-01-01

Full Text Available In this work local features are used in feature extraction process in image processing for textures. The local binary pattern feature extraction method from textures are introduced. Filtering is also used during the feature extraction process for getting discriminative features. To show the effectiveness of the algorithm before the extraction process, three different noise are added to both train and test images. Wiener filter and median filter are used to remove the noise from images. We evaluate the performance of the method with Naïve Bayesian classifier. We conduct the comparative analysis on benchmark dataset with different filtering and size. Our experiments demonstrate that feature extraction process combine with filtering give promising results on noisy images.

Feature Extraction Using Fractal Codes

NARCIS (Netherlands)

B.A.M. Schouten (Ben); P.M. de Zeeuw (Paul)

1999-01-01

htmlabstractFast and successful searching for an object in a multimedia database is a highly desirable functionality. Several approaches to content based retrieval for multimedia databases can be found in the literature [9,10,12,14,17]. The approach we consider is feature extraction. A feature can

Characters Feature Extraction Based on Neat Oracle Bone Rubbings

OpenAIRE

Lei Guo

2013-01-01

In order to recognize characters on the neat oracle bone rubbings, a new mesh point feature extraction algorithm was put forward in this paper by researching and improving of the existing coarse mesh feature extraction algorithm and the point feature extraction algorithm. Some improvements of this algorithm were as followings: point feature was introduced into the coarse mesh feature, the absolute address was converted to relative address, and point features have been changed grid and positio...

Feature extraction using fractal codes

NARCIS (Netherlands)

B.A.M. Ben Schouten; Paul M. de Zeeuw

1999-01-01

Fast and successful searching for an object in a multimedia database is a highly desirable functionality. Several approaches to content based retrieval for multimedia databases can be found in the literature [9,10,12,14,17]. The approach we consider is feature extraction. A feature can be seen as a

ECG denoising with adaptive bionic wavelet transform.

Science.gov (United States)

Sayadi, Omid; Shamsollahi, Mohammad Bagher

2006-01-01

In this paper a new ECG denoising scheme is proposed using a novel adaptive wavelet transform, named bionic wavelet transform (BWT), which had been first developed based on a model of the active auditory system. There has been some outstanding features with the BWT such as nonlinearity, high sensitivity and frequency selectivity, concentrated energy distribution and its ability to reconstruct signal via inverse transform but the most distinguishing characteristic of BWT is that its resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential. Besides by optimizing the BWT parameters parallel to modifying a new threshold value, one can handle ECG denoising with results comparing to those of wavelet transform (WT). Preliminary tests of BWT application to ECG denoising were constructed on the signals of MIT-BIH database which showed high performance of noise reduction.

Heritability of ECG Biomarkers in the Netherlands Twin Registry Measured from Holter ECGs.

Directory of Open Access Journals (Sweden)

Emily C Hodkinson

2016-04-01

Full Text Available INTRODUCTIONThe resting ECG is the most commonly used tool to assess cardiac electrophysiology. Previous studies have estimated heritability of ECG parameters based on these snapshots of the cardiac electrical activity. In this study we set out to determine whether analysis of heart rate specific data from Holter ECGs allows more complete assessment of the heritability of ECG parameters.METHODS and RESULTSHolter ECGs were recorded from 221 twin pairs and analyzed using a multi-parameter beat binning approach. Heart rate dependent estimates of heritability for QRS duration, QT interval, Tpeak–Tend and Theight were calculated using structural equation modelling. QRS duration is largely determined by environmental factors whereas repolarization is primarily genetically determined. Heritability estimates of both QT interval and Theight were significantly higher when measured from Holter compared to resting ECGs and the heritability estimate of each was heart rate dependent. Analysis of the genetic contribution to correlation between repolarization parameters demonstrated that covariance of individual ECG parameters at different heart rates overlap but at each specific heart rate there was relatively little overlap in the genetic determinants of the different repolarization parameters.CONCLUSIONSHere we present the first study of heritability of repolarization parameters measured from Holter ECGs. Our data demonstrate that higher heritability can be estimated from the Holter than the resting ECG and reveals rate dependence in the genetic – environmental determinants of the ECG that has not previously been tractable. Future applications include deeper dissection of the ECG of participants with inherited cardiac electrical disease.

Improving ECG Services at a Children’s Hospital: Implementation of a Digital ECG System

Directory of Open Access Journals (Sweden)

Frank A. Osei

2015-01-01

Full Text Available Background. The use of digital ECG software and services is becoming common. We hypothesized that the introduction of a completely digital ECG system would increase the volume of ECGs interpreted at our children’s hospital. Methods. As part of a hospital wide quality improvement initiative, a digital ECG service (MUSE, GE was implemented at the Children’s Hospital at Montefiore in June 2012. The total volume of ECGs performed in the first 6 months of the digital ECG era was compared to 18 months of the predigital era. Predigital and postdigital data were compared via t-tests. Results. The mean ECGs interpreted per month were 53 ± 16 in the predigital era and 216 ± 37 in the postdigital era (p<0.001, a fourfold increase in ECG volume after introduction of the digital system. There was no significant change in inpatient or outpatient service volume during that time. The mean billing time decreased from 21 ± 27 days in the postdigital era to 12 ± 5 days in the postdigital era (p<0.001. Conclusion. Implementation of a digital ECG system increased the volume of ECGs officially interpreted and reported.

Feature Extraction in Radar Target Classification

Directory of Open Access Journals (Sweden)

Z. Kus

1999-09-01

Full Text Available This paper presents experimental results of extracting features in the Radar Target Classification process using the J frequency band pulse radar. The feature extraction is based on frequency analysis methods, the discrete-time Fourier Transform (DFT and Multiple Signal Characterisation (MUSIC, based on the detection of Doppler effect. The analysis has turned to the preference of DFT with implemented Hanning windowing function. We assumed to classify targets-vehicles into two classes, the wheeled vehicle and tracked vehicle. The results show that it is possible to classify them only while moving. The feature of the class results from a movement of moving parts of the vehicle. However, we have not found any feature to classify the wheeled and tracked vehicles while non-moving, although their engines are on.

Object feature extraction and recognition model

International Nuclear Information System (INIS)

Wan Min; Xiang Rujian; Wan Yongxing

2001-01-01

The characteristics of objects, especially flying objects, are analyzed, which include characteristics of spectrum, image and motion. Feature extraction is also achieved. To improve the speed of object recognition, a feature database is used to simplify the data in the source database. The feature vs. object relationship maps are stored in the feature database. An object recognition model based on the feature database is presented, and the way to achieve object recognition is also explained

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

OpenAIRE

Ţarălungă, Dragoş-Daniel; Ungureanu, Georgeta-Mihaela; Gussi, Ilinca; Strungaru, Rodica; Wolf, Werner

2014-01-01

Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the...

ECG Electrocardiogram (For Parents)

Science.gov (United States)

... Staying Safe Videos for Educators Search English Español ECG (Electrocardiogram) KidsHealth / For Parents / ECG (Electrocardiogram) Print en ... whether there is any damage. How Is an ECG Done? There is nothing painful about getting an ...

Using Intracardiac Vectorcardiographic Loop for Surface ECG Synthesis

Directory of Open Access Journals (Sweden)

G. Carrault

2008-09-01

Full Text Available Current cardiac implantable devices offer improved processing power and recording capabilities. Some of these devices already provide basic telemonitoring features that may help to reduce health care expenditure. A challenge is posed in particular for the telemonitoring of the patient's cardiac electrical activity. Indeed, only intracardiac electrograms (EGMs are acquired by the implanted device and these signals are difficult to analyze directly by clinicians. In this paper, we propose a patient-specific method to synthesize the surface electrocardiogram (ECG from a set of EGM signals, based on a 3D representation of the cardiac electrical activity and principal component analysis (PCA. The results, in the case of sinus rhythm, show a correlation coefficient between the real ECG and the synthesized ECG of about 0.85. Moreover, the application of the proposed method to the patients who present an abnormal heart rhythm exhibits promising results, especially for characterizing the bundle branch blocs. Finally, in order to evaluate the behavior of our procedure in some practical situations, the quality of the ECG reconstruction is studied as a function of the number of EGM electrodes provided by the CIDs.

Using Intracardiac Vectorcardiographic Loop for Surface ECG Synthesis

Science.gov (United States)

Kachenoura, A.; Porée, F.; Hernández, A. I.; Carrault, G.

2008-12-01

Current cardiac implantable devices offer improved processing power and recording capabilities. Some of these devices already provide basic telemonitoring features that may help to reduce health care expenditure. A challenge is posed in particular for the telemonitoring of the patient's cardiac electrical activity. Indeed, only intracardiac electrograms (EGMs) are acquired by the implanted device and these signals are difficult to analyze directly by clinicians. In this paper, we propose a patient-specific method to synthesize the surface electrocardiogram (ECG) from a set of EGM signals, based on a 3D representation of the cardiac electrical activity and principal component analysis (PCA). The results, in the case of sinus rhythm, show a correlation coefficient between the real ECG and the synthesized ECG of about 0.85. Moreover, the application of the proposed method to the patients who present an abnormal heart rhythm exhibits promising results, especially for characterizing the bundle branch blocs. Finally, in order to evaluate the behavior of our procedure in some practical situations, the quality of the ECG reconstruction is studied as a function of the number of EGM electrodes provided by the CIDs.

Advanced ECG in 2016: is there more than just a tracing?

Science.gov (United States)

Reichlin, Tobias; Abächerli, Roger; Twerenbold, Raphael; Kühne, Michael; Schaer, Beat; Müller, Christian; Sticherling, Christian; Osswald, Stefan

2016-01-01

The 12-lead electrocardiogram (ECG) is the most frequently used technology in clinical cardiology. It is critical for evidence-based management of patients with most cardiovascular conditions, including patients with acute myocardial infarction, suspected chronic cardiac ischaemia, cardiac arrhythmias, heart failure and implantable cardiac devices. In contrast to many other techniques in cardiology, the ECG is simple, small, mobile, universally available and cheap, and therefore particularly attractive. Standard ECG interpretation mainly relies on direct visual assessment. The progress in biomedical computing and signal processing, and the available computational power offer fascinating new options for ECG analysis relevant to all fields of cardiology. Several digital ECG markers and advanced ECG technologies have shown promise in preliminary studies. This article reviews promising novel surface ECG technologies in three different fields. (1) For the detection of myocardial ischaemia and infarction, QRS morphology feature analysis, the analysis of high frequency QRS components (HF-QRS) and methods using vectorcardiography as well as ECG imaging are discussed. (2) For the identification and management of patients with cardiac arrhythmias, methods of advanced P-wave analysis are discussed and the concept of ECG imaging for noninvasive localisation of cardiac arrhythmias is presented. (3) For risk stratification of sudden cardiac death and the selection of patients for medical device therapy, several novel markers including an automated QRS-score for scar quantification, the QRS-T angle or the T-wave peak-to-end-interval are discussed. Despite the existing preliminary data, none of the advanced ECG markers and technologies has yet accomplished the transition into clinical practice. Further refinement of these technologies and broader validation in large unselected patient cohorts are the critical next step needed to facilitate translation of advanced ECG technologies

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals.

Science.gov (United States)

Sahoo, Prasan Kumar; Thakkar, Hiren Kumar; Lin, Wen-Yen; Chang, Po-Cheng; Lee, Ming-Yih

2018-01-28

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

Directory of Open Access Journals (Sweden)

Prasan Kumar Sahoo

2018-01-01

Full Text Available Cardiovascular disease (CVD is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI, computerized tomography scan (CT scan, and echocardiography (Echo are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL. In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to

Real-time hypothesis driven feature extraction on parallel processing architectures

DEFF Research Database (Denmark)

Granmo, O.-C.; Jensen, Finn Verner

2002-01-01

the problem of higher-order feature-content/feature-feature correlation, causally complexly interacting features are identified through Bayesian network d-separation analysis and combined into joint features. When used on a moderately complex object-tracking case, the technique is able to select...... extraction, which selectively extract relevant features one-by-one, have in some cases achieved real-time performance on single processing element architectures. In this paperwe propose a novel technique which combines the above two approaches. Features are selectively extracted in parallelizable sets...

Uniform competency-based local feature extraction for remote sensing images

Science.gov (United States)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

The morphological classification of heartbeats as dominant and non-dominant in ECG signals

International Nuclear Information System (INIS)

Chiarugi, Franco; Emmanouilidou, Dimitra; Tsamardinos, Ioannis

2010-01-01

Surface electrocardiography (ECG) is the art of analyzing the heart's electrical activity by applying electrodes to certain positions on the body and measuring potentials at the body surface resulting from this electrical activity. Usually, significant clinical information can be obtained from analysis of the dominant beat morphology. In this respect, identification of the dominant beats and their averaging can be very helpful, allowing clinicians to carry out the measurement of amplitudes and intervals on a beat much cleaner from noise than a generic beat selected from the entire ECG recording. In this paper a standard clustering algorithm for the morphological grouping of heartbeats has been analyzed based on K-means, different signal representations, distance metrics and validity indices. The algorithm has been tested on all the records of the MIT-BIH Arrhythmia Database (MIT-BIH AD) obtaining satisfying performances in terms of averaged dominant beat estimation, but the results have not been fully satisfactory in terms of sensitivity and specificity. In order to improve the clustering accuracy, an ad hoc algorithm based on a two-phase decision tree, which integrates additional specific knowledge related to the ECG domain, has been implemented. Similarity features extracted from every beat have been used in the decision trees for the identification of different morphological classes of ECG beats. The results, in terms of dominant beat discrimination, have been evaluated on all annotated beats of the MIT-BIH AD with sensitivity = 99.05%, specificity = 93.94%, positive predictive value = 99.32% and negative predictive value = 91.69%. Further tests have shown a very slight decrement of the performances on all detected beats of the same database using an already published QRS detector, demonstrating the validity of the algorithm in real unsupervised clustering situations where annotated beat positions are not available but beats are detected with a high

Multi-purpose ECG telemetry system.

Science.gov (United States)

Marouf, Mohamed; Vukomanovic, Goran; Saranovac, Lazar; Bozic, Miroslav

2017-06-19

The Electrocardiogram ECG is one of the most important non-invasive tools for cardiac diseases diagnosis. Taking advantage of the developed telecommunication infrastructure, several approaches that address the development of telemetry cardiac devices were introduced recently. Telemetry ECG devices allow easy and fast ECG monitoring of patients with suspected cardiac issues. Choosing the right device with the desired working mode, signal quality, and the device cost are still the main obstacles to massive usage of these devices. In this paper, we introduce design, implementation, and validation of a multi-purpose telemetry system for recording, transmission, and interpretation of ECG signals in different recording modes. The system consists of an ECG device, a cloud-based analysis pipeline, and accompanied mobile applications for physicians and patients. The proposed ECG device's mechanical design allows laypersons to easily record post-event short-term ECG signals, using dry electrodes without any preparation. Moreover, patients can use the device to record long-term signals in loop and holter modes, using wet electrodes. In order to overcome the problem of signal quality fluctuation due to using different electrodes types and different placements on subject's chest, customized ECG signal processing and interpretation pipeline is presented for each working mode. We present the evaluation of the novel short-term recorder design. Recording of an ECG signal was performed for 391 patients using a standard 12-leads golden standard ECG and the proposed patient-activated short-term post-event recorder. In the validation phase, a sample of validation signals followed peer review process wherein two experts annotated the signals in terms of signal acceptability for diagnosis.We found that 96% of signals allow detecting arrhythmia and other signal's abnormal changes. Additionally, we compared and presented the correlation coefficient and the automatic QRS delineation results

Image segmentation-based robust feature extraction for color image watermarking

Science.gov (United States)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

RESEARCH ON FEATURE POINTS EXTRACTION METHOD FOR BINARY MULTISCALE AND ROTATION INVARIANT LOCAL FEATURE DESCRIPTOR

Directory of Open Access Journals (Sweden)

Hongwei Ying

2014-08-01

Full Text Available An extreme point of scale space extraction method for binary multiscale and rotation invariant local feature descriptor is studied in this paper in order to obtain a robust and fast method for local image feature descriptor. Classic local feature description algorithms often select neighborhood information of feature points which are extremes of image scale space, obtained by constructing the image pyramid using certain signal transform method. But build the image pyramid always consumes a large amount of computing and storage resources, is not conducive to the actual applications development. This paper presents a dual multiscale FAST algorithm, it does not need to build the image pyramid, but can extract feature points of scale extreme quickly. Feature points extracted by proposed method have the characteristic of multiscale and rotation Invariant and are fit to construct the local feature descriptor.

Multiple ECG Fiducial Points-Based Random Binary Sequence Generation for Securing Wireless Body Area Networks.

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Zheng, Guanglou; Fang, Gengfa; Shankaran, Rajan; Orgun, Mehmet A; Zhou, Jie; Qiao, Li; Saleem, Kashif

2017-05-01

Generating random binary sequences (BSes) is a fundamental requirement in cryptography. A BS is a sequence of N bits, and each bit has a value of 0 or 1. For securing sensors within wireless body area networks (WBANs), electrocardiogram (ECG)-based BS generation methods have been widely investigated in which interpulse intervals (IPIs) from each heartbeat cycle are processed to produce BSes. Using these IPI-based methods to generate a 128-bit BS in real time normally takes around half a minute. In order to improve the time efficiency of such methods, this paper presents an ECG multiple fiducial-points based binary sequence generation (MFBSG) algorithm. The technique of discrete wavelet transforms is employed to detect arrival time of these fiducial points, such as P, Q, R, S, and T peaks. Time intervals between them, including RR, RQ, RS, RP, and RT intervals, are then calculated based on this arrival time, and are used as ECG features to generate random BSes with low latency. According to our analysis on real ECG data, these ECG feature values exhibit the property of randomness and, thus, can be utilized to generate random BSes. Compared with the schemes that solely rely on IPIs to generate BSes, this MFBSG algorithm uses five feature values from one heart beat cycle, and can be up to five times faster than the solely IPI-based methods. So, it achieves a design goal of low latency. According to our analysis, the complexity of the algorithm is comparable to that of fast Fourier transforms. These randomly generated ECG BSes can be used as security keys for encryption or authentication in a WBAN system.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

Science.gov (United States)

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

Integrated Phoneme Subspace Method for Speech Feature Extraction

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Park Hyunsin

2009-01-01

Full Text Available Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA, independent component analysis (ICA, and linear discriminant analysis (LDA. Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.

3D Finite Element Electrical Model of Larval Zebrafish ECG Signals

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Crowcombe, James; Dhillon, Sundeep Singh; Hurst, Rhiannon Mary; Egginton, Stuart; Müller, Ferenc; Sík, Attila; Tarte, Edward

2016-01-01

Assessment of heart function in zebrafish larvae using electrocardiography (ECG) is a potentially useful tool in developing cardiac treatments and the assessment of drug therapies. In order to better understand how a measured ECG waveform is related to the structure of the heart, its position within the larva and the position of the electrodes, a 3D model of a 3 days post fertilisation (dpf) larval zebrafish was developed to simulate cardiac electrical activity and investigate the voltage distribution throughout the body. The geometry consisted of two main components; the zebrafish body was modelled as a homogeneous volume, while the heart was split into five distinct regions (sinoatrial region, atrial wall, atrioventricular band, ventricular wall and heart chambers). Similarly, the electrical model consisted of two parts with the body described by Laplace’s equation and the heart using a bidomain ionic model based upon the Fitzhugh-Nagumo equations. Each region of the heart was differentiated by action potential (AP) parameters and activation wave conduction velocities, which were fitted and scaled based on previously published experimental results. ECG measurements in vivo at different electrode recording positions were then compared to the model results. The model was able to simulate action potentials, wave propagation and all the major features (P wave, R wave, T wave) of the ECG, as well as polarity of the peaks observed at each position. This model was based upon our current understanding of the structure of the normal zebrafish larval heart. Further development would enable us to incorporate features associated with the diseased heart and hence assist in the interpretation of larval zebrafish ECGs in these conditions. PMID:27824910

Automatic detection of slow-wave sleep and REM-sleep stages using polysomnographic ECG signals

International Nuclear Information System (INIS)

Khemiri, S.; Aloui, K.; Naceur, M. S.

2011-01-01

We describe in this paper a new approach of classifying the different sleep stages only by focusing on the polysomnographic ECG signals. We show the pre-processing technique of the ECG signals. At the same time the identifcation and elimination of the different types of artifacts which contain the signal and its reconstruction are shown. The automatic classification of the slow-deep sleep and the rapid eye movement sleep called in this work REM-sleep consists in extracting physiological indicators that characterize these two sleep stages through the polysomnographic ECG signal. In other words, this classification is based on the analysis of the cardiac rhythm during a night's sleep.

Textile Concentric Ring Electrodes for ECG Recording Based on Screen-Printing Technology

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José Vicente Lidón-Roger

2018-01-01

Full Text Available Among many of the electrode designs used in electrocardiography (ECG, concentric ring electrodes (CREs are one of the most promising due to their enhanced spatial resolution. Their development has undergone a great push due to their use in recent years; however, they are not yet widely used in clinical practice. CRE implementation in textiles will lead to a low cost, flexible, comfortable, and robust electrode capable of detecting high spatial resolution ECG signals. A textile CRE set has been designed and developed using screen-printing technology. This is a mature technology in the textile industry and, therefore, does not require heavy investments. Inks employed as conductive elements have been silver and a conducting polymer (poly (3,4-ethylenedioxythiophene polystyrene sulfonate; PEDOT:PSS. Conducting polymers have biocompatibility advantages, they can be used with flexible substrates, and they are available for several printing technologies. CREs implemented with both inks have been compared by analyzing their electric features and their performance in detecting ECG signals. The results reveal that silver CREs present a higher average thickness and slightly lower skin-electrode impedance than PEDOT:PSS CREs. As for ECG recordings with subjects at rest, both CREs allowed the uptake of bipolar concentric ECG signals (BC-ECG with signal-to-noise ratios similar to that of conventional ECG recordings. Regarding the saturation and alterations of ECGs captured with textile CREs caused by intentional subject movements, silver CREs presented a more stable response (fewer saturations and alterations than those of PEDOT:PSS. Moreover, BC-ECG signals provided higher spatial resolution compared to conventional ECG. This improved spatial resolution was manifested in the identification of P1 and P2 waves of atrial activity in most of the BC-ECG signals. It can be concluded that textile silver CREs are more suitable than those of PEDOT:PSS for obtaining

Textile Concentric Ring Electrodes for ECG Recording Based on Screen-Printing Technology.

Science.gov (United States)

Lidón-Roger, José Vicente; Prats-Boluda, Gema; Ye-Lin, Yiyao; Garcia-Casado, Javier; Garcia-Breijo, Eduardo

2018-01-21

Among many of the electrode designs used in electrocardiography (ECG), concentric ring electrodes (CREs) are one of the most promising due to their enhanced spatial resolution. Their development has undergone a great push due to their use in recent years; however, they are not yet widely used in clinical practice. CRE implementation in textiles will lead to a low cost, flexible, comfortable, and robust electrode capable of detecting high spatial resolution ECG signals. A textile CRE set has been designed and developed using screen-printing technology. This is a mature technology in the textile industry and, therefore, does not require heavy investments. Inks employed as conductive elements have been silver and a conducting polymer (poly (3,4-ethylenedioxythiophene) polystyrene sulfonate; PEDOT:PSS). Conducting polymers have biocompatibility advantages, they can be used with flexible substrates, and they are available for several printing technologies. CREs implemented with both inks have been compared by analyzing their electric features and their performance in detecting ECG signals. The results reveal that silver CREs present a higher average thickness and slightly lower skin-electrode impedance than PEDOT:PSS CREs. As for ECG recordings with subjects at rest, both CREs allowed the uptake of bipolar concentric ECG signals (BC-ECG) with signal-to-noise ratios similar to that of conventional ECG recordings. Regarding the saturation and alterations of ECGs captured with textile CREs caused by intentional subject movements, silver CREs presented a more stable response (fewer saturations and alterations) than those of PEDOT:PSS. Moreover, BC-ECG signals provided higher spatial resolution compared to conventional ECG. This improved spatial resolution was manifested in the identification of P1 and P2 waves of atrial activity in most of the BC-ECG signals. It can be concluded that textile silver CREs are more suitable than those of PEDOT:PSS for obtaining BC-ECG records

Visualization of neonatal coronary arteries on multidetector row CT: ECG-gated versus non-ECG-gated technique

International Nuclear Information System (INIS)

Tsai, I.C.; Lee, Tain; Chen, Min-Chi; Fu, Yun-Ching; Jan, Sheng-Lin; Wang, Chung-Chi; Chang, Yen

2007-01-01

Multidetector CT (MDCT) seems to be a promising tool for detection of neonatal coronary arteries, but whether the ECG-gated or non-ECG-gated technique should be used has not been established. To compare the detection rate and image quality of neonatal coronary arteries on MDCT using ECG-gated and non-ECG-gated techniques. Twelve neonates with complex congenital heart disease were included. The CT scan was acquired using an ECG-gated technique, and the most quiescent phase of the RR interval was selected to represent the ECG-gated images. The raw data were then reconstructed without the ECG signal to obtain non-ECG-gated images. The detection rate and image quality of nine coronary artery segments in the two sets of images were then compared. A two-tailed paired t test was used with P values <0.05 considered as statistically significant. In all coronary segments the ECG-gated technique had a better detection rate and produced images of better quality. The difference between the two techniques ranged from 25% in the left main coronary artery to 100% in the distal right coronary artery. For neonates referred for MDCT, if evaluation of coronary artery anatomy is important for the clinical management or surgical planning, the ECG-gated technique should be used because it can reliably detect the coronary arteries. (orig.)

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

Science.gov (United States)

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

Feature extraction for classification in the data mining process

NARCIS (Netherlands)

Pechenizkiy, M.; Puuronen, S.; Tsymbal, A.

2003-01-01

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of "the curse of dimensionality". Three different eigenvector-based feature extraction approaches

A configurable and low-power mixed signal SoC for portable ECG monitoring applications.

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Kim, Hyejung; Kim, Sunyoung; Van Helleputte, Nick; Artes, Antonio; Konijnenburg, Mario; Huisken, Jos; Van Hoof, Chris; Yazicioglu, Refet Firat

2014-04-01

This paper describes a mixed-signal ECG System-on-Chip (SoC) that is capable of implementing configurable functionality with low-power consumption for portable ECG monitoring applications. A low-voltage and high performance analog front-end extracts 3-channel ECG signals and single channel electrode-tissue-impedance (ETI) measurement with high signal quality. This can be used to evaluate the quality of the ECG measurement and to filter motion artifacts. A custom digital signal processor consisting of 4-way SIMD processor provides the configurability and advanced functionality like motion artifact removal and R peak detection. A built-in 12-bit analog-to-digital converter (ADC) is capable of adaptive sampling achieving a compression ratio of up to 7, and loop buffer integration reduces the power consumption for on-chip memory access. The SoC is implemented in 0.18 μm CMOS process and consumes 32 μ W from a 1.2 V while heart beat detection application is running, and integrated in a wireless ECG monitoring system with Bluetooth protocol. Thanks to the ECG SoC, the overall system power consumption can be reduced significantly.

The acquisition and retention of ECG interpretation skills after a standardized web-based ECG tutorial

DEFF Research Database (Denmark)

Rolskov Bojsen, Signe; Räder, Sune Bernd Emil Werner; Holst, Anders Gaardsdal

2015-01-01

BACKGROUND: Electrocardiogram (ECG) interpretation is of great importance for patient management. However, medical students frequently lack proficiency in ECG interpretation and rate their ECG training as inadequate. Our aim was to examine the effect of a standalone web-based ECG tutorial...... and to assess the retention of skills using multiple follow-up intervals. METHODS: 203 medical students were included in the study. All participants completed a pre-test, an ECG tutorial, and a post-test. The participants were also randomised to complete a retention-test after short (2-4 weeks), medium (10.......6), respectively). When comparing the pre-test to retention-test delta scores, junior students had learned significantly more than senior students (junior students improved 10.7 points and senior students improved 4.7 points, p = 0.003). CONCLUSION: A standalone web-based ECG tutorial can be an effective means...

Specificity of elevated intercostal space ECG recording for the type 1 Brugada ECG pattern

DEFF Research Database (Denmark)

Holst, Anders G; Tangø, Mogens; Batchvarov, Velislav

2012-01-01

Right precordial (V1-3) elevated electrode placement ECG (EEP-ECG) is often used in the diagnosis of Brugada syndrome (BrS). However, the specificity of this has only been studied in smaller studies in Asian populations. We aimed to study this in a larger European population.......Right precordial (V1-3) elevated electrode placement ECG (EEP-ECG) is often used in the diagnosis of Brugada syndrome (BrS). However, the specificity of this has only been studied in smaller studies in Asian populations. We aimed to study this in a larger European population....

A real time ECG signal processing application for arrhythmia detection on portable devices

Science.gov (United States)

Georganis, A.; Doulgeraki, N.; Asvestas, P.

2017-11-01

Arrhythmia describes the disorders of normal heart rate, which, depending on the case, can even be fatal for a patient with severe history of heart disease. The purpose of this work is to develop an application for heart signal visualization, processing and analysis in Android portable devices e.g. Mobile phones, tablets, etc. The application is able to retrieve the signal initially from a file and at a later stage this signal is processed and analysed within the device so that it can be classified according to the features of the arrhythmia. In the processing and analysing stage, different algorithms are included among them the Moving Average and Pan Tompkins algorithm as well as the use of wavelets, in order to extract features and characteristics. At the final stage, testing is performed by simulating our application in real-time records, using the TCP network protocol for communicating the mobile with a simulated signal source. The classification of ECG beat to be processed is performed by neural networks.

Biometric security based on ECG

NARCIS (Netherlands)

Ma, L.; Groot, de J.A.; Linnartz, J.P.M.G.

2011-01-01

Recently the electrocardiogram (ECG) has been proposed as a novel biometric. This paper aims to construct a reliable ECG verification system, in terms of privacy protection. To this end, an improved expression to estimate the capacity in the autocorrelation (AC) of the ECG is derived, which not only

Multistage feature extraction for accurate face alignment

NARCIS (Netherlands)

Zuo, F.; With, de P.H.N.

2004-01-01

We propose a novel multistage facial feature extraction approach using a combination of 'global' and 'local' techniques. At the first stage, we use template matching, based on an Edge-Orientation-Map for fast feature position estimation. Using this result, a statistical framework applying the Active

Prominent feature extraction for review analysis: an empirical study

Science.gov (United States)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

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Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

Directory of Open Access Journals (Sweden)

L. Ding

2018-04-01

Full Text Available In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

Investigating the effect of traditional Persian music on ECG signals in young women using wavelet transform and neural networks.

Science.gov (United States)

Abedi, Behzad; Abbasi, Ataollah; Goshvarpour, Atefeh

2017-05-01

In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are different. In the present study, we aimed to examine the effects of listening to traditional Persian music on electrocardiogram (ECG) signals in young women. Twenty-two healthy females participated in this study. ECG signals were recorded under two conditions: rest and music. For each ECG signal, 20 morphological and wavelet-based features were selected. Artificial neural network (ANN) and probabilistic neural network (PNN) classifiers were used for the classification of ECG signals during and before listening to music. Collected data were separated into two data sets: train and test. Classification accuracies of 88% and 97% were achieved in train data sets using ANN and PNN, respectively. In addition, the test data set was employed for evaluating the classifiers, and classification rates of 84% and 93% were obtained using ANN and PNN, respectively. The present study investigated the effect of music on ECG signals based on wavelet transform and morphological features. The results obtained here can provide a good understanding on the effects of music on ECG signals to researchers.

Competency in ECG Interpretation Among Medical Students

Science.gov (United States)

Kopeć, Grzegorz; Magoń, Wojciech; Hołda, Mateusz; Podolec, Piotr

2015-01-01

Background Electrocardiogram (ECG) is commonly used in diagnosis of heart diseases, including many life-threatening disorders. We aimed to assess skills in ECG interpretation among Polish medical students and to analyze the determinants of these skills. Material/Methods Undergraduates from all Polish medical schools were asked to complete a web-based survey containing 18 ECG strips. Questions concerned primary ECG parameters (rate, rhythm, and axis), emergencies, and common ECG abnormalities. Analysis was restricted to students in their clinical years (4th–6th), and students in their preclinical years (1st–3rd) were used as controls. Results We enrolled 536 medical students (females: n=299; 55.8%), aged 19 to 31 (23±1.6) years from all Polish medical schools. Most (72%) were in their clinical years. The overall rate of good response was better in students in years 4th–5th than those in years 1st–3rd (66% vs. 56%; pECG interpretation was higher in students who reported ECG self-learning (69% vs. 62%; pECG classes (66% vs. 66%; p=0.99). On multivariable analysis (pECG interpretation. Conclusions Polish medical students in their clinical years have a good level of competency in interpreting the primary ECG parameters, but their ability to recognize ECG signs of emergencies and common heart abnormalities is low. ECG interpretation skills are determined by self-education but not by attendance at regular ECG classes. Our results indicate qualitative and quantitative deficiencies in teaching ECG interpretation at medical schools. PMID:26541993

Quality assessment of digital annotated ECG data from clinical trials by the FDA ECG Warehouse.

Science.gov (United States)

Sarapa, Nenad

2007-09-01

The FDA mandates that digital electrocardiograms (ECGs) from 'thorough' QTc trials be submitted into the ECG Warehouse in Health Level 7 extended markup language format with annotated onset and offset points of waveforms. The FDA did not disclose the exact Warehouse metrics and minimal acceptable quality standards. The author describes the Warehouse scoring algorithms and metrics used by FDA, points out ways to improve FDA review and suggests Warehouse benefits for pharmaceutical sponsors. The Warehouse ranks individual ECGs according to their score for each quality metric and produces histogram distributions with Warehouse-specific thresholds that identify ECGs of questionable quality. Automatic Warehouse algorithms assess the quality of QT annotation and duration of manual QT measurement by the central ECG laboratory.

FEATURE EXTRACTION FOR EMG BASED PROSTHESES CONTROL

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R. Aishwarya

2013-01-01

Full Text Available The control of prosthetic limb would be more effective if it is based on Surface Electromyogram (SEMG signals from remnant muscles. The analysis of SEMG signals depend on a number of factors, such as amplitude as well as time- and frequency-domain properties. Time series analysis using Auto Regressive (AR model and Mean frequency which is tolerant to white Gaussian noise are used as feature extraction techniques. EMG Histogram is used as another feature vector that was seen to give more distinct classification. The work was done with SEMG dataset obtained from the NINAPRO DATABASE, a resource for bio robotics community. Eight classes of hand movements hand open, hand close, Wrist extension, Wrist flexion, Pointing index, Ulnar deviation, Thumbs up, Thumb opposite to little finger are taken into consideration and feature vectors are extracted. The feature vectors can be given to an artificial neural network for further classification in controlling the prosthetic arm which is not dealt in this paper.

Ecg manifestations in dengue infection

International Nuclear Information System (INIS)

Tarique, S.; Murtaza, G.; Asif, S.; Qureshi, I.H.

2013-01-01

To determine the frequency of ECG changes in patients with dengue fever and dengue hemorrhagic fever. Place of study: Department of Medicine, Mayo Hospital Lahore Duration of study: September to November 201 Study design: Cross sectional analytical study Patient and methods: 116 patients with dengue infection were enrolled in the study. Their clinical presentation and examination was duly noted. Each patient had baseline and then regular monitoring of blood counts, metabolic profile and fluid status. Patients with Dengue Hemorrhagic fever underwent radiological examination in form of chest radiograph and ultrasound abdomen. ECG was carried out in all patients. Results: Out of 116 patients, 61(52.6%) suffered from Dengue Fever and 55(47.4%) had Dengue Hemorrhagic Fever. Overall 78 patients had normal ECG. Abnormal ECG findings like tachycardia, bradycardia, supraventricular tachycardia, left bundle branch block, ST depression, poor progression of R wave were noted. There was no significant relationship of ECG findings with the disease. Conclusion: ECG changes can occur in dengue infection with or without cardiac symptoms. Commonly noted findings were ST depression and bradycardia. (author)

Adaptive Fourier decomposition based R-peak detection for noisy ECG Signals.

Science.gov (United States)

Ze Wang; Chi Man Wong; Feng Wan

2017-07-01

An adaptive Fourier decomposition (AFD) based R-peak detection method is proposed for noisy ECG signals. Although lots of QRS detection methods have been proposed in literature, most detection methods require high signal quality. The proposed method extracts the R waves from the energy domain using the AFD and determines the R-peak locations based on the key decomposition parameters, achieving the denoising and the R-peak detection at the same time. Validated by clinical ECG signals in the MIT-BIH Arrhythmia Database, the proposed method shows better performance than the Pan-Tompkin (PT) algorithm in both situations of a native PT and the PT with a denoising process.

Utility of the CORD ECG Database in Evaluating ECG Interpretation by Emergency Medicine Residents

Directory of Open Access Journals (Sweden)

Wong, Hubert E

2002-10-01

Full Text Available OBJECTIVES: Electrocardiograph (ECG interpretation is a vital component of Emergency Medicine (EM resident education, but few studies have formally examined ECG teaching methods used in residency training. Recently, the Council of EM Residency Directors (CORD developed an Internet database of 395 ECGs that have been extensively peer-reviewed to incorporate all findings and abnormalities. We examined the efficacy of this database in assessing EM residents' skills in ECG interpretation. METHODS: We used the CORD ECG database to evaluate residents at our academic three-year EM residency. Thirteen residents participated, including four first-year, four second-year, and five third-year residents. Twenty ECGs were selected using 14 search criteria representing a broad range of abnormalities, including infarction, rhythm, and conduction abnormalities. Exams were scored based on all abnormalities and findings listed in the teaching points accompanying each ECG. We assigned points to each abnormal finding based on clinical relevance. RESULTS: Out of a total of 183 points in our clinically weighted scoring system, first-year residents scored an average of 99 points (54.1% [9 1- 1191, second-year residents 11 1 points (60.4% [97-1261, and third-year residents 130 points (7 1.0% [94- 1501, p = 0.12. Clinically relevant abnormalities, including anterior and inferior myocardial infarctions, were most frequently diagnosed correctly, while posterior infarction was more frequently missed. Rhythm abnormalities including ventricular and supraventricular tachycardias were most frequently diagnosed correctly, while conduction abnormalities including left bundle branch block and atrioventricular (AV block were more frequently missed. CONCLUSION: The CORD database represents a valuable resource in the assessment and teaching of ECG skills, allowing more precise identification of areas upon which instruction should be further focused or individually tailored. Our

An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis

Directory of Open Access Journals (Sweden)

Jian-Hua Zhong

2016-01-01

Full Text Available Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, contain a great deal of redundant information which extends the fault identification time and degrades the diagnostic accuracy. To improve the diagnostic performance in the GTGS, an effective fault feature extraction framework is proposed to solve the problem of the signal disorder and redundant information in the acquired signal. The proposed framework combines feature extraction with a general machine learning method, support vector machine (SVM, to implement an intelligent fault diagnosis. The feature extraction method adopts wavelet packet transform and time-domain statistical features to extract the features of faults from the vibration signal. To further reduce the redundant information in extracted features, kernel principal component analysis is applied in this study. Experimental results indicate that the proposed feature extracted technique is an effective method to extract the useful features of faults, resulting in improvement of the performance of fault diagnosis for the GTGS.

Statistical Feature Extraction and Recognition of Beverages Using Electronic Tongue

Directory of Open Access Journals (Sweden)

P. C. PANCHARIYA

2010-01-01

Full Text Available This paper describes an approach for extraction of features from data generated from an electronic tongue based on large amplitude pulse voltammetry. In this approach statistical features of the meaningful selected variables from current response signals are extracted and used for recognition of beverage samples. The proposed feature extraction approach not only reduces the computational complexity but also reduces the computation time and requirement of storage of data for the development of E-tongue for field applications. With the reduced information, a probabilistic neural network (PNN was trained for qualitative analysis of different beverages. Before the qualitative analysis of the beverages, the methodology has been tested for the basic artificial taste solutions i.e. sweet, sour, salt, bitter, and umami. The proposed procedure was compared with the more conventional and linear feature extraction technique employing principal component analysis combined with PNN. Using the extracted feature vectors, highly correct classification by PNN was achieved for eight types of juices and six types of soft drinks. The results indicated that the electronic tongue based on large amplitude pulse voltammetry with reduced feature was capable of discriminating not only basic artificial taste solutions but also the various sorts of the same type of natural beverages (fruit juices, vegetable juices, soft drinks, etc..

Evaluation of a novel portable capacitive ECG system in the clinical practice for a fast and simple ECG assessment in patients presenting with chest pain: FIDET (Fast Infarction Diagnosis ECG Trial)

OpenAIRE

Rasenack, Eva C. L.; Oehler, Martin; Els?sser, Albrecht; Schilling, Meinhard; Maier, Lars S.

2012-01-01

Background Electrocardiogram (ECG) assessment plays a crucial role in patients presenting with chest pain and suspected acute coronary syndrome (ACS). In a pilot study, we previously evaluated a capacitive ECG system (cECG) as a novel ECG technique for a fast and simple ECG assessment in patients with ST-elevation myocardial infarction (STEMI). In a next step, the sensitivity and specificity of this novel ECG technique have to be assessed in patients with ACS. Hypothesis The Fast Infarction D...

Large datasets: Segmentation, feature extraction, and compression

Energy Technology Data Exchange (ETDEWEB)

Downing, D.J.; Fedorov, V.; Lawkins, W.F.; Morris, M.D.; Ostrouchov, G.

1996-07-01

Large data sets with more than several mission multivariate observations (tens of megabytes or gigabytes of stored information) are difficult or impossible to analyze with traditional software. The amount of output which must be scanned quickly dilutes the ability of the investigator to confidently identify all the meaningful patterns and trends which may be present. The purpose of this project is to develop both a theoretical foundation and a collection of tools for automated feature extraction that can be easily customized to specific applications. Cluster analysis techniques are applied as a final step in the feature extraction process, which helps make data surveying simple and effective.

Feature Extraction and Selection Strategies for Automated Target Recognition

Science.gov (United States)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-01-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

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Jie Hu

2018-01-01

Full Text Available Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM, and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall.

Mobile Cloud-Computing-Based Healthcare Service by Noncontact ECG Monitoring

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Ee-May Fong

2013-12-01

Full Text Available Noncontact electrocardiogram (ECG measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service.

Phase information of time-frequency transforms as a key feature for classification of atrial fibrillation episodes

International Nuclear Information System (INIS)

Ortigosa, Nuria; Fernández, Carmen; Galbis, Antonio; Cano, Óscar

2015-01-01

Patients suffering from atrial fibrillation can be classified into different subtypes, according to the temporal pattern of the arrhythmia and its recurrence. Nowadays, clinicians cannot differentiate a priori between the different subtypes, and patient classification is done afterwards, when its clinical course is available. In this paper we present a comparison of classification performances when differentiating paroxysmal and persistent atrial fibrillation episodes by means of support vector machines. We analyze short surface electrocardiogram recordings by extracting modulus and phase features from several time-frequency transforms: short-time Fourier transform, Wigner–Ville, Choi–Williams, Stockwell transform, and general Fourier-family transform. Overall, accuracy higher than 81% is obtained when classifying phase information features of real test ECGs from a heterogeneous cohort of patients (in terms of progression of the arrhythmia and antiarrhythmic treatment) recorded in a tertiary center. Therefore, phase features can facilitate the clinicians’ choice of the most appropriate treatment for each patient by means of a non-invasive technique (the surface ECG). (paper)

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

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Gang Hu

2018-01-01

Full Text Available The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

A method for real-time implementation of HOG feature extraction

Science.gov (United States)

Luo, Hai-bo; Yu, Xin-rong; Liu, Hong-mei; Ding, Qing-hai

2011-08-01

Histogram of oriented gradient (HOG) is an efficient feature extraction scheme, and HOG descriptors are feature descriptors which is widely used in computer vision and image processing for the purpose of biometrics, target tracking, automatic target detection(ATD) and automatic target recognition(ATR) etc. However, computation of HOG feature extraction is unsuitable for hardware implementation since it includes complicated operations. In this paper, the optimal design method and theory frame for real-time HOG feature extraction based on FPGA were proposed. The main principle is as follows: firstly, the parallel gradient computing unit circuit based on parallel pipeline structure was designed. Secondly, the calculation of arctangent and square root operation was simplified. Finally, a histogram generator based on parallel pipeline structure was designed to calculate the histogram of each sub-region. Experimental results showed that the HOG extraction can be implemented in a pixel period by these computing units.

Comparative study of measured heart cycle phase durations: standard lead ECG versus original ascending aorta lead ECG

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Sergey V. Kolmakov

2012-11-01

Full Text Available Aims The present paper aims at evaluating the existing difference in duration measurements of the same heart cycle phases in the standard V3, V4, V5, V6 leads ECG versus original HDA lead ECG of the ascending aorta. Materials and methods The method of changing the filter pass band is used. Its essence is in artificial changing of the conditions of the signal recording carrying the informative indications of the initial information used in hemodynamic equations. The method also enables calculating the percentage deviation from the initial values. The principle of balance of the blood volume entering the heart and the blood volume leaving the heart is used to trace the minimal deviations and their respective recording conditions. Results In each of the V3, V4, V5, V6 ECG leads durations of the same phases have different values. The values measured on the ECG of the ascending aorta and those measured using the standard V4 ECG lead differ slightly. Conclusion For heart cycle phase analysis it is possible to use only the ECG of the ascending aorta and V4 standard lead ECG. Using conventional standard ECG leads causes an error up to 25%.

Feature extraction for magnetic domain images of magneto-optical recording films using gradient feature segmentation

International Nuclear Information System (INIS)

Quanqing, Zhu.; Xinsai, Wang; Xuecheng, Zou; Haihua, Li; Xiaofei, Yang

2002-01-01

In this paper, we present a method to realize feature extraction on low contrast magnetic domain images of magneto-optical recording films. The method is based on the following three steps: first, Lee-filtering method is adopted to realize pre-filtering and noise reduction; this is followed by gradient feature segmentation, which separates the object area from the background area; finally the common linking method is adopted and the characteristic parameters of magnetic domain are calculated. We describe these steps with particular emphasis on the gradient feature segmentation. The results show that this method has advantages over other traditional ones for feature extraction of low contrast images

Experimental evaluations of wearable ECG monitor.

Science.gov (United States)

Ha, Kiryong; Kim, Youngsung; Jung, Junyoung; Lee, Jeunwoo

2008-01-01

Healthcare industry is changing with ubiquitous computing environment and wearable ECG measurement is one of the most popular approaches in this healthcare industry. Reliability and performance of healthcare device is fundamental issue for widespread adoptions, and interdisciplinary perspectives of wearable ECG monitor make this more difficult. In this paper, we propose evaluation criteria considering characteristic of both ECG measurement and ubiquitous computing. With our wearable ECG monitors, various levels of experimental analysis are performed based on evaluation strategy.

Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images

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Chen Xing

2016-01-01

Full Text Available Deep learning methods have been successfully applied to learn feature representations for high-dimensional data, where the learned features are able to reveal the nonlinear properties exhibited in the data. In this paper, deep learning method is exploited for feature extraction of hyperspectral data, and the extracted features can provide good discriminability for classification task. Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning. We utilized stacked denoise autoencoder (SDAE method to pretrain the network, which is robust to noise. In the top layer of the network, logistic regression (LR approach is utilized to perform supervised fine-tuning and classification. Since sparsity of features might improve the separation capability, we utilized rectified linear unit (ReLU as activation function in SDAE to extract high level and sparse features. Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR can achieve higher accuracies than the popular support vector machine (SVM classifier.

Resting ECG findings in elite football players.

Science.gov (United States)

Bohm, Philipp; Ditzel, Roman; Ditzel, Heribert; Urhausen, Axel; Meyer, Tim

2013-01-01

The purpose of the study was to evaluate ECG abnormalities in a large sample of elite football players. Data from 566 elite male football players (57 of them of African origin) above 16 years of age were screened retrospectively (age: 20.9 ± 5.3 years; BMI: 22.9 ± 1.7 kg · m(-2), training history: 13.8 ± 4.7 years). The resting ECGs were analysed and classified according to the most current ECG categorisation of the European Society of Cardiology (ESC) (2010) and a classification of Pelliccia et al. (2000) in order to assess the impact of the new ESC-approach. According to the classification of Pelliccia, 52.5% showed mildly abnormal ECG patterns and 12% were classified as distinctly abnormal ECG patterns. According to the classification of the ESC, 33.7% showed 'uncommon ECG patterns'. Short-QT interval was the most frequent ECG pattern in this group (41.9%), followed by a shortened PR-interval (19.9%). When assessed with a QTc cut-off-point of 340 ms (instead of 360 ms), only 22.2% would have had 'uncommon ECG patterns'. Resting ECG changes amongst elite football players are common. Adjustment of the ESC criteria by adapting proposed time limits for the ECG (e.g. QTc, PR) should further reduce the rate of false-positive results.

Self-organized neural network for the quality control of 12-lead ECG signals

International Nuclear Information System (INIS)

Chen, Yun; Yang, Hui

2012-01-01

Telemedicine is very important for the timely delivery of health care to cardiovascular patients, especially those who live in the rural areas of developing countries. However, there are a number of uncertainty factors inherent to the mobile-phone-based recording of electrocardiogram (ECG) signals such as personnel with minimal training and other extraneous noises. PhysioNet organized a challenge in 2011 to develop efficient algorithms that can assess the ECG signal quality in telemedicine settings. This paper presents our efforts in this challenge to integrate multiscale recurrence analysis with a self-organizing map for controlling the ECG signal quality. As opposed to directly evaluating the 12-lead ECG, we utilize an information-preserving transform, i.e. Dower transform, to derive the 3-lead vectorcardiogram (VCG) from the 12-lead ECG in the first place. Secondly, we delineate the nonlinear and nonstationary characteristics underlying the 3-lead VCG signals into multiple time-frequency scales. Furthermore, a self-organizing map is trained, in both supervised and unsupervised ways, to identify the correlations between signal quality and multiscale recurrence features. The efficacy and robustness of this approach are validated using real-world ECG recordings available from PhysioNet. The average performance was demonstrated to be 95.25% for the training dataset and 90.0% for the independent test dataset with unknown labels. (paper)

Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data

International Nuclear Information System (INIS)

Behar, Joachim; Oster, Julien; Clifford, Gari D

2014-01-01

Despite significant advances in adult clinical electrocardiography (ECG) signal processing techniques and the power of digital processors, the analysis of non-invasive foetal ECG (NI-FECG) is still in its infancy. The Physionet/Computing in Cardiology Challenge 2013 addresses some of these limitations by making a set of FECG data publicly available to the scientific community for evaluation of signal processing techniques. The abdominal ECG signals were first preprocessed with a band-pass filter in order to remove higher frequencies and baseline wander. A notch filter to remove power interferences at 50 Hz or 60 Hz was applied if required. The signals were then normalized before applying various source separation techniques to cancel the maternal ECG. These techniques included: template subtraction, principal/independent component analysis, extended Kalman filter and a combination of a subset of these methods (FUSE method). Foetal QRS detection was performed on all residuals using a Pan and Tompkins QRS detector and the residual channel with the smoothest foetal heart rate time series was selected. The FUSE algorithm performed better than all the individual methods on the training data set. On the validation and test sets, the best Challenge scores obtained were E1 = 179.44, E2 = 20.79, E3 = 153.07, E4 = 29.62 and E5 = 4.67 for events 1–5 respectively using the FUSE method. These were the best Challenge scores for E1 and E2 and third and second best Challenge scores for E3, E4 and E5 out of the 53 international teams that entered the Challenge. The results demonstrated that existing standard approaches for foetal heart rate estimation can be improved by fusing estimators together. We provide open source code to enable benchmarking for each of the standard approaches described. (paper)

Chinese character recognition based on Gabor feature extraction and CNN

Science.gov (United States)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

'Brugada ECG' elicited by imipramine overdose

NARCIS (Netherlands)

van den Berg, M. P.; Tulleken, J. E.; Wilde, A. A. M.

2004-01-01

The ECG hallmark of the Brugada syndrome is ST-segment elevation in the right precordial leads. However, a 'Brugada ECG' may also occasionally be caused by other conditions. We report a case of a Brugada ECG due to an overdose of imipramine, a tricyclic antidepressant. The patient, a 66-year-old

e-SCP-ECG+ Protocol: An Expansion on SCP-ECG Protocol for Health Telemonitoring—Pilot Implementation

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George J. Mandellos

2010-01-01

Full Text Available Standard Communication Protocol for Computer-assisted Electrocardiography (SCP-ECG provides standardized communication among different ECG devices and medical information systems. This paper extends the use of this protocol in order to be included in health monitoring systems. It introduces new sections into SCP-ECG structure for transferring data for positioning, allergies, and five additional biosignals: noninvasive blood pressure (NiBP, body temperature (Temp, Carbon dioxide (CO2, blood oxygen saturation (SPO2, and pulse rate. It also introduces new tags in existing sections for transferring comprehensive demographic data. The proposed enhanced version is referred to as e-SCP-ECG+ protocol. This paper also considers the pilot implementation of the new protocol as a software component in a Health Telemonitoring System.

Study on a Biometric Authentication Model based on ECG using a Fuzzy Neural Network

Science.gov (United States)

Kim, Ho J.; Lim, Joon S.

2018-03-01

Traditional authentication methods use numbers or graphic passwords and thus involve the risk of loss or theft. Various studies are underway regarding biometric authentication because it uses the unique biometric data of a human being. Biometric authentication technology using ECG from biometric data involves signals that record electrical stimuli from the heart. It is difficult to manipulate and is advantageous in that it enables unrestrained measurements from sensors that are attached to the skin. This study is on biometric authentication methods using the neural network with weighted fuzzy membership functions (NEWFM). In the biometric authentication process, normalization and the ensemble average is applied during preprocessing, characteristics are extracted using Haar-wavelets, and a registration process called “training” is performed in the fuzzy neural network. In the experiment, biometric authentication was performed on 73 subjects in the Physionet Database. 10-40 ECG waveforms were tested for use in the registration process, and 15 ECG waveforms were deemed the appropriate number for registering ECG waveforms. 1 ECG waveforms were used during the authentication stage to conduct the biometric authentication test. Upon testing the proposed biometric authentication method based on 73 subjects from the Physionet Database, the TAR was 98.32% and FAR was 5.84%.

PyEEG: an open source Python module for EEG/MEG feature extraction.

Science.gov (United States)

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

Retrospectively ECG-gated multi-detector row CT of the chest: does ECG-gating improve three-dimensional visualization of the bronchial tree?

International Nuclear Information System (INIS)

Schertler, T.; Wildermuth, S.; Willmann, J.K.; Crook, D.W.; Marincek, B.; Boehm, T.

2004-01-01

Purpose: To determine the impact of retrospectively ECG-gated multi-detector row CT (MDCT) on three-dimensional (3D) visualization of the bronchial tree and virtual bronchoscopy (VB) as compared to non-ECG-gated data acquisition. Materials and Methods: Contrast-enhanced retrospectively ECG-gated and non-ECG-gated MDCT of the chest was performed in 25 consecutive patients referred for assessment of coronary artery bypass grafts and pathology of the ascending aorta. ECG-gated MDCT data were reconstructed in diastole using an absolute reverse delay of -400 msec in all patients. In 10 patients additional reconstructions at -200 msec, -300 msec, and -500 msec prior to the R-wave were performed. Shaded surface display (SSD) and virtual bronchoscopy (VB) for visualization of the bronchial segments was performed with ECG-gated and non-ECG-gated MDCT data. The visualization of the bronchial tree underwent blinded scoring. Effective radiation dose and signal-to-noise ratio (SNR) for both techniques were compared. Results: There was no significant difference in visualizing single bronchial segments using ECG-gated compared to non-ECG-gated MDCT data. However, the total sum of scores for all bronchial segments visualized with non-ECG-gated MDCT was significantly higher compared to ECG-gated MDCT (P [de

On the resolution of ECG acquisition systems for the reliable analysis of the P-wave

International Nuclear Information System (INIS)

Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Triventi, Michele; Bartolini, Pietro; Corazza, Ivan; Boriani, Giuseppe

2012-01-01

The analysis of the P-wave on surface ECG is widely used to assess the risk of atrial arrhythmias. In order to provide reliable results, the automatic analysis of the P-wave must be precise and reliable and must take into account technical aspects, one of those being the resolution of the acquisition system. The aim of this note is to investigate the effects of the amplitude resolution of ECG acquisition systems on the P-wave analysis. Starting from ECG recorded by an acquisition system with a less significant bit (LSB) of 31 nV (24 bit on an input range of 524 mVpp), we reproduced an ECG signal as acquired by systems with lower resolution (16, 15, 14, 13 and 12 bit). We found that, when the LSB is of the order of 128 µV (12 bit), a single P-wave is not recognizable on ECG. However, when averaging is applied, a P-wave template can be extracted, apparently suitable for the P-wave analysis. Results obtained in terms of P-wave duration and morphology revealed that the analysis of ECG at lowest resolutions (from 12 to 14 bit, LSB higher than 30 µV) could lead to misleading results. However, the resolution used nowadays in modern electrocardiographs (15 and 16 bit, LSB <10 µV) is sufficient for the reliable analysis of the P-wave. (note)

Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images

Directory of Open Access Journals (Sweden)

Mei Yu

2012-01-01

Full Text Available The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT scans. The algorithm includes mainly two processes: (1 distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2 representation using bag of visual words (BoW based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%.

Shape adaptive, robust iris feature extraction from noisy iris images.

Science.gov (United States)

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-10-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate.

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

Science.gov (United States)

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

[Analysis of pacemaker ECGs].

Science.gov (United States)

Israel, Carsten W; Ekosso-Ejangue, Lucy; Sheta, Mohamed-Karim

2015-09-01

The key to a successful analysis of a pacemaker electrocardiogram (ECG) is the application of the systematic approach used for any other ECG without a pacemaker: analysis of (1) basic rhythm and rate, (2) QRS axis, (3) PQ, QRS and QT intervals, (4) morphology of P waves, QRS, ST segments and T(U) waves and (5) the presence of arrhythmias. If only the most obvious abnormality of a pacemaker ECG is considered, wrong conclusions can easily be drawn. If a systematic approach is skipped it may be overlooked that e.g. atrial pacing is ineffective, the left ventricle is paced instead of the right ventricle, pacing competes with intrinsic conduction or that the atrioventricular (AV) conduction time is programmed too long. Apart from this analysis, a pacemaker ECG which is not clear should be checked for the presence of arrhythmias (e.g. atrial fibrillation, atrial flutter, junctional escape rhythm and endless loop tachycardia), pacemaker malfunction (e.g. atrial or ventricular undersensing or oversensing, atrial or ventricular loss of capture) and activity of specific pacing algorithms, such as automatic mode switching, rate adaptation, AV delay modifying algorithms, reaction to premature ventricular contractions (PVC), safety window pacing, hysteresis and noise mode. A systematic analysis of the pacemaker ECG almost always allows a probable diagnosis of arrhythmias and malfunctions to be made, which can be confirmed by pacemaker control and can often be corrected at the touch of the right button to the patient's benefit.

Transform Domain Robust Variable Step Size Griffiths' Adaptive Algorithm for Noise Cancellation in ECG

Science.gov (United States)

Hegde, Veena; Deekshit, Ravishankar; Satyanarayana, P. S.

2011-12-01

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality of ECG is utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts or noise. Noise severely limits the utility of the recorded ECG and thus needs to be removed, for better clinical evaluation. In the present paper a new noise cancellation technique is proposed for removal of random noise like muscle artifact from ECG signal. A transform domain robust variable step size Griffiths' LMS algorithm (TVGLMS) is proposed for noise cancellation. For the TVGLMS, the robust variable step size has been achieved by using the Griffiths' gradient which uses cross-correlation between the desired signal contaminated with observation or random noise and the input. The algorithm is discrete cosine transform (DCT) based and uses symmetric property of the signal to represent the signal in frequency domain with lesser number of frequency coefficients when compared to that of discrete Fourier transform (DFT). The algorithm is implemented for adaptive line enhancer (ALE) filter which extracts the ECG signal in a noisy environment using LMS filter adaptation. The proposed algorithm is found to have better convergence error/misadjustment when compared to that of ordinary transform domain LMS (TLMS) algorithm, both in the presence of white/colored observation noise. The reduction in convergence error achieved by the new algorithm with desired signal decomposition is found to be lower than that obtained without decomposition. The experimental results indicate that the proposed method is better than traditional adaptive filter using LMS algorithm in the aspects of retaining geometrical characteristics of ECG signal.

Airborne LIDAR and high resolution satellite data for rapid 3D feature extraction

Science.gov (United States)

Jawak, S. D.; Panditrao, S. N.; Luis, A. J.

2014-11-01

This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary *.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM

Multi detector computed tomography (MDCT) of the aortic root; ECG-gated verses non-ECG-gated examinations

International Nuclear Information System (INIS)

Kristiansen, Joanna; Guenther, Anne; Aalokken, Trond Mogens; Andersen, Rune

2011-01-01

Purpose: Motion artifacts may degrade a conventional CT examination of the ascending aorta and hinder accurate diagnosis. We quantitatively compared retrospectively electrocardiographic (ECG) -gated multi detector computed tomography (MDCT) with non-ECG-gated MDCT in order to demonstrate whether or not one of the methods should be preferred. Method: The study included seventeen patients with surgically reconstructed aortic root and reimplanted coronary arteries. All patients had undergone both non-gated MDCT and retrospectively ECG-gated MDCT employing a stringently modulated tube current with single phase image reconstruction. The incidence of motion artifacts in the left main coronary artery (LM), proximal right coronary artery (RCA), and aortic root and ascending aorta were rated using a four point scale. The effective dose for each scan was calculated and normalized to a 15 cm scan length. Statistical analysis of motion artifacts and radiation dose was performed using Wilcoxon matched pairs signed rank sum test. Results: A significant reduction in motion artifacts was found in all three vessels in images from the retrospectively ECG-gated scans (LM: P = 0.005, RCA: P = 0.015, aorta: P = 0.003). The mean normalized effective radiation dose was 3.69 mSv (±1.03) for the non-ECG-gated scans and 16.37 mSv (±2.53) for the ECG-gated scans. Conclusion: Retrospective ECG-gating with single phase reconstruction significantly reduces the incidence of motion artifacts in the aortic root and the proximal portion of the coronary arteries but at the expense of a fourfold increase in radiation dose.

Optimized Feature Extraction for Temperature-Modulated Gas Sensors

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Alexander Vergara

2009-01-01

Full Text Available One of the most serious limitations to the practical utilization of solid-state gas sensors is the drift of their signal. Even if drift is rooted in the chemical and physical processes occurring in the sensor, improved signal processing is generally considered as a methodology to increase sensors stability. Several studies evidenced the augmented stability of time variable signals elicited by the modulation of either the gas concentration or the operating temperature. Furthermore, when time-variable signals are used, the extraction of features can be accomplished in shorter time with respect to the time necessary to calculate the usual features defined in steady-state conditions. In this paper, we discuss the stability properties of distinct dynamic features using an array of metal oxide semiconductors gas sensors whose working temperature is modulated with optimized multisinusoidal signals. Experiments were aimed at measuring the dispersion of sensors features in repeated sequences of a limited number of experimental conditions. Results evidenced that the features extracted during the temperature modulation reduce the multidimensional data dispersion among repeated measurements. In particular, the Energy Signal Vector provided an almost constant classification rate along the time with respect to the temperature modulation.

Multi-scale salient feature extraction on mesh models

KAUST Repository

Yang, Yongliang; Shen, ChaoHui

2012-01-01

We present a new method of extracting multi-scale salient features on meshes. It is based on robust estimation of curvature on multiple scales. The coincidence between salient feature and the scale of interest can be established straightforwardly, where detailed feature appears on small scale and feature with more global shape information shows up on large scale. We demonstrate this multi-scale description of features accords with human perception and can be further used for several applications as feature classification and viewpoint selection. Experiments exhibit that our method as a multi-scale analysis tool is very helpful for studying 3D shapes. © 2012 Springer-Verlag.

Compact and Hybrid Feature Description for Building Extraction

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Li, Z.; Liu, Y.; Hu, Y.; Li, P.; Ding, Y.

2017-05-01

Building extraction in aerial orthophotos is crucial for various applications. Currently, deep learning has been shown to be successful in addressing building extraction with high accuracy and high robustness. However, quite a large number of samples is required in training a classifier when using deep learning model. In order to realize accurate and semi-interactive labelling, the performance of feature description is crucial, as it has significant effect on the accuracy of classification. In this paper, we bring forward a compact and hybrid feature description method, in order to guarantees desirable classification accuracy of the corners on the building roof contours. The proposed descriptor is a hybrid description of an image patch constructed from 4 sets of binary intensity tests. Experiments show that benefiting from binary description and making full use of color channels, this descriptor is not only computationally frugal, but also accurate than SURF for building extraction.

A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

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Khorshidtalab, Aida; Mesbah, Mostefa; Salami, Momoh J. E.

2015-01-01

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$T^{2}$ \\end{document} statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%. PMID:27170898

Analysis of Feature Extraction Methods for Speaker Dependent Speech Recognition

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Gurpreet Kaur

2017-02-01

Full Text Available Speech recognition is about what is being said, irrespective of who is saying. Speech recognition is a growing field. Major progress is taking place on the technology of automatic speech recognition (ASR. Still, there are lots of barriers in this field in terms of recognition rate, background noise, speaker variability, speaking rate, accent etc. Speech recognition rate mainly depends on the selection of features and feature extraction methods. This paper outlines the feature extraction techniques for speaker dependent speech recognition for isolated words. A brief survey of different feature extraction techniques like Mel-Frequency Cepstral Coefficients (MFCC, Linear Predictive Coding Coefficients (LPCC, Perceptual Linear Prediction (PLP, Relative Spectra Perceptual linear Predictive (RASTA-PLP analysis are presented and evaluation is done. Speech recognition has various applications from daily use to commercial use. We have made a speaker dependent system and this system can be useful in many areas like controlling a patient vehicle using simple commands.

A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis

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Yao, Hao-Dong; Ma, Jia-Li; Fu, Bin-Bin; Wang, Hai-Yang; Dong, Ming-Chui

2015-07-01

Auscultation of heart sound (HS) signals serves as an important primary approach to diagnose cardiovascular diseases (CVDs) for centuries. Confronting the intrinsic drawbacks of traditional HS auscultation, computer-aided automatic HS auscultation based on feature extraction technique has witnessed explosive development. Yet, most existing HS feature extraction methods adopt acoustic or time-frequency features which exhibit poor relationship with diagnostic information, thus restricting the performance of further interpretation and analysis. Tackling such a bottleneck problem, this paper innovatively proposes a novel murmur-based HS feature extraction method since murmurs contain massive pathological information and are regarded as the first indications of pathological occurrences of heart valves. Adapting discrete wavelet transform (DWT) and Shannon envelope, the envelope-morphological characteristics of murmurs are obtained and three features are extracted accordingly. Validated by discriminating normal HS and 5 various abnormal HS signals with extracted features, the proposed method provides an attractive candidate in automatic HS auscultation.

A 1.0 V 78 mircoW reconfigurable ASIC embedded in an intelligent electrode for continuous remote ECG applications.

Science.gov (United States)

Yang, Geng; Chen, Jian; Jonsson, Fredrik; Tenhunen, Hannu; Zheng, Li-Rong

2009-01-01

In this paper, a reconfigurable, low-power Application Specific Integrated Circuit (ASIC) that extracts and transmits electrocardiograph (ECG) signals is presented. An Intelligent Electrode is introduced which consists of the proposed ASIC and a micro spike array, permitting onsite ECG signal acquisition, processing and transmission. Fabricated in a standard 0.18 microm CMOS process, the ASIC consumes 78 microW with 1.0 V core voltage at 6 MHz operating frequency and only occupies 2.25 mm(2). The tiny silicon size makes it possible and suitable to embed the proposed ASIC into an Intelligent Electrode, and the low power consumption makes it feasible for long term continuous ECG monitoring.

Opinion mining feature-level using Naive Bayes and feature extraction based analysis dependencies

Science.gov (United States)

Sanda, Regi; Baizal, Z. K. Abdurahman; Nhita, Fhira

2015-12-01

Development of internet and technology, has major impact and providing new business called e-commerce. Many e-commerce sites that provide convenience in transaction, and consumers can also provide reviews or opinions on products that purchased. These opinions can be used by consumers and producers. Consumers to know the advantages and disadvantages of particular feature of the product. Procuders can analyse own strengths and weaknesses as well as it's competitors products. Many opinions need a method that the reader can know the point of whole opinion. The idea emerged from review summarization that summarizes the overall opinion based on sentiment and features contain. In this study, the domain that become the main focus is about the digital camera. This research consisted of four steps 1) giving the knowledge to the system to recognize the semantic orientation of an opinion 2) indentify the features of product 3) indentify whether the opinion gives a positive or negative 4) summarizing the result. In this research discussed the methods such as Naï;ve Bayes for sentiment classification, and feature extraction algorithm based on Dependencies Analysis, which is one of the tools in Natural Language Processing (NLP) and knowledge based dictionary which is useful for handling implicit features. The end result of research is a summary that contains a bunch of reviews from consumers on the features and sentiment. With proposed method, accuration for sentiment classification giving 81.2 % for positive test data, 80.2 % for negative test data, and accuration for feature extraction reach 90.3 %.

Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

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Yi An

2013-01-01

Full Text Available Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from [0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

Nonredundant sparse feature extraction using autoencoders with receptive fields clustering.

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Ayinde, Babajide O; Zurada, Jacek M

2017-09-01

This paper proposes new techniques for data representation in the context of deep learning using agglomerative clustering. Existing autoencoder-based data representation techniques tend to produce a number of encoding and decoding receptive fields of layered autoencoders that are duplicative, thereby leading to extraction of similar features, thus resulting in filtering redundancy. We propose a way to address this problem and show that such redundancy can be eliminated. This yields smaller networks and produces unique receptive fields that extract distinct features. It is also shown that autoencoders with nonnegativity constraints on weights are capable of extracting fewer redundant features than conventional sparse autoencoders. The concept is illustrated using conventional sparse autoencoder and nonnegativity-constrained autoencoders with MNIST digits recognition, NORB normalized-uniform object data and Yale face dataset. Copyright © 2017 Elsevier Ltd. All rights reserved.

Annotation-based feature extraction from sets of SBML models.

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Alm, Rebekka; Waltemath, Dagmar; Wolfien, Markus; Wolkenhauer, Olaf; Henkel, Ron

2015-01-01

Model repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models. In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate. Annotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.

Facial Feature Extraction Using Frequency Map Series in PCNN

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Rencan Nie

2016-01-01

Full Text Available Pulse coupled neural network (PCNN has been widely used in image processing. The 3D binary map series (BMS generated by PCNN effectively describes image feature information such as edges and regional distribution, so BMS can be treated as the basis of extracting 1D oscillation time series (OTS for an image. However, the traditional methods using BMS did not consider the correlation of the binary sequence in BMS and the space structure for every map. By further processing for BMS, a novel facial feature extraction method is proposed. Firstly, consider the correlation among maps in BMS; a method is put forward to transform BMS into frequency map series (FMS, and the method lessens the influence of noncontinuous feature regions in binary images on OTS-BMS. Then, by computing the 2D entropy for every map in FMS, the 3D FMS is transformed into 1D OTS (OTS-FMS, which has good geometry invariance for the facial image, and contains the space structure information of the image. Finally, by analyzing the OTS-FMS, the standard Euclidean distance is used to measure the distances for OTS-FMS. Experimental results verify the effectiveness of OTS-FMS in facial recognition, and it shows better recognition performance than other feature extraction methods.

Automated Feature Extraction from Hyperspectral Imagery, Phase II

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National Aeronautics and Space Administration — The proposed activities will result in the development of a novel hyperspectral feature-extraction toolkit that will provide a simple, automated, and accurate...

Coding visual features extracted from video sequences.

Science.gov (United States)

Baroffio, Luca; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2014-05-01

Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.

New methodologies for measuring Brugada ECG patterns cannot differentiate the ECG pattern of Brugada syndrome from Brugada phenocopy.

Science.gov (United States)

Gottschalk, Byron H; Garcia-Niebla, Javier; Anselm, Daniel D; Jaidka, Atul; De Luna, Antoni Bayés; Baranchuk, Adrian

2016-01-01

Brugada phenocopies (BrP) are clinical entities characterized by ECG patterns that are identical to true Brugada syndrome (BrS), but are elicited by various clinical circumstances. A recent study demonstrated that the patterns of BrP and BrS are indistinguishable under the naked eye, thereby validating the concept that the patterns are identical. The aim of our study was to determine whether recently developed ECG criteria would allow for discrimination between type-2 BrS ECG pattern and type-2 BrP ECG pattern. Ten ECGs from confirmed BrS (aborted sudden death, transformation into type 1 upon sodium channel blocking test and/or ventricular arrhythmias, positive genetics) cases and 9 ECGs from confirmed BrP were included in the study. Surface 12-lead ECGs were scanned, saved in JPEG format for blind measurement of two values: (i) β-angle; and (ii) the base of the triangle. Cut-off values of ≥58° for the β-angle and ≥4mm for the base of the triangle were used to determine the BrS ECG pattern. Mean values for the β-angle in leads V1 and V2 were 66.7±25.5 and 55.4±28.1 for BrS and 54.1±26.5 and 43.1±16.1 for BrP respectively (p=NS). Mean values for the base of the triangle in V1 and V2 were 7.5±3.9 and 5.7±3.9 for BrS and 5.6±3.2 and 4.7±2.7 for BrP respectively (p=NS). The β-angle had a sensitivity of 60%, specificity of 78% (LR+ 2.7, LR- 0.5). The base of the triangle had a sensitivity of 80%, specificity of 40% (LR+ 1.4, LR- 0.5). New ECG criteria presented relatively low sensitivity and specificity, positive and negative predictive values to discriminate between BrS and BrP ECG patterns, providing further evidence that the two patterns are identical. Copyright © 2016 Elsevier Inc. All rights reserved.

Correlation of resting ECG, stress ECG and thallium scan in the evaluation of coronary artery disease

International Nuclear Information System (INIS)

Khan, A.; Amin, W.; Khan, M.Z.A.; Ahmed, A.; Kiani, M.R.

1987-01-01

This study includes 70 cases who underwent myocardial perfusion studies with thallium 201 during the year 1984-85. They were studied clinically, had their resting ECGs, stress ECGs and coronary angiograms. Majority of these patients were males, their ages ranged between 34-70 years. The patients population included with typical/atypical chest pain, some with resting ECG abnormalities, after coronary angiography and a few after coronary artery bypass graft surgery. The result of all the modalities were compared with the conventional gold standard for ischaemic heart disease, i.e. coronary angiogram. It is concluded that the sensitivity of resting ECG in the diagnosis of ischaemic heart disease is very low. The exercise test alone was found conclusive in about 74% of patients while sensitivity of thallium scan was 66% in this particular group of patients. (author)

Extraction and representation of common feature from uncertain facial expressions with cloud model.

Science.gov (United States)

Wang, Shuliang; Chi, Hehua; Yuan, Hanning; Geng, Jing

2017-12-01

Human facial expressions are key ingredient to convert an individual's innate emotion in communication. However, the variation of facial expressions affects the reliable identification of human emotions. In this paper, we present a cloud model to extract facial features for representing human emotion. First, the uncertainties in facial expression are analyzed in the context of cloud model. The feature extraction and representation algorithm is established under cloud generators. With forward cloud generator, facial expression images can be re-generated as many as we like for visually representing the extracted three features, and each feature shows different roles. The effectiveness of the computing model is tested on Japanese Female Facial Expression database. Three common features are extracted from seven facial expression images. Finally, the paper is concluded and remarked.

Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

Directory of Open Access Journals (Sweden)

Xiaojun Lu

2017-01-01

Full Text Available This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN. First, with Clarifai net and VGG Net-D (16 layers, we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW.

Fixed kernel regression for voltammogram feature extraction

International Nuclear Information System (INIS)

Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N

2009-01-01

Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

Science.gov (United States)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Dominant color and texture feature extraction for banknote discrimination

Science.gov (United States)

Wang, Junmin; Fan, Yangyu; Li, Ning

2017-07-01

Banknote discrimination with image recognition technology is significant in many applications. The traditional methods based on image recognition only recognize the banknote denomination without discriminating the counterfeit banknote. To solve this problem, we propose a systematical banknote discrimination approach with the dominant color and texture features. After capturing the visible and infrared images of the test banknote, we first implement the tilt correction based on the principal component analysis (PCA) algorithm. Second, we extract the dominant color feature of the visible banknote image to recognize the denomination. Third, we propose an adaptively weighted local binary pattern with "delta" tolerance algorithm to extract the texture features of the infrared banknote image. At last, we discriminate the genuine or counterfeit banknote by comparing the texture features between the test banknote and the benchmark banknote. The proposed approach is tested using 14,000 banknotes of six different denominations from Chinese yuan (CNY). The experimental results show 100% accuracy for denomination recognition and 99.92% accuracy for counterfeit banknote discrimination.

Feature extraction from multiple data sources using genetic programming.

Energy Technology Data Exchange (ETDEWEB)

Szymanski, J. J. (John J.); Brumby, Steven P.; Pope, P. A. (Paul A.); Eads, D. R. (Damian R.); Galassi, M. C. (Mark C.); Harvey, N. R. (Neal R.); Perkins, S. J. (Simon J.); Porter, R. B. (Reid B.); Theiler, J. P. (James P.); Young, A. C. (Aaron Cody); Bloch, J. J. (Jeffrey J.); David, N. A. (Nancy A.); Esch-Mosher, D. M. (Diana M.)

2002-01-01

Feature extration from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. The tool used is the GENetic Imagery Exploitation (GENIE) software, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land-cover features including towns, grasslands, wild fire burn scars, and several types of forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal

International Nuclear Information System (INIS)

Mohebbi, Maryam; Ghassemian, Hassan

2011-01-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal. This method consists of three steps: preprocessing, feature extraction and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the RR-interval signal is extracted. In the next step, the recurrence plot (RP) of the RR-interval signal is obtained and five statistically significant features are extracted to characterize the basic patterns of the RP. These features consist of the recurrence rate, length of longest diagonal segments (L max  ), average length of the diagonal lines (L mean ), entropy, and trapping time. Recurrence quantification analysis can reveal subtle aspects of dynamics not easily appreciated by other methods and exhibits characteristic patterns which are caused by the typical dynamical behavior. In the final step, a support vector machine (SVM)-based classifier is used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30 min ECG recordings that end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, positive predictivity and negative predictivity were 97%, 100%, 100%, and 96%, respectively. The proposed methodology presents better results than other existing approaches

Variable threshold method for ECG R-peak detection.

Science.gov (United States)

Kew, Hsein-Ping; Jeong, Do-Un

2011-10-01

In this paper, a wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenient in wearing. ECG signal is detected using a potential instrument system. The measured ECG signal is transmits via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. ECG signals carry a lot of clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed. There will be errors in peak detection when the baseline changes due to motion artifacts and signal size changes. Preprocessing process which includes differentiation process and Hilbert transform is used as signal preprocessing algorithm. Thereafter, variable threshold method is used to detect the R-peak which is more accurate and efficient than fixed threshold value method. R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research in order to evaluate the performance analysis.

Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

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Xi Wenfei

2017-07-01

Full Text Available Feature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV, this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.

Common ECG Lead Placement Errors. Part I: Limb Lead Reversals

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Allison V. Rosen

2015-10-01

Full Text Available Background: Electrocardiography (ECG is a very useful diagnostic tool. However, errors in placement of ECG leads can create artifacts, mimic pathologies, and hinder proper ECG interpretation. It is important for members of the health care team to be able to recognize the common patterns resulting from lead placement errors. Methods: 12-lead ECGs were recorded in a single male healthy subject in his mid 20s. Six different limb lead reversals were compared to ECG recordings from correct lead placement. Results: Classic ECG patterns were observed when leads were reversed. Methods of discriminating these ECG patterns from true pathologic findings were described. Conclusion: Correct recording and interpretation of ECGs is key to providing optimal patient care. It is therefore crucial to be able to recognize common ECG patterns that are indicative of lead reversals.

Feature extraction for dynamic integration of classifiers

NARCIS (Netherlands)

Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.; Patterson, D.W.

2007-01-01

Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique

Feature extraction with deep neural networks by a generalized discriminant analysis.

Science.gov (United States)

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

Directory of Open Access Journals (Sweden)

A. Kianisarkaleh

2015-12-01

Full Text Available Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.

DCTNet and PCANet for acoustic signal feature extraction

OpenAIRE

Xian, Yin; Thompson, Andrew; Sun, Xiaobai; Nowacek, Douglas; Nolte, Loren

2016-01-01

We introduce the use of DCTNet, an efficient approximation and alternative to PCANet, for acoustic signal classification. In PCANet, the eigenfunctions of the local sample covariance matrix (PCA) are used as filterbanks for convolution and feature extraction. When the eigenfunctions are well approximated by the Discrete Cosine Transform (DCT) functions, each layer of of PCANet and DCTNet is essentially a time-frequency representation. We relate DCTNet to spectral feature representation method...

ECG-gating in non-cardiac digital subtraction angiography

International Nuclear Information System (INIS)

Gattoni, F.; Baldini, V.; Cairo, F.

1987-01-01

This paper reports the results of the ECG-gating in non-cardiac digital subtraction angiography (DSA). One hundred and fifteen patients underwent DSA (126 examinations); ECG-gating was applied in 66/126 examinations: images recorded at 70% of R wave were subtracted. Artifacts produced by vascular movements were evaluated in all patients: only 40 examinations, carried out whithout ECG-gating, showed vascular artifacts. The major advantage of the ECG-gated DSA is the more efficent subtraction because of the better images superimposition: therefore, ECG-gating can be clinically helpful. On the contrary, it could be a problem in arrhytmic or bradycardic patients. ECG-gating is helpful in DSA imaging of the thoracic and abdominal aorta and of the cervical and renal arteries. In the examinations of peripheral vessels of the limbs it is not so efficent as in the trunk or in the neck

Automatic feature extraction in large fusion databases by using deep learning approach

Energy Technology Data Exchange (ETDEWEB)

Farias, Gonzalo, E-mail: gonzalo.farias@ucv.cl [Pontificia Universidad Católica de Valparaíso, Valparaíso (Chile); Dormido-Canto, Sebastián [Departamento de Informática y Automática, UNED, Madrid (Spain); Vega, Jesús; Rattá, Giuseppe [Asociación EURATOM/CIEMAT Para Fusión, CIEMAT, Madrid (Spain); Vargas, Héctor; Hermosilla, Gabriel; Alfaro, Luis; Valencia, Agustín [Pontificia Universidad Católica de Valparaíso, Valparaíso (Chile)

2016-11-15

Highlights: • Feature extraction is a very critical stage in any machine learning algorithm. • The problem dimensionality can be reduced enormously when selecting suitable attributes. • Despite the importance of feature extraction, the process is commonly done manually by trial and error. • Fortunately, recent advances in deep learning approach have proposed an encouraging way to find a good feature representation automatically. • In this article, deep learning is applied to the TJ-II fusion database to get more robust and accurate classifiers in comparison to previous work. - Abstract: Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one.

Automatic feature extraction in large fusion databases by using deep learning approach

International Nuclear Information System (INIS)

Farias, Gonzalo; Dormido-Canto, Sebastián; Vega, Jesús; Rattá, Giuseppe; Vargas, Héctor; Hermosilla, Gabriel; Alfaro, Luis; Valencia, Agustín

2016-01-01

Highlights: • Feature extraction is a very critical stage in any machine learning algorithm. • The problem dimensionality can be reduced enormously when selecting suitable attributes. • Despite the importance of feature extraction, the process is commonly done manually by trial and error. • Fortunately, recent advances in deep learning approach have proposed an encouraging way to find a good feature representation automatically. • In this article, deep learning is applied to the TJ-II fusion database to get more robust and accurate classifiers in comparison to previous work. - Abstract: Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one.

Kernel-based discriminant feature extraction using a representative dataset

Science.gov (United States)

Li, Honglin; Sancho Gomez, Jose-Luis; Ahalt, Stanley C.

2002-07-01

Discriminant Feature Extraction (DFE) is widely recognized as an important pre-processing step in classification applications. Most DFE algorithms are linear and thus can only explore the linear discriminant information among the different classes. Recently, there has been several promising attempts to develop nonlinear DFE algorithms, among which is Kernel-based Feature Extraction (KFE). The efficacy of KFE has been experimentally verified by both synthetic data and real problems. However, KFE has some known limitations. First, KFE does not work well for strongly overlapped data. Second, KFE employs all of the training set samples during the feature extraction phase, which can result in significant computation when applied to very large datasets. Finally, KFE can result in overfitting. In this paper, we propose a substantial improvement to KFE that overcomes the above limitations by using a representative dataset, which consists of critical points that are generated from data-editing techniques and centroid points that are determined by using the Frequency Sensitive Competitive Learning (FSCL) algorithm. Experiments show that this new KFE algorithm performs well on significantly overlapped datasets, and it also reduces computational complexity. Further, by controlling the number of centroids, the overfitting problem can be effectively alleviated.

Noninvasive Recording of True-to-Form Fetal ECG during the Third Trimester of Pregnancy

Directory of Open Access Journals (Sweden)

Istvan Peterfi

2014-01-01

Full Text Available Objective. The aim of the study was to develop a complex electrophysiological measurement system (hardware and software which uses the methods of electrophysiology and provides significant information about the intrauterine status of the fetus, intending to obtain true-to-form, morphologically evaluated fetal ECG from transabdominal maternal lead. Results. The present method contains many novel ideas that allow creating true-to-form noninvasive fetal ECG in the third trimester of the pregnancy in 80% of the cases. Such ideas are the telemetric data collection, the “cleanse” of the real time recording from the maternal ECG, and the use of the cardiotocograph (CTG that allows identifying the fetal heart events. The advantage of this developed system is that it does not require any qualified staff, because both the extraction of the information from the abdominal recording and the processing of the data are automatic. Discussion. Although the idea of a noninvasive fetal electrocardiography is more than 100 years old still there is no simple, effective, and cheap method available that would enable an extensive use. This developed system can be used in the third trimester of the pregnancy efficiently. It can produce true-to-form fetal ECGs with amplitude less than 10 µV.

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

Science.gov (United States)

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

Science.gov (United States)

Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

2017-07-01

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time

Reliability of Computer Analysis of Electrocardiograms (ECG) of ...

African Journals Online (AJOL)

Background: Computer programmes have been introduced to electrocardiography (ECG) with most physicians in Africa depending on computer interpretation of ECG. This study was undertaken to evaluate the reliability of computer interpretation of the 12-Lead ECG in the Black race. Methodology: Using the SCHILLER ...

A multi-approach feature extractions for iris recognition

Science.gov (United States)

Sanpachai, H.; Settapong, M.

2014-04-01

Biometrics is a promising technique that is used to identify individual traits and characteristics. Iris recognition is one of the most reliable biometric methods. As iris texture and color is fully developed within a year of birth, it remains unchanged throughout a person's life. Contrary to fingerprint, which can be altered due to several aspects including accidental damage, dry or oily skin and dust. Although iris recognition has been studied for more than a decade, there are limited commercial products available due to its arduous requirement such as camera resolution, hardware size, expensive equipment and computational complexity. However, at the present time, technology has overcome these obstacles. Iris recognition can be done through several sequential steps which include pre-processing, features extractions, post-processing, and matching stage. In this paper, we adopted the directional high-low pass filter for feature extraction. A box-counting fractal dimension and Iris code have been proposed as feature representations. Our approach has been tested on CASIA Iris Image database and the results are considered successful.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

Science.gov (United States)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy.

Science.gov (United States)

Hesar, Hamed Danandeh; Mohebbi, Maryam

2017-05-01

In this paper, a model-based Bayesian filtering framework called the "marginalized particle-extended Kalman filter (MP-EKF) algorithm" is proposed for electrocardiogram (ECG) denoising. This algorithm does not have the extended Kalman filter (EKF) shortcoming in handling non-Gaussian nonstationary situations because of its nonlinear framework. In addition, it has less computational complexity compared with particle filter. This filter improves ECG denoising performance by implementing marginalized particle filter framework while reducing its computational complexity using EKF framework. An automatic particle weighting strategy is also proposed here that controls the reliance of our framework to the acquired measurements. We evaluated the proposed filter on several normal ECGs selected from MIT-BIH normal sinus rhythm database. To do so, artificial white Gaussian and colored noises as well as nonstationary real muscle artifact (MA) noise over a range of low SNRs from 10 to -5 dB were added to these normal ECG segments. The benchmark methods were the EKF and extended Kalman smoother (EKS) algorithms which are the first model-based Bayesian algorithms introduced in the field of ECG denoising. From SNR viewpoint, the experiments showed that in the presence of Gaussian white noise, the proposed framework outperforms the EKF and EKS algorithms in lower input SNRs where the measurements and state model are not reliable. Owing to its nonlinear framework and particle weighting strategy, the proposed algorithm attained better results at all input SNRs in non-Gaussian nonstationary situations (such as presence of pink noise, brown noise, and real MA). In addition, the impact of the proposed filtering method on the distortion of diagnostic features of the ECG was investigated and compared with EKF/EKS methods using an ECG diagnostic distortion measure called the "Multi-Scale Entropy Based Weighted Distortion Measure" or MSEWPRD. The results revealed that our proposed

Extraction Of Audio Features For Emotion Recognition System Based On Music

Directory of Open Access Journals (Sweden)

Kee Moe Han

2015-08-01

Full Text Available Music is the combination of melody linguistic information and the vocalists emotion. Since music is a work of art analyzing emotion in music by computer is a difficult task. Many approaches have been developed to detect the emotions included in music but the results are not satisfactory because emotion is very complex. In this paper the evaluations of audio features from the music files are presented. The extracted features are used to classify the different emotion classes of the vocalists. Musical features extraction is done by using Music Information Retrieval MIR tool box in this paper. The database of 100 music clips are used to classify the emotions perceived in music clips. Music may contain many emotions according to the vocalists mood such as happy sad nervous bored peace etc. In this paper the audio features related to the emotions of the vocalists are extracted to use in emotion recognition system based on music.

A Study of Feature Extraction Using Divergence Analysis of Texture Features

Science.gov (United States)

Hallada, W. A.; Bly, B. G.; Boyd, R. K.; Cox, S.

1982-01-01

An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters.

21 CFR 892.1970 - Radiographic ECG/respirator synchronizer.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Radiographic ECG/respirator synchronizer. 892.1970... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.1970 Radiographic ECG/respirator synchronizer. (a) Identification. A radiographic ECG/respirator synchronizer is a device intended to be used to...

Expert knowledge for computerized ECG interpretation

NARCIS (Netherlands)

J.A. Kors (Jan)

1992-01-01

textabstractIn this study, two main questions are addressed: (1) Can the time consuming and cumbersome development and refinement of (heuristic) ECG classifiers be alleviated, and (2) Is it possible to increase diagnostic performance of ECG computer programs by combining knowledge from multiple

Non-invasive Drosophila ECG recording by using eutectic gallium-indium alloy electrode: a feasible tool for future research on the molecular mechanisms involved in cardiac arrhythmia.

Directory of Open Access Journals (Sweden)

Po-Hung Kuo

Full Text Available BACKGROUND: Drosophila heart tube is a feasible model for cardiac physiological research. However, obtaining Drosophila electrocardiograms (ECGs is difficult, due to the weak signals and limited contact area to apply electrodes. This paper presents a non-invasive Gallium-Indium (GaIn based recording system for Drosophila ECG measurement, providing the heart rate and heartbeat features to be observed. This novel, high-signal-quality system prolongs the recording time of insect ECGs, and provides a feasible platform for research on the molecular mechanisms involved in cardiovascular diseases. METHODS: In this study, two types of electrode, tungsten needle probes and GaIn electrodes, were used respectively to noiselessly conduct invasive and noninvasive ECG recordings of Drosophila. To further analyze electrode properties, circuit models were established and simulated. By using electromagnetic shielded heart signal acquiring system, consisted of analog amplification and digital filtering, the ECG signals of three phenotypes that have different heart functions were recorded without dissection. RESULTS AND DISCUSSION: The ECG waveforms of different phenotypes of Drosophila recorded invasively and repeatedly with n value (n>5 performed obvious difference in heart rate. In long period ECG recordings, non-invasive method implemented by GaIn electrodes acts relatively stable in both amplitude and period. To analyze GaIn electrode, the correctness of GaIn electrode model established by this paper was validated, presenting accuracy, stability, and reliability. CONCLUSIONS: Noninvasive ECG recording by GaIn electrodes was presented for recording Drosophila pupae ECG signals within a limited contact area and signal strength. Thus, the observation of ECG changes in normal and SERCA-depleted Drosophila over an extended period is feasible. This method prolongs insect survival time while conserving major ECG features, and provides a platform for

DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

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S. Eken

2017-11-01

Full Text Available In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

A capacitive ECG array with visual patient feedback.

Science.gov (United States)

Eilebrecht, Benjamin; Schommartz, Antje; Walter, Marian; Wartzek, Tobias; Czaplik, Michael; Leonhardt, Steffen

2010-01-01

Capacitive electrocardiogram (ECG) sensing is a promising technique for less constraining vital signal measurement and close to a commercial application. Even bigger trials testing the diagnostic significance were already done with single lead systems. Anyway, most applications to be found in research are limited to one channel and thus limited in its diagnostic relevance as only diseases coming along with a change of the heart rate can be diagnosed adequately. As a consequence the need for capacitive multi-channel ECGs combining the diagnostic relevance and the advantages of capacitive ECG sensing emerges. This paper introduces a capacitive ECG measurement system which allows the recording of standardized ECG leads according to Einthoven and Goldberger by means of an electrode array with nine electrodes.

Feature extraction and sensor selection for NPP initiating event identification

International Nuclear Information System (INIS)

Lin, Ting-Han; Wu, Shun-Chi; Chen, Kuang-You; Chou, Hwai-Pwu

2017-01-01

Highlights: • A two-stage feature extraction scheme for NPP initiating event identification. • With stBP, interrelations among the sensors can be retained for identification. • With dSFS, sensors that are crucial for identification can be efficiently selected. • Efficacy of the scheme is illustrated with data from the Maanshan NPP simulator. - Abstract: Initiating event identification is essential in managing nuclear power plant (NPP) severe accidents. In this paper, a novel two-stage feature extraction scheme that incorporates the proposed sensor type-wise block projection (stBP) and deflatable sequential forward selection (dSFS) is used to elicit the discriminant information in the data obtained from various NPP sensors to facilitate event identification. With the stBP, the primal features can be extracted without eliminating the interrelations among the sensors of the same type. The extracted features are then subjected to a further dimensionality reduction by selecting the sensors that are most relevant to the events under consideration. This selection is not easy, and a combinatorial optimization technique is normally required. With the dSFS, an optimal sensor set can be found with less computational load. Moreover, its sensor deflation stage allows sensors in the preselected set to be iteratively refined to avoid being trapped into a local optimum. Results from detailed experiments containing data of 12 event categories and a total of 112 events generated with a Taiwan’s Maanshan NPP simulator are presented to illustrate the efficacy of the proposed scheme.

Human listening studies reveal insights into object features extracted by echolocating dolphins

Science.gov (United States)

Delong, Caroline M.; Au, Whitlow W. L.; Roitblat, Herbert L.

2004-05-01

Echolocating dolphins extract object feature information from the acoustic parameters of object echoes. However, little is known about which object features are salient to dolphins or how they extract those features. To gain insight into how dolphins might be extracting feature information, human listeners were presented with echoes from objects used in a dolphin echoic-visual cross-modal matching task. Human participants performed a task similar to the one the dolphin had performed; however, echoic samples consisting of 23-echo trains were presented via headphones. The participants listened to the echoic sample and then visually selected the correct object from among three alternatives. The participants performed as well as or better than the dolphin (M=88.0% correct), and reported using a combination of acoustic cues to extract object features (e.g., loudness, pitch, timbre). Participants frequently reported using the pattern of aural changes in the echoes across the echo train to identify the shape and structure of the objects (e.g., peaks in loudness or pitch). It is likely that dolphins also attend to the pattern of changes across echoes as objects are echolocated from different angles.

Level Sets and Voronoi based Feature Extraction from any Imagery

DEFF Research Database (Denmark)

Sharma, O.; Anton, François; Mioc, Darka

2012-01-01

Polygon features are of interest in many GEOProcessing applications like shoreline mapping, boundary delineation, change detection, etc. This paper presents a unique new GPU-based methodology to automate feature extraction combining level sets, or mean shift based segmentation together with Voron...

A cloud computing based 12-lead ECG telemedicine service.

Science.gov (United States)

Hsieh, Jui-Chien; Hsu, Meng-Wei

2012-07-28

Due to the great variability of 12-lead ECG instruments and medical specialists' interpretation skills, it remains a challenge to deliver rapid and accurate 12-lead ECG reports with senior cardiologists' decision making support in emergency telecardiology. We create a new cloud and pervasive computing based 12-lead Electrocardiography (ECG) service to realize ubiquitous 12-lead ECG tele-diagnosis. This developed service enables ECG to be transmitted and interpreted via mobile phones. That is, tele-consultation can take place while the patient is on the ambulance, between the onsite clinicians and the off-site senior cardiologists, or among hospitals. Most importantly, this developed service is convenient, efficient, and inexpensive. This cloud computing based ECG tele-consultation service expands the traditional 12-lead ECG applications onto the collaboration of clinicians at different locations or among hospitals. In short, this service can greatly improve medical service quality and efficiency, especially for patients in rural areas. This service has been evaluated and proved to be useful by cardiologists in Taiwan.

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

Science.gov (United States)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

Conventional QT Variability Measurement vs. Template Matching Techniques: Comparison of Performance Using Simulated and Real ECG

Science.gov (United States)

Baumert, Mathias; Starc, Vito; Porta, Alberto

2012-01-01

Increased beat-to-beat variability in the QT interval (QTV) of ECG has been associated with increased risk for sudden cardiac death, but its measurement is technically challenging and currently not standardized. The aim of this study was to investigate the performance of commonly used beat-to-beat QT interval measurement algorithms. Three different methods (conventional, template stretching and template time shifting) were subjected to simulated data featuring typical ECG recording issues (broadband noise, baseline wander, amplitude modulation) and real short-term ECG of patients before and after infusion of sotalol, a QT interval prolonging drug. Among the three algorithms, the conventional algorithm was most susceptible to noise whereas the template time shifting algorithm showed superior overall performance on simulated and real ECG. None of the algorithms was able to detect increased beat-to-beat QT interval variability after sotalol infusion despite marked prolongation of the average QT interval. The QTV estimates of all three algorithms were inversely correlated with the amplitude of the T wave. In conclusion, template matching algorithms, in particular the time shifting algorithm, are recommended for beat-to-beat variability measurement of QT interval in body surface ECG. Recording noise, T wave amplitude and the beat-rejection strategy are important factors of QTV measurement and require further investigation. PMID:22860030

Conventional QT variability measurement vs. template matching techniques: comparison of performance using simulated and real ECG.

Directory of Open Access Journals (Sweden)

Mathias Baumert

Full Text Available Increased beat-to-beat variability in the QT interval (QTV of ECG has been associated with increased risk for sudden cardiac death, but its measurement is technically challenging and currently not standardized. The aim of this study was to investigate the performance of commonly used beat-to-beat QT interval measurement algorithms. Three different methods (conventional, template stretching and template time shifting were subjected to simulated data featuring typical ECG recording issues (broadband noise, baseline wander, amplitude modulation and real short-term ECG of patients before and after infusion of sotalol, a QT interval prolonging drug. Among the three algorithms, the conventional algorithm was most susceptible to noise whereas the template time shifting algorithm showed superior overall performance on simulated and real ECG. None of the algorithms was able to detect increased beat-to-beat QT interval variability after sotalol infusion despite marked prolongation of the average QT interval. The QTV estimates of all three algorithms were inversely correlated with the amplitude of the T wave. In conclusion, template matching algorithms, in particular the time shifting algorithm, are recommended for beat-to-beat variability measurement of QT interval in body surface ECG. Recording noise, T wave amplitude and the beat-rejection strategy are important factors of QTV measurement and require further investigation.

Research on comparison of exposure with electrocardiographic gated mA modulation (ECG) and ECG and CAREDose 4D mode in coronary multi-slice spiral CT angiography

International Nuclear Information System (INIS)

Liu Bin; Guo Senlin; Wei Lan; Fei Xiaolu; Bai Mei

2009-01-01

Objective: The objective of this article was to compare patients dose with electrocardiographic gated mA modulation (ECG) and ECG and CAREDose 4D mode during coronary MSCT angiography. Methods: The research was based on phantom experiment and computer simulation to get the mean value of peak skin dose data and effective dose data respectively and to analyze deterministic and stochastic radiation risk. Results: The peak skin dose using ECG mode alone and using ECG and CAREDose 4D mode with the same image noise level was (87.4 ± 0.9) and (45.9 ± 1.2) mGy respectively. Effective dose was 17 and 10 rosy for ECG mode and ECG and CAREDose 4D mode respectively. Comparing with ECG mode alone, ECG and CAREDose 4D mode reduced organ dose of gonad, red marrow, lung, stomach, breast and thyroid by 40.0%, 36.7%, 39.3%, 37.7%, 38.8% and 38.9%, respectively. Conclusion: Results showed that ECG and CAREDose 4D mode can reduce radiation dose effectively comparing using ECG mode alone, and that ECG and CAREDose 4D mode should be widely applied clinically with appropriate initial settings. (authors)

Wearable Textile Electrodes for ECG Measurement

Directory of Open Access Journals (Sweden)

Lukas Vojtech

2013-01-01

Full Text Available The electrocardiogram (ECG is one of the most important parameters for monitoring of the physiological state of a person. Currently available systems for ECG monitoring are both stationary and wearable, but the comfort of the monitored person is not at a satisfactory level because these systems are not part of standard clothing. This article is therefore devoted to the development and measurement of wearable textile electrodes for ECG measurement device with high comfort for the user. The electrode material is made of electrically conductive textile. This creates a textile composite that guarantees high comfort for the user while ensuring good quality of ECG measurements. The composite is implemented by a carrier (a T-shirt with flame retardant and sensing electrodes embroidered with yarn based on a mixture of polyester coated with silver nanoparticles and cotton. The electrodes not only provide great comfort but are also antibacterial and antiallergic due to silver nanoparticles.

The algorithm of fast image stitching based on multi-feature extraction

Science.gov (United States)

Yang, Chunde; Wu, Ge; Shi, Jing

2018-05-01

This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

A Novel 12-Lead ECG T-Shirt with Active Electrodes

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Anna Boehm

2016-11-01

Full Text Available We developed an ECG T-shirt with a portable recorder for unobtrusive and long-term multichannel ECG monitoring with active electrodes. A major drawback of conventional 12-lead ECGs is the use of adhesive gel electrodes, which are uncomfortable during long-term application and may even cause skin irritations and allergic reactions. Therefore, we integrated comfortable patches of conductive textile into the ECG T-shirt in order to replace the adhesive gel electrodes. In order to prevent signal deterioration, as reported for other textile ECG systems, we attached active circuits on the outside of the T-shirt to further improve the signal quality of the dry electrodes. Finally, we validated the ECG T-shirt against a commercial Holter ECG with healthy volunteers during phases of lying down, sitting, and walking. The 12-lead ECG was successfully recorded with a resulting mean relative error of the RR intervals of 0.96% and mean coverage of 96.6%. Furthermore, the ECG waves of the 12 leads were analyzed separately and showed high accordance. The P-wave had a correlation of 0.703 for walking subjects, while the T-wave demonstrated lower correlations for all three scenarios (lying: 0.817, sitting: 0.710, walking: 0.403. The other correlations for the P, Q, R, and S-waves were all higher than 0.9. This work demonstrates that our ECG T-shirt is suitable for 12-lead ECG recordings while providing a higher level of comfort compared with a commercial Holter ECG.

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

OpenAIRE

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting ...

A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording

Directory of Open Access Journals (Sweden)

Nannan Zhang

2017-02-01

Full Text Available Non-invasive fetal electrocardiograms (FECGs are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG in the spectral domain. Research in the area of non-invasive separations of FECG from abdominal electrocardiograms (AECGs is in its infancy and several studies are currently focusing on this area. An adaptive noise canceller (ANC is commonly used for cancelling interference in cases where the reference signal only correlates with an interference signal, and not with a signal of interest. However, results from some existing studies suggest that propagation of electrocardiogram (ECG signals from the maternal heart to the abdomen is nonlinear, hence the adaptive filter approach may fail if the thoracic and abdominal MECG lack strict waveform similarity. In this study, singular value decomposition (SVD and smooth window (SW techniques are combined to build a reference signal in an ANC. This is to avoid the limitation that thoracic MECGs recorded separately must be similar to abdominal MECGs in waveform. Validation of the proposed method with r01 and r07 signals from a public dataset, and a self-recorded private dataset showed that the proposed method achieved F1 scores of 99.61%, 99.28% and 98.58%, respectively for the detection of fetal QRS. Compared with four other single-channel methods, the proposed method also achieved higher accuracy values of 99.22%, 98.57% and 97.21%, respectively. The findings from this study suggest that the proposed method could potentially aid accurate extraction of FECG from MECG recordings in both clinical and commercial applications.

Automatic ECG quality scoring methodology: mimicking human annotators

International Nuclear Information System (INIS)

Johannesen, Lars; Galeotti, Loriano

2012-01-01

An algorithm to determine the quality of electrocardiograms (ECGs) can enable inexperienced nurses and paramedics to record ECGs of sufficient diagnostic quality. Previously, we proposed an algorithm for determining if ECG recordings are of acceptable quality, which was entered in the PhysioNet Challenge 2011. In the present work, we propose an improved two-step algorithm, which first rejects ECGs with macroscopic errors (signal absent, large voltage shifts or saturation) and subsequently quantifies the noise (baseline, powerline or muscular noise) on a continuous scale. The performance of the improved algorithm was evaluated using the PhysioNet Challenge database (1500 ECGs rated by humans for signal quality). We achieved a classification accuracy of 92.3% on the training set and 90.0% on the test set. The improved algorithm is capable of detecting ECGs with macroscopic errors and giving the user a score of the overall quality. This allows the user to assess the degree of noise and decide if it is acceptable depending on the purpose of the recording. (paper)

Research on feature extraction techniques of Hainan Li brocade pattern

Science.gov (United States)

Zhou, Yuping; Chen, Fuqiang; Zhou, Yuhua

2016-03-01

Hainan Li brocade skills has been listed as world non-material cultural heritage preservation, therefore, the research on Hainan Li brocade patterns plays an important role in Li brocade culture inheritance. The meaning of Li brocade patterns was analyzed and the shape feature extraction techniques to original Li brocade patterns were advanced in this paper, based on the contour tracking algorithm. First, edge detection was made on the design patterns, and then the morphological closing operation was used to smooth the image, and finally contour tracking was used to extract the outer contours of Li brocade patterns. The extracted contour features were processed by means of morphology, and digital characteristics of contours are obtained by invariant moments. At last, different patterns of Li brocade design are briefly analyzed according to the digital characteristics. The results showed that the pattern extraction method to Li brocade pattern shapes is feasible and effective according to above method.

Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

Directory of Open Access Journals (Sweden)

Seungsoo Nam

2018-01-01

Full Text Available This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector, which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

Basic principles of the ECG. The normal ECG

African Journals Online (AJOL)

Angel_D

Southern Sudan Medical Journal vol 3. no 2. 26. How to read an ... Reduce some of the anxiety juniors often experience when faced with an ECG. ... This overall direction of travel of the electrical .... Anne Lancey, Education Centre, St Mary's Hospital, Isle of Wight, UK. .... 'method' section explains how the literature search.

An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms.

Science.gov (United States)

Andreotti, Fernando; Behar, Joachim; Zaunseder, Sebastian; Oster, Julien; Clifford, Gari D

2016-05-01

Over the past decades, many studies have been published on the extraction of non-invasive foetal electrocardiogram (NI-FECG) from abdominal recordings. Most of these contributions claim to obtain excellent results in detecting foetal QRS (FQRS) complexes in terms of location. A small subset of authors have investigated the extraction of morphological features from the NI-FECG. However, due to the shortage of available public databases, the large variety of performance measures employed and the lack of open-source reference algorithms, most contributions cannot be meaningfully assessed. This article attempts to address these issues by presenting a standardised methodology for stress testing NI-FECG algorithms, including absolute data, as well as extraction and evaluation routines. To that end, a large database of realistic artificial signals was created, totaling 145.8 h of multichannel data and over one million FQRS complexes. An important characteristic of this dataset is the inclusion of several non-stationary events (e.g. foetal movements, uterine contractions and heart rate fluctuations) that are critical for evaluating extraction routines. To demonstrate our testing methodology, three classes of NI-FECG extraction algorithms were evaluated: blind source separation (BSS), template subtraction (TS) and adaptive methods (AM). Experiments were conducted to benchmark the performance of eight NI-FECG extraction algorithms on the artificial database focusing on: FQRS detection and morphological analysis (foetal QT and T/QRS ratio). The overall median FQRS detection accuracies (i.e. considering all non-stationary events) for the best performing methods in each group were 99.9% for BSS, 97.9% for AM and 96.0% for TS. Both FQRS detections and morphological parameters were shown to heavily depend on the extraction techniques and signal-to-noise ratio. Particularly, it is shown that their evaluation in the source domain, obtained after using a BSS technique, should be

Image feature extraction based on the camouflage effectiveness evaluation

Science.gov (United States)

Yuan, Xin; Lv, Xuliang; Li, Ling; Wang, Xinzhu; Zhang, Zhi

2018-04-01

The key step of camouflage effectiveness evaluation is how to combine the human visual physiological features, psychological features to select effectively evaluation indexes. Based on the predecessors' camo comprehensive evaluation method, this paper chooses the suitable indexes combining with the image quality awareness, and optimizes those indexes combining with human subjective perception. Thus, it perfects the theory of index extraction.

Feature-extraction algorithms for the PANDA electromagnetic calorimeter

NARCIS (Netherlands)

Kavatsyuk, M.; Guliyev, E.; Lemmens, P. J. J.; Loehner, H.; Poelman, T. P.; Tambave, G.; Yu, B

2009-01-01

The feature-extraction algorithms are discussed which have been developed for the digital front-end electronics of the electromagnetic calorimeter of the PANDA detector at the future FAIR facility. Performance parameters have been derived in test measurements with cosmic rays, particle and photon

Freeware eLearning Flash-ECG for learning electrocardiography.

Science.gov (United States)

Romanov, Kalle; Kuusi, Timo

2009-06-01

Electrocardiographic (ECG) analysis can be taught in eLearning programmes with suitable software that permits the effective use of basic tools such as a ruler and a magnifier, required for measurements. The Flash-ECG (Research & Development Unit for Medical Education, University of Helsinki, Finland) was developed to enable teachers and students to use scanned and archived ECGs on computer screens and classroom projectors. The software requires only a standard web browser with a Flash plug-in and can be integrated with learning environments (Blackboard/WebCT, Moodle). The Flash-ECG is freeware and is available to medical teachers worldwide.

A cloud computing based 12-lead ECG telemedicine service

Science.gov (United States)

2012-01-01

Background Due to the great variability of 12-lead ECG instruments and medical specialists’ interpretation skills, it remains a challenge to deliver rapid and accurate 12-lead ECG reports with senior cardiologists’ decision making support in emergency telecardiology. Methods We create a new cloud and pervasive computing based 12-lead Electrocardiography (ECG) service to realize ubiquitous 12-lead ECG tele-diagnosis. Results This developed service enables ECG to be transmitted and interpreted via mobile phones. That is, tele-consultation can take place while the patient is on the ambulance, between the onsite clinicians and the off-site senior cardiologists, or among hospitals. Most importantly, this developed service is convenient, efficient, and inexpensive. Conclusions This cloud computing based ECG tele-consultation service expands the traditional 12-lead ECG applications onto the collaboration of clinicians at different locations or among hospitals. In short, this service can greatly improve medical service quality and efficiency, especially for patients in rural areas. This service has been evaluated and proved to be useful by cardiologists in Taiwan. PMID:22838382

A cloud computing based 12-lead ECG telemedicine service

Directory of Open Access Journals (Sweden)

Hsieh Jui-chien

2012-07-01

Full Text Available Abstract Background Due to the great variability of 12-lead ECG instruments and medical specialists’ interpretation skills, it remains a challenge to deliver rapid and accurate 12-lead ECG reports with senior cardiologists’ decision making support in emergency telecardiology. Methods We create a new cloud and pervasive computing based 12-lead Electrocardiography (ECG service to realize ubiquitous 12-lead ECG tele-diagnosis. Results This developed service enables ECG to be transmitted and interpreted via mobile phones. That is, tele-consultation can take place while the patient is on the ambulance, between the onsite clinicians and the off-site senior cardiologists, or among hospitals. Most importantly, this developed service is convenient, efficient, and inexpensive. Conclusions This cloud computing based ECG tele-consultation service expands the traditional 12-lead ECG applications onto the collaboration of clinicians at different locations or among hospitals. In short, this service can greatly improve medical service quality and efficiency, especially for patients in rural areas. This service has been evaluated and proved to be useful by cardiologists in Taiwan.

ECG telemetry in conscious guinea pigs.

Science.gov (United States)

Ruppert, Sabine; Vormberge, Thomas; Igl, Bernd-Wolfgang; Hoffmann, Michael

2016-01-01

During preclinical drug development, monitoring of the electrocardiogram (ECG) is an important part of cardiac safety assessment. To detect potential pro-arrhythmic liabilities of a drug candidate and for internal decision-making during early stage drug development an in vivo model in small animals with translatability to human cardiac function is required. Over the last years, modifications/improvements regarding animal housing, ECG electrode placement, and data evaluation have been introduced into an established model for ECG recordings using telemetry in conscious, freely moving guinea pigs. Pharmacological validation using selected reference compounds affecting different mechanisms relevant for cardiac electrophysiology (quinidine, flecainide, atenolol, dl-sotalol, dofetilide, nifedipine, moxifloxacin) was conducted and findings were compared with results obtained in telemetered Beagle dogs. Under standardized conditions, reliable ECG data with low variability allowing largely automated evaluation were obtained from the telemetered guinea pig model. The model is sensitive to compounds blocking cardiac sodium channels, hERG K(+) channels and calcium channels, and appears to be even more sensitive to β-blockers as observed in dogs at rest. QT interval correction according to Bazett and Sarma appears to be appropriate methods in conscious guinea pigs. Overall, the telemetered guinea pig is a suitable model for the conduct of early stage preclinical ECG assessment. Copyright © 2016 Elsevier Inc. All rights reserved.

Methods for Improving the Diagnosis of a Brugada ECG Pattern.

Science.gov (United States)

Gottschalk, Byron H; Garcia-Niebla, Javier; Anselm, Daniel D; Glover, Benedict; Baranchuk, Adrian

2016-03-01

Brugada syndrome (BrS) is an inherited channelopathy that predisposes individuals to malignant arrhythmias and can lead to sudden cardiac death. The condition is characterized by two electrocardiography (ECG) patterns: the type-1 or "coved" ECG and the type-2 or "saddleback" ECG. Although the type-1 Brugada ECG pattern is diagnostic for the condition, the type-2 Brugada ECG pattern requires differential diagnosis from conditions that produce a similar morphology. In this article, we present a case that is suspicious but not diagnostic for BrS and discuss the application of ECG methodologies for increasing or decreasing suspicion for a diagnosis of BrS. © 2015 Wiley Periodicals, Inc.

Wireless ECG and PCG Portable Telemedicine Kit for Rural Areas of Colombia

Directory of Open Access Journals (Sweden)

Miguel Jimeno

2014-07-01

Full Text Available Telemedicine is always a popular topic thanks to the constants advancements of technology. The focus on development of new devices has been mainly on decreasing size to increase portability. Our research focused on improving functionality but not giving up on portability and cost. In this paper we are presenting the first prototype device that measures 4-leads electrocardiogram (ECG and phonocardiogram (PCG signals with low cost, high portability and wireless connectivity features in mind. We designed and developed a prototype that measures ECG using a standard ECG cable; we designed and developed a digital stethoscope prototype and also the necessary hardware for both medical signals to be transmitted through Bluetooth to a computer. We present here the hardware design, a new communication protocol for transmission of both signals from the device to the computer, and the software system to enable remote consultations. We designed the prototype with the main purpose of using low cost parts without sacrificing functionality, with the purpose of using it in remote zones of the Caribbean coast of Colombia. We show open issues and prepare a field implementation of the kit in the target zone.

Usefulness of ST elevation score by using vector-projected virtual 187-channel ECG for risk stratification in patients with Brugada-type ECG pattern

Directory of Open Access Journals (Sweden)

Shoko Ishikawa

2012-08-01

Conclusion: The ST elevation score in VP-ECG objectively documented the degree of ST elevation in surface ECG in Brugada-type ECG patterns. The ST-elevation score might be useful for risk stratification in patients with asymptomatic Brugada syndrome.

A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

Science.gov (United States)

Zhang, Yang; Rockett, Peter I

2009-01-01

In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

FAST DISCRETE CURVELET TRANSFORM BASED ANISOTROPIC FEATURE EXTRACTION FOR IRIS RECOGNITION

Directory of Open Access Journals (Sweden)

Amol D. Rahulkar

2010-11-01

Full Text Available The feature extraction plays a very important role in iris recognition. Recent researches on multiscale analysis provide good opportunity to extract more accurate information for iris recognition. In this work, a new directional iris texture features based on 2-D Fast Discrete Curvelet Transform (FDCT is proposed. The proposed approach divides the normalized iris image into six sub-images and the curvelet transform is applied independently on each sub-image. The anisotropic feature vector for each sub-image is derived using the directional energies of the curvelet coefficients. These six feature vectors are combined to create the resultant feature vector. During recognition, the nearest neighbor classifier based on Euclidean distance has been used for authentication. The effectiveness of the proposed approach has been tested on two different databases namely UBIRIS and MMU1. Experimental results show the superiority of the proposed approach.

An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction

Directory of Open Access Journals (Sweden)

Yong Zhu

2015-01-01

Full Text Available After summarizing the advantages and disadvantages of current integral methods, a novel vibration signal integral method based on feature information extraction was proposed. This method took full advantage of the self-adaptive filter characteristic and waveform correction feature of ensemble empirical mode decomposition in dealing with nonlinear and nonstationary signals. This research merged the superiorities of kurtosis, mean square error, energy, and singular value decomposition on signal feature extraction. The values of the four indexes aforementioned were combined into a feature vector. Then, the connotative characteristic components in vibration signal were accurately extracted by Euclidean distance search, and the desired integral signals were precisely reconstructed. With this method, the interference problem of invalid signal such as trend item and noise which plague traditional methods is commendably solved. The great cumulative error from the traditional time-domain integral is effectively overcome. Moreover, the large low-frequency error from the traditional frequency-domain integral is successfully avoided. Comparing with the traditional integral methods, this method is outstanding at removing noise and retaining useful feature information and shows higher accuracy and superiority.

Empirical mode decomposition of the ECG signal for noise removal

Science.gov (United States)

Khan, Jesmin; Bhuiyan, Sharif; Murphy, Gregory; Alam, Mohammad

2011-04-01

Electrocardiography is a diagnostic procedure for the detection and diagnosis of heart abnormalities. The electrocardiogram (ECG) signal contains important information that is utilized by physicians for the diagnosis and analysis of heart diseases. So good quality ECG signal plays a vital role for the interpretation and identification of pathological, anatomical and physiological aspects of the whole cardiac muscle. However, the ECG signals are corrupted by noise which severely limit the utility of the recorded ECG signal for medical evaluation. The most common noise presents in the ECG signal is the high frequency noise caused by the forces acting on the electrodes. In this paper, we propose a new ECG denoising method based on the empirical mode decomposition (EMD). The proposed method is able to enhance the ECG signal upon removing the noise with minimum signal distortion. Simulation is done on the MIT-BIH database to verify the efficacy of the proposed algorithm. Experiments show that the presented method offers very good results to remove noise from the ECG signal.

Biometric and Emotion Identification: An ECG Compression Based Method.

Science.gov (United States)

Brás, Susana; Ferreira, Jacqueline H T; Soares, Sandra C; Pinho, Armando J

2018-01-01

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.

Towards Home-Made Dictionaries for Musical Feature Extraction

DEFF Research Database (Denmark)

Harbo, Anders La-Cour

2003-01-01

arguably unnecessary limitations on the ability of the transform to extract and identify features. However, replacing the nicely structured dictionary of the Fourier transform (or indeed other nice transform such as the wavelet transform) with a home-made dictionary is a dangerous task, since even the most...

A threshold auto-adjustment algorithm of feature points extraction based on grid

Science.gov (United States)

Yao, Zili; Li, Jun; Dong, Gaojie

2018-02-01

When dealing with high-resolution digital images, detection of feature points is usually the very first important step. Valid feature points depend on the threshold. If the threshold is too low, plenty of feature points will be detected, and they may be aggregated in the rich texture regions, which consequently not only affects the speed of feature description, but also aggravates the burden of following processing; if the threshold is set high, the feature points in poor texture area will lack. To solve these problems, this paper proposes a threshold auto-adjustment method of feature extraction based on grid. By dividing the image into numbers of grid, threshold is set in every local grid for extracting the feature points. When the number of feature points does not meet the threshold requirement, the threshold will be adjusted automatically to change the final number of feature points The experimental results show that feature points produced by our method is more uniform and representative, which avoids the aggregation of feature points and greatly reduces the complexity of following work.

Design and implementation of a multiband digital filter using FPGA to extract the ECG signal in the presence of different interference signals.

Science.gov (United States)

Aboutabikh, Kamal; Aboukerdah, Nader

2015-07-01

In this paper, we propose a practical way to synthesize and filter an ECG signal in the presence of four types of interference signals: (1) those arising from power networks with a fundamental frequency of 50Hz, (2) those arising from respiration, having a frequency range from 0.05 to 0.5Hz, (3) muscle signals with a frequency of 25Hz, and (4) white noise present within the ECG signal band. This was done by implementing a multiband digital filter (seven bands) of type FIR Multiband Least Squares using a digital programmable device (Cyclone II EP2C70F896C6 FPGA, Altera), which was placed on an education and development board (DE2-70, Terasic). This filter was designed using the VHDL language in the Quartus II 9.1 design environment. The proposed method depends on Direct Digital Frequency Synthesizers (DDFS) designed to synthesize the ECG signal and various interference signals. So that the synthetic ECG specifications would be closer to actual ECG signals after filtering, we designed in a single multiband digital filter instead of using three separate digital filters LPF, HPF, BSF. Thus all interference signals were removed with a single digital filter. The multiband digital filter results were studied using a digital oscilloscope to characterize input and output signals in the presence of differing sinusoidal interference signals and white noise. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hyperkalemia on ECG

Directory of Open Access Journals (Sweden)

Bryson Hicks

2016-09-01

Full Text Available History of present illness: A 34-year-old diabetic female presented to the emergency department with chest pain status-post AICD firing. She described the pain as a “12 out of 10” which woke her from sleep at 0200, one hour prior to arrival. Vitals were unremarkable. She had no known history of renal failure. Due to frequent ED visits for chronic pain, patient had difficult vascular access and nursing was initially unable to obtain IV access. An abnormal rhythm was noted on the cardiac monitor, and ECG was ordered. Significant findings: Initial ECG shows tall, peaked T waves, most prominently in V3 and V4, as well as QRS widening. These findings are consistent with hyperkalemia, which was promptly treated. Follow-up ECG post-treatment shows narrowing of the QRS complexes and normalization of peaked T waves. Discussion: The etiology of hyperkalemia may be due to an acute insult such as crush injury, drug side effect, or in acute renal failure, but may also occur in the setting of a chronic insult such as chronic kidney disease.1 As potassium rises, several abnormalities can be identified on ECG. Initially the T waves become peaked and the QRS complexes widen.2,3 This can devolve into a wide complex rhythm, ventricular tachycardia, ventricular fibrillation, or asystole. Patients may also experience systemic symptoms such as weakness or paralysis.1 In this particular case, labs showed a potassium of 7.6-mmol/L after initial treatment (see repeat EKG. While the incidence of hyperkalemia in the general population is not defined, the incidence in hospitalized patients is 1.3-10%.4-8 Impaired kidney function is the most common risk factor found in 33-83% of affected patients.4,5,8,9 Treatment for hyperkalemia generally includes IV insulin and IV dextrose and nebulized albuterol for intracellular shift of potassium, IV furosemide and IV fluids for dilution and renal excretion of furosemide, and IV calcium for stabilization of cardiac membranes.2,3

Wireless Self-Acquistion of 12-Lead ECG via Android Smart Phone

Science.gov (United States)

Schlegel, Todd T.

2012-01-01

Researchers at NASA s Johnson Space Center and at Orbital Research, Inc. (a NASA SBIR grant recipient) have recently developed a dry-electrode harness that allows for self-acquisition of resting 12-lead ECGs by minimally trained laypersons. When used in conjunction with commercial wireless (e.g., Bluetooth(TM) or 802.11-enabled) 12-lead ECG devices and custom smart phone-based software, the collected 12-lead ECG data can also immediately be forwarded from any geographic location within cellular range to the user s physician(s) of choice. The system can also be used to immediately forward to central receiving stations 12-lead ECG data collected during space flight or during activities in any remote terrestrial location supported by an internet or cellular phone infrastructure. The main novel aspects of the system are first, the dry-electrode 12-lead ECG harness itself, and second, an accompanying Android(TM) smart phone-based wireless 12-lead ECG capability. The ECG harness nominally employs dry electrodes manufactured by Orbital Research, Inc, recently cleared through the Food and Drug Administration (FDA). However, other dry electrodes that are not yet FDA cleared, for example those recently developed by Nanosonic, Inc as part of another NASA SBIR grant, can also be used. The various advantageous features of the harness include: 1) laypersons can be quickly instructed on its correct use, remotely if necessary; 2) all tangled "leadwire spaghetti" is eliminated, as is the common clinical problem of "leadwire reversal"; 3) all adhesives and disposables are also eliminated, the harness being fully reusable; if multiple individuals intend to use use the same harness, then standard antimicrobial wipes can be employed to sterilize the dry electrodes (and harness surface if needed) between users; 5) padded cushions at the lateral sides of the torso function to press the left arm (LA) and right arm (RA) dry electrodes mounted on the cushions against sideward or downward

Ability of a 5-minute electrocardiography (ECG) for predicting arrhythmias in Doberman Pinschers with cardiomyopathy in comparison with a 24-hour ambulatory ECG.

Science.gov (United States)

Wess, G; Schulze, A; Geraghty, N; Hartmann, K

2010-01-01

Ventricular premature contractions (VPCs) are common in the occult stage of cardiomyopathy in Doberman Pinschers. Although the gold standard for detecting arrhythmia is the 24-hour ambulatory electrocardiography (ECG) (Holter), this method is more expensive, time-consuming and often not as readily available as common ECG. Comparison of 5-minute ECGs with Holter examinations. Eight hundred and seventy-five 5-minute ECGs and Holter examinations of 431 Doberman Pinschers. Each examination included a 5-minute ECG and Holter examination. A cut-off value of > 100 VPCs/24 hours using Holter was considered diagnostic for the presence of cardiomyopathy. Statistical evaluation included calculation of sensitivity, specificity, positive predictive value, and negative predictive value. Holter examinations revealed > 100 VPCs/24 hours in 204/875 examinations. At least 1 VPC during a 5-minute ECG was detected in 131 (64.2%) of these 204 examinations. No VPCs were found in the 5-minute ECG in 73 (35.8%) examinations of affected Doberman Pinschers. A 5-minute ECG with at least 1 VPC as cut-off had a sensitivity of 64.2%, a specificity of 96.7%, a positive predictive value of 85.6% and a negative predictive value of 89.9% for the presence of > 100 VPCs/24 hours. A 5-minute ECG is a rather insensitive method for detecting arrhythmias in Doberman Pinschers. However, the occurrence of at least 1 VPC in 5 minutes strongly warrants further examination of the dog, because specificity (96.7%) and positive predictive value (85.6%) are high and could suggest occult cardiomyopathy.

[ECG for non-competitive sports in childhood: strengths and disputes].

Science.gov (United States)

Poggi, Elena; Giannattasio, Alessandro; Bolloli, Sara; Beccaria, Andrea; Mezzano, Paola; Rocca, Paola; Del Vecchio, Cecilia

2016-11-01

Sport is very important for health promotion and conservation. Active lifestyle and regular exercise reduce cardiovascular disease incidence. The Italian Ministry of Health issued the Law Decree no. 243 (10/18/2014) concerning "guidelines for certification about non-competitive sports" to promote safety in sports. This regulation defines the activities for which a certificate is required, the professional actors involved and the clinical exams to be performed according to the patient's health status. In particular, the Law Decree recommends to perform an electrocardiogram (ECG) "at least once in a lifetime", introducing much greater news into pediatric practice. We proposed a survey evaluating frequency of ECG implementation for non-competitive sports and cardiovascular diseases incidence was administered to 7 Ligurian pediatricians. The number of ECG/year for pediatrician increased from 10 ECG/year to 50 ECG/year with an indication of suitability to non-competitive sports. One case of QT prolongation and 2 cases of type 1 Brugada ECG pattern were diagnosed. In addition, 3 patients had an atrial septal defect and 3 children had a ventricular septal defect. Forty-three percent of the pediatricians considered useful performing the ECG. ECG in children has enhanced the positive effects on the community health. However, it remains to be defined in agreement with scientific societies the age at which to perform ECG, the sports for which ECG is required and the cost-benefit ratio for the National Health System and families.

Female False Positive Exercise Stress ECG Testing - Fact Verses Fiction.

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Fitzgerald, Benjamin T; Scalia, William M; Scalia, Gregory M

2018-03-07

Exercise stress testing is a well validated cardiovascular investigation. Accuracy for treadmill stress electrocardiograph (ECG) testing has been documented at 60%. False positive stress ECGs (exercise ECG changes with non-obstructive disease on anatomical testing) are common, especially in women, limiting the effectiveness of the test. This study investigates the incidence and predictors of false positive stress ECG findings, referenced against stress echocardiography (SE) as a standard. Stress echocardiography was performed using the Bruce treadmill protocol. False positive stress ECG tests were defined as greater than 1mm of ST depression on ECG during exertion, without pain, with a normal SE. Potential causes for false positive tests were recorded before the test. Three thousand consecutive negative stress echocardiograms (1036 females, 34.5%) were analysed (age 59+/-14 years. False positive (F+) stress ECGs were documented in 565/3000 tests (18.8%). F+ stress ECGs were equally prevalent in females (194/1036, 18.7%) and males (371/1964, 18.9%, p=0.85 for the difference). Potential causes (hypertension, left ventricular hypertrophy, known coronary disease, arrhythmia, diabetes mellitus, valvular heart disease) were recorded in 36/194 (18.6%) of the female F+ ECG tests and 249/371 (68.2%) of the male F+ ECG tests (preinforce the value of stress imaging, particularly in women. Copyright © 2018 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). All rights reserved.

A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set

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Abdul Wahab Muzaffar

2015-01-01

Full Text Available The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, there is a need for automatic tools and techniques for information extraction in biomedical text mining. Relation extraction is a significant area under biomedical information extraction that has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction focusing on rule-based and machine learning techniques. In the last decade, the focus has changed to hybrid approaches showing better results. This research presents a hybrid feature set for classification of relations between biomedical entities. The main contribution of this research is done in the semantic feature set where verb phrases are ranked using Unified Medical Language System (UMLS and a ranking algorithm. Support Vector Machine and Naïve Bayes, the two effective machine learning techniques, are used to classify these relations. Our approach has been validated on the standard biomedical text corpus obtained from MEDLINE 2001. Conclusively, it can be articulated that our framework outperforms all state-of-the-art approaches used for relation extraction on the same corpus.

ECG abnormalities in patients with chronic kidney disease

International Nuclear Information System (INIS)

Shafi, S.; Saleem, M.; Anjum, R.; Abdullah, W.; Shafi, T.

2017-01-01

Chronic kidney disease (CKD) is associated with increased risk of cardiovascular disease. Electrocardiographic (ECG) abnormalities are common in CKD patients. However, there is variation in literature regarding frequency of ECG abnormalities in CKD patients and limited information in local population. Methods: The study design was cross-sectional in nature. All patients between ages of 20-80 years with CKD not previously on renal replacement therapy who were admitted to nephrology ward at a tertiary care facility over a 6-month period were included. All patients underwent 12 lead electrocardiograms (ECG). ECG abnormalities were defined based on accepted standard criteria. Results: Total number of patients included in the study was 124. Mean age of all patients was 49.9+-13.8 years, 106 (84.8%) had hypertension, 84 (70%) had diabetes mellitus, and 35 (29.9%) had known cardiovascular disease. Mean serum creatinine was 7.2+-3.4 mg/dl, mean eGFR was 10.6+-9.2 ml/min/1.73 m/sup 2/. Overall 78.4% of all CKD patients have one or more ECG abnormality. Left ventricular hypertrophy (40%), Q waves (27.2%), ST segment elevation or depression (23.4%), prolonged QRS duration (19.2%), tachycardia (17.6%) and left and right atrial enlargement (17.6%) were the most common abnormalities. Conclusion: ECG abnormalities are common in hospitalized CKD patients in local population. All hospitalized CKD patients should undergo ECG to screen for cardiovascular disease. (author)

Matrix of regularity for improving the quality of ECGs

International Nuclear Information System (INIS)

Xia, Henian; Garcia, Gabriel A; Zhao, Xiaopeng; Bains, Jujhar; Wortham, Dale C

2012-01-01

The 12-lead electrocardiography (ECG) is the gold standard for diagnosis of abnormalities of the heart. However, the ECG is susceptible to artifacts, which may lead to wrong diagnosis and thus mistreatment. It is a clinical challenge of great significance differentiating ECG artifacts from patterns of diseases. We propose a computational framework, called the matrix of regularity, to evaluate the quality of ECGs. The matrix of regularity is a novel mechanism to fuse results from multiple tests of signal quality. Moreover, this method can produce a continuous grade, which can more accurately represent the quality of an ECG. When tested on a dataset from the Computing in Cardiology/PhysioNet Challenge 2011, the algorithm achieves up to 95% accuracy. The area under the receiver operating characteristic curve is 0.97. The developed framework and computer program have the potential to improve the quality of ECGs collected using conventional and portable devices. (paper)

The PLR-DTW method for ECG based biometric identification.

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Shen, Jun; Bao, Shu-Di; Yang, Li-Cai; Li, Ye

2011-01-01

There has been a surge of research on electrocardiogram (ECG) signal based biometric for person identification. Though most of the existing studies claimed that ECG signal is unique to an individual and can be a viable biometric, one of the main difficulties for real-world applications of ECG biometric is the accuracy performance. To address this problem, this study proposes a PLR-DTW method for ECG biometric, where the Piecewise Linear Representation (PLR) is used to keep important information of an ECG signal segment while reduce the data dimension at the same time if necessary, and the Dynamic Time Warping (DTW) is used for similarity measures between two signal segments. The performance evaluation was carried out on three ECG databases, and the existing method using wavelet coefficients, which was proved to have good accuracy performance, was selected for comparison. The analysis results show that the PLR-DTW method achieves an accuracy rate of 100% for identification, while the one using wavelet coefficients achieved only around 93%.

Extracted facial feature of racial closely related faces

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Liewchavalit, Chalothorn; Akiba, Masakazu; Kanno, Tsuneo; Nagao, Tomoharu

2010-02-01

Human faces contain a lot of demographic information such as identity, gender, age, race and emotion. Human being can perceive these pieces of information and use it as an important clue in social interaction with other people. Race perception is considered the most delicacy and sensitive parts of face perception. There are many research concerning image-base race recognition, but most of them are focus on major race group such as Caucasoid, Negroid and Mongoloid. This paper focuses on how people classify race of the racial closely related group. As a sample of racial closely related group, we choose Japanese and Thai face to represents difference between Northern and Southern Mongoloid. Three psychological experiment was performed to study the strategies of face perception on race classification. As a result of psychological experiment, it can be suggested that race perception is an ability that can be learn. Eyes and eyebrows are the most attention point and eyes is a significant factor in race perception. The Principal Component Analysis (PCA) was performed to extract facial features of sample race group. Extracted race features of texture and shape were used to synthesize faces. As the result, it can be suggested that racial feature is rely on detailed texture rather than shape feature. This research is a indispensable important fundamental research on the race perception which are essential in the establishment of human-like race recognition system.

Critical analysis of a computer-assisted tutorial on ECG interpretation and its ability to determine competency.

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Burke, J F; Gnall, E; Umrudden, Z; Kyaw, M; Schick, P K

2008-01-01

We developed a computer-based tutorial and a posttest on ECG interpretation for training residents and determining competency. Forty residents, 6 cardiology fellows, and 4 experienced physicians participated. The tutorial emphasized recognition and understanding of abnormal ECG features. Active learning was promoted by asking questions prior to the discussion of ECGs. Interactivity was facilitated by providing rapid and in-depth rationale for correct answers. Responses to questions were recorded and extensively analyzed to determine the quality of questions, baseline knowledge at different levels of training and improvement of grades in posttest. Posttest grades were used to assess improvement and to determine competency. The questions were found to be challenging, fair, appropriate and discriminative. This was important since the quality of Socratic questions is critical for the success of interactive programs. The information on strengths and weakness in baseline knowledge at different levels of training were used to adapt our training program to the needs of residents. The posttest revealed that the tutorial contributed to marked improvement in feature recognition. Competency testing distinguished between residents with outstanding grades and those who needed remediation. The strategy for critical evaluation of our computer program could be applied to any computer-based educational program, regardless of topic.

Feature extraction using convolutional neural network for classifying breast density in mammographic images

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Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

2017-03-01

Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

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He, Runnan; Wang, Kuanquan; Li, Qince; Yuan, Yongfeng; Zhao, Na; Liu, Yang; Zhang, Henggui

2017-12-01

Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.

A Study on the Optimal Positions of ECG Electrodes in a Garment for the Design of ECG-Monitoring Clothing for Male.

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Cho, Hakyung; Lee, Joo Hyeon

2015-09-01

Smart clothing is a sort of wearable device used for ubiquitous health monitoring. It provides comfort and efficiency in vital sign measurements and has been studied and developed in various types of monitoring platforms such as T-shirt and sports bra. However, despite these previous approaches, smart clothing for electrocardiography (ECG) monitoring has encountered a serious shortcoming relevant to motion artifacts caused by wearer movement. In effect, motion artifacts are one of the major problems in practical implementation of most wearable health-monitoring devices. In the ECG measurements collected by a garment, motion artifacts are usually caused by improper location of the electrode, leading to lack of contact between the electrode and skin with body motion. The aim of this study was to suggest a design for ECG-monitoring clothing contributing to reduction of motion artifacts. Based on the clothing science theory, it was assumed in this study that the stability of the electrode in a dynamic state differed depending on the electrode location in an ECG-monitoring garment. Founded on this assumption, effects of 56 electrode positions were determined by sectioning the surface of the garment into grids with 6 cm intervals in the front and back of the bodice. In order to determine the optimal locations of the ECG electrodes from the 56 positions, ECG measurements were collected from 10 participants at every electrode position in the garment while the wearer was in motion. The electrode locations indicating both an ECG measurement rate higher than 80.0 % and a large amplitude during motion were selected as the optimal electrode locations. The results of this analysis show four electrode locations with consistently higher ECG measurement rates and larger amplitudes amongst the 56 locations. These four locations were abstracted to be least affected by wearer movement in this research. Based on this result, a design of the garment-formed ECG monitoring platform

PIC microcontroller-based RF wireless ECG monitoring system.

Science.gov (United States)

Oweis, R J; Barhoum, A

2007-01-01

This paper presents a radio-telemetry system that provides the possibility of ECG signal transmission from a patient detection circuit via an RF data link. A PC then receives the signal through the National Instrument data acquisition card (NIDAQ). The PC is equipped with software allowing the received ECG signals to be saved, analysed, and sent by email to another part of the world. The proposed telemetry system consists of a patient unit and a PC unit. The amplified and filtered ECG signal is sampled 360 times per second, and the A/D conversion is performed by a PIC16f877 microcontroller. The major contribution of the final proposed system is that it detects, processes and sends patients ECG data over a wireless RF link to a maximum distance of 200 m. Transmitted ECG data with different numbers of samples were received, decoded by means of another PIC microcontroller, and displayed using MATLAB program. The designed software is presented in a graphical user interface utility.

Image Processing and Features Extraction of Fingerprint Images ...

African Journals Online (AJOL)

To demonstrate the importance of the image processing of fingerprint images prior to image enrolment or comparison, the set of fingerprint images in databases (a) and (b) of the FVC (Fingerprint Verification Competition) 2000 database were analyzed using a features extraction algorithm. This paper presents the results of ...

Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

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Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chung-Hsien

2017-09-01

Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

Javanese Character Feature Extraction Based on Shape Energy

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Galih Hendra Wibowo

2017-07-01

Full Text Available Javanese character is one of Indonesia's noble culture, especially in Java. However, the number of Javanese people who are able to read the letter has decreased so that there need to be conservation efforts in the form of a system that is able to recognize the characters. One solution to these problem lies in Optical Character Recognition (OCR studies, where one of its heaviest points lies in feature extraction which is to distinguish each character. Shape Energy is one of feature extraction method with the basic idea of how the character can be distinguished simply through its skeleton. Based on the basic idea, then the development of feature extraction is done based on its components to produce an angular histogram with various variations of multiples angle. Furthermore, the performance test of this method and its basic method is performed in Javanese character dataset, which has been obtained from various images, is 240 data with 19 labels by using K-Nearest Neighbors as its classification method. Performance values were obtained based on the accuracy which is generated through the Cross-Validation process of 80.83% in the angular histogram with an angle of 20 degrees, 23% better than Shape Energy. In addition, other test results show that this method is able to recognize rotated character with the lowest performance value of 86% at 180-degree rotation and the highest performance value of 96.97% at 90-degree rotation. It can be concluded that this method is able to improve the performance of Shape Energy in the form of recognition of Javanese characters as well as robust to the rotation.

A wearable 12-lead ECG acquisition system with fabric electrodes.

Science.gov (United States)

Haoshi Zhang; Lan Tian; Huiyang Lu; Ming Zhou; Haiqing Zou; Peng Fang; Fuan Yao; Guanglin Li

2017-07-01

Continuous electrocardiogram (ECG) monitoring is significant for prevention of heart disease and is becoming an important part of personal and family health care. In most of the existing wearable solutions, conventional metal sensors and corresponding chips are simply integrated into clothes and usually could only collect few leads of ECG signals that could not provide enough information for diagnosis of cardiac diseases such as arrhythmia and myocardial ischemia. In this study, a wearable 12-lead ECG acquisition system with fabric electrodes was developed and could simultaneously process 12 leads of ECG signals. By integrating the fabric electrodes into a T-shirt, the wearable system would provide a comfortable and convenient user interface for ECG recording. For comparison, the proposed fabric electrode and the gelled traditional metal electrodes were used to collect ECG signals on a subject, respectively. The approximate entropy (ApEn) of ECG signals from both types of electrodes were calculated. The experimental results show that the fabric electrodes could achieve similar performance as the gelled metal electrodes. This preliminary work has demonstrated that the developed ECG system with fabric electrodes could be utilized for wearable health management and telemedicine applications.

Biometric and Emotion Identification: An ECG Compression Based Method

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Susana Brás

2018-04-01

Full Text Available We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG. The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1 conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2 conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3 identification of the ECG record class, using a 1-NN (nearest neighbor classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.

Biometric and Emotion Identification: An ECG Compression Based Method

Science.gov (United States)

Brás, Susana; Ferreira, Jacqueline H. T.; Soares, Sandra C.; Pinho, Armando J.

2018-01-01

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model. PMID:29670564

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System.

Science.gov (United States)

Kwon, Sungjun; Lee, Dongseok; Kim, Jeehoon; Lee, Youngki; Kang, Seungwoo; Seo, Sangwon; Park, Kwangsuk

2016-03-11

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user's ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user's high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user's daily smartphone use.

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System

Directory of Open Access Journals (Sweden)

Sungjun Kwon

2016-03-01

Full Text Available In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG monitoring system, Sinabro, which monitors a user’s ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR and heart rate variability (HRV, to support the pervasive healthcare apps for smartphones based on the user’s high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1 upgrading the sensor device; (2 improving the feature extraction process; and (3 evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user’s daily smartphone use.

The Development of a Portable ECG Monitor Based on DSP

Science.gov (United States)

Nan, CHI Jian; Tao, YAN Yan; Meng Chen, LIU; Li, YANG

With the advent of global information, researches of Smart Home system are in the ascendant, the ECG real-time detection, and wireless transmission of ECG become more useful. In order to achieve the purpose we developed a portable ECG monitor which achieves the purpose of cardiac disease remote monitoring, and will be used in the physical and psychological disease surveillance in smart home system, we developed this portable ECG Monitor, based on the analysis of existing ECG Monitor, using TMS320F2812 as the core controller, which complete the signal collection, storage, processing, waveform display and transmission.

A feature extraction algorithm based on corner and spots in self-driving vehicles

Directory of Open Access Journals (Sweden)

Yupeng FENG

2017-06-01

Full Text Available To solve the poor real-time performance problem of the visual odometry based on embedded system with limited computing resources, an image matching method based on Harris and SIFT is proposed, namely the Harris-SIFT algorithm. On the basis of the review of SIFT algorithm, the principle of Harris-SIFT algorithm is provided. First, Harris algorithm is used to extract the corners of the image as candidate feature points, and scale invariant feature transform (SIFT features are extracted from those candidate feature points. At last, through an example, the algorithm is simulated by Matlab, then the complexity and other performance of the algorithm are analyzed. The experimental results show that the proposed method reduces the computational complexity and improves the speed of feature extraction. Harris-SIFT algorithm can be used in the real-time vision odometer system, and will bring about a wide application of visual odometry in embedded navigation system.

Chemical-induced disease relation extraction with various linguistic features.

Science.gov (United States)

Gu, Jinghang; Qian, Longhua; Zhou, Guodong

2016-01-01

Understanding the relations between chemicals and diseases is crucial in various biomedical tasks such as new drug discoveries and new therapy developments. While manually mining these relations from the biomedical literature is costly and time-consuming, such a procedure is often difficult to keep up-to-date. To address these issues, the BioCreative-V community proposed a challenging task of automatic extraction of chemical-induced disease (CID) relations in order to benefit biocuration. This article describes our work on the CID relation extraction task on the BioCreative-V tasks. We built a machine learning based system that utilized simple yet effective linguistic features to extract relations with maximum entropy models. In addition to leveraging various features, the hypernym relations between entity concepts derived from the Medical Subject Headings (MeSH)-controlled vocabulary were also employed during both training and testing stages to obtain more accurate classification models and better extraction performance, respectively. We demoted relation extraction between entities in documents to relation extraction between entity mentions. In our system, pairs of chemical and disease mentions at both intra- and inter-sentence levels were first constructed as relation instances for training and testing, then two classification models at both levels were trained from the training examples and applied to the testing examples. Finally, we merged the classification results from mention level to document level to acquire final relations between chemicals and diseases. Our system achieved promisingF-scores of 60.4% on the development dataset and 58.3% on the test dataset using gold-standard entity annotations, respectively. Database URL:https://github.com/JHnlp/BC5CIDTask. © The Author(s) 2016. Published by Oxford University Press.

Analysis of Time n Frequency EEG Feature Extraction Methods for Mental Task Classification

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Caglar Uyulan

2017-01-01

Full Text Available Many endogenous and external components may affect the physiological, mental and behavioral states in humans. Monitoring tools are required to evaluate biomarkers, identify biological events, and predict their outcomes. Being one of the valuable indicators, brain biomarkers derived from temporal or spectral electroencephalography (EEG signals processing, allow for the classification of mental disorders and mental tasks. An EEG signal has a nonstationary nature and individual frequency feature, hence it can be concluded that each subject has peculiar timing and data to extract unique features. In order to classify data, which are collected by performing four mental task (reciting the alphabet backwards, imagination of rotation of a cube, imagination of right hand movements (open/close and performing mathematical operations, discriminative features were extracted using four competitive time-frequency techniques; Wavelet Packet Decomposition (WPD, Morlet Wavelet Transform (MWT, Short Time Fourier Transform (STFT and Wavelet Filter Bank (WFB, respectively. The extracted features using both time and frequency domain information were then reduced using a principal component analysis for subset reduction. Finally, the reduced subsets were fed into a multi-layer perceptron neural network (MP-NN trained with back propagation (BP algorithm to generate a predictive model. This study mainly focuses on comparing the relative performance of time-frequency feature extraction methods that are used to classify mental tasks. The real-time (RT conducted experimental results underlined that the WPD feature extraction method outperforms with 92% classification accuracy compared to three other aforementioned methods for four different mental tasks.

Detection of ST-T Episode Based on the Global Curvature of Isoelectric Level in ECG

Energy Technology Data Exchange (ETDEWEB)

Kang, D. W.; Jun, D. G.; Lee, K. J.; Yoon, H. R. [Yonsei University, Seoul (Korea)

2001-04-01

This paper describes an automated detection algorithm of ST-T episodes using global curvature which can connect the isoelectric level in ECG and can eliminate not only the slope of ST segment, but also difference of the baseline and global curve. This above method of baseline correction is very faster than classical baseline correction methods. The optimal values of parameters for baseline correction were found as the value having the highest detection rate of ST episode. The features as input of backpropagation Neural Network were extracted from the whole ST segment. The European ST-T database was used as training and test data. Finally, ST elevation, ST depression and normal ST were classified. The average ST episode sensitivity and predictivity were 85.42%, 80.29%, respectively. This result shows the high speed and reliability in ST episode detection. In conclusion, the proposed method showed the possibility in various applications for the Holter system. (author). 17 refs., 5 figs., 4 tabs.

Usefulness of exercise ECG test with nitroglycerin and exercise cardiac scintigraphy in patients with false positive exercise ECG test

International Nuclear Information System (INIS)

Moritani, Kohshiro

1984-01-01

The purpose of this study is to evaluate the clinical usefulness of exercise (Ex) ECG test with sublingual nitroglycerin (NTG) and Ex cardiac scintigraphy in differentiating false positive responses from true positive responses of Ex ECG test. We examined 7 pts (age : 46+-7 years) with true positive Ex ECG test (TP) and 8 pts (age : 55+-10 years) with false positive Ex ECG test (FP). TP had significant coronary artery disease and FP did not. Ex test was done by multistage ergometer test. In 5 pts of TP and all pts of FP, Ex cardiac scintigraphy was performed. In TP, Ex cardiac scintigraphy revealed reversible perfusion deficit, but not in FP. NTG was administered 3 minutes before Ex test was started. Ex test with NTG was terminated at the same load as Ex test without NTG. Pressure-rate products at the end point of Ex test did not show significant difference between Ex test without NTG and that with NTG (TP: 203x10 2 , 213x10 2 , FP: 196x10 2 , 206x10 2 , respectively). In 7 pts of FP, ST depression in Ex test without NTG was not improved in Ex test with NTG. On the other hand, in all pts of TP, ST depression seen in Ex test without NTG, was not observed in Ex test with NTG. It may be concluded that Ex cardiac scintigraphy is diagnostic for differentiation of false positive responses from true positive responses of Ex ECG test, as well as Ex ECG test with NTG is. (author)

Prehospital ECG transmission: comparison of advanced mobile phone and facsimile devices in an urban Emergency Medical Service System.

Science.gov (United States)

Väisänen, Olli; Mäkijärvi, Markku; Silfvast, Tom

2003-05-01

To compare the speed and reliability of electrocardiogram (ECG) transmissions from the prehospital setting to a conventional table facsimile device and to an advanced mobile phone in a Helicopter Emergency Medical Service System (HEMS). Eighteen authentic ECGs stored in the memory module of a monitor defibrillator were used. The ECGs were (1) sent directly from the monitor defibrillator to a table fax and an advanced mobile phone at the HEMS base; (2) printed out and sent from a mobile fax connected to an ordinary mobile phone to the table fax and the advanced mobile phone at the HEMS base; (3) printed out and sent from an ordinary table fax as well as from a table fax connected to a satellite phone system to the receiving devices at the HEMS base. When the ECGs were sent from the table fax via satellite, the transmission times were longer to the advanced mobile phone than to the table fax at the HEMS base (1 min 54 s+/-0 min 21 s vs. 1 min 37 s+/-0 min 20 s, (mean+/-SD), (Ptransmission from the other fax devices, there were no differences in transmission times between the two receiving devices. The fastest way to transmit ECGs to the advanced mobile phone was to send it from conventional table fax (1 min 22 s+/-0 min 18 s) and the longest transmission times were with mobile fax connected to mobile phone (5 min 23 s+/-3 min 5 s). In all ECGs transmitted except one the cardiac rhythm and ST-changes could be recognised. An advanced mobile phone is as fast and reliable as a conventional table fax in receiving ECGs. A mobile phone with advanced features is a practical tool for HEMS physicians who need to evaluate ECGs in the prehospital setting.

From Pacemaker to Wearable: Techniques for ECG Detection Systems.

Science.gov (United States)

Kumar, Ashish; Komaragiri, Rama; Kumar, Manjeet

2018-01-11

With the alarming rise in the deaths due to cardiovascular diseases (CVD), present medical research scenario places notable importance on techniques and methods to detect CVDs. As adduced by world health organization, technological proceeds in the field of cardiac function assessment have become the nucleus and heart of all leading research studies in CVDs in which electrocardiogram (ECG) analysis is the most functional and convenient tool used to test the range of heart-related irregularities. Most of the approaches present in the literature of ECG signal analysis consider noise removal, rhythm-based analysis, and heartbeat detection to improve the performance of a cardiac pacemaker. Advancements achieved in the field of ECG segments detection and beat classification have a limited evaluation and still require clinical approvals. In this paper, approaches on techniques to implement on-chip ECG detector for a cardiac pacemaker system are discussed. Moreover, different challenges regarding the ECG signal morphology analysis deriving from medical literature is extensively reviewed. It is found that robustness to noise, wavelet parameter choice, numerical efficiency, and detection performance are essential performance indicators required by a state-of-the-art ECG detector. Furthermore, many algorithms described in the existing literature are not verified using ECG data from the standard databases. Some ECG detection algorithms show very high detection performance with the total number of detected QRS complexes. However, the high detection performance of the algorithm is verified using only a few datasets. Finally, gaps in current advancements and testing are identified, and the primary challenge remains to be implementing bullseye test for morphology analysis evaluation.

SAR Data Fusion Imaging Method Oriented to Target Feature Extraction

Directory of Open Access Journals (Sweden)

Yang Wei

2015-02-01

Full Text Available To deal with the difficulty for target outlines extracting precisely due to neglect of target scattering characteristic variation during the processing of high-resolution space-borne SAR data, a novel fusion imaging method is proposed oriented to target feature extraction. Firstly, several important aspects that affect target feature extraction and SAR image quality are analyzed, including curved orbit, stop-and-go approximation, atmospheric delay, and high-order residual phase error. Furthermore, the corresponding compensation methods are addressed as well. Based on the analysis, the mathematical model of SAR echo combined with target space-time spectrum is established for explaining the space-time-frequency change rule of target scattering characteristic. Moreover, a fusion imaging strategy and method under high-resolution and ultra-large observation angle range conditions are put forward to improve SAR quality by fusion processing in range-doppler and image domain. Finally, simulations based on typical military targets are used to verify the effectiveness of the fusion imaging method.

Sensor-Based Auto-Focusing System Using Multi-Scale Feature Extraction and Phase Correlation Matching

Directory of Open Access Journals (Sweden)

Jinbeum Jang

2015-03-01

Full Text Available This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF algorithm consists of four steps: (i acquisition of left and right images using AF points in the region-of-interest; (ii feature extraction in the left image under low illumination and out-of-focus blur; (iii the generation of two feature images using the phase difference between the left and right images; and (iv estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems.

Design of Secure ECG-Based Biometric Authentication in Body Area Sensor Networks.

Science.gov (United States)

Peter, Steffen; Reddy, Bhanu Pratap; Momtaz, Farshad; Givargis, Tony

2016-04-22

Body area sensor networks (BANs) utilize wireless communicating sensor nodes attached to a human body for convenience, safety, and health applications. Physiological characteristics of the body, such as the heart rate or Electrocardiogram (ECG) signals, are promising means to simplify the setup process and to improve security of BANs. This paper describes the design and implementation steps required to realize an ECG-based authentication protocol to identify sensor nodes attached to the same human body. Therefore, the first part of the paper addresses the design of a body-area sensor system, including the hardware setup, analogue and digital signal processing, and required ECG feature detection techniques. A model-based design flow is applied, and strengths and limitations of each design step are discussed. Real-world measured data originating from the implemented sensor system are then used to set up and parametrize a novel physiological authentication protocol for BANs. The authentication protocol utilizes statistical properties of expected and detected deviations to limit the number of false positive and false negative authentication attempts. The result of the described holistic design effort is the first practical implementation of biometric authentication in BANs that reflects timing and data uncertainties in the physical and cyber parts of the system.

Design of Secure ECG-Based Biometric Authentication in Body Area Sensor Networks

Science.gov (United States)

Peter, Steffen; Pratap Reddy, Bhanu; Momtaz, Farshad; Givargis, Tony

2016-01-01

Body area sensor networks (BANs) utilize wireless communicating sensor nodes attached to a human body for convenience, safety, and health applications. Physiological characteristics of the body, such as the heart rate or Electrocardiogram (ECG) signals, are promising means to simplify the setup process and to improve security of BANs. This paper describes the design and implementation steps required to realize an ECG-based authentication protocol to identify sensor nodes attached to the same human body. Therefore, the first part of the paper addresses the design of a body-area sensor system, including the hardware setup, analogue and digital signal processing, and required ECG feature detection techniques. A model-based design flow is applied, and strengths and limitations of each design step are discussed. Real-world measured data originating from the implemented sensor system are then used to set up and parametrize a novel physiological authentication protocol for BANs. The authentication protocol utilizes statistical properties of expected and detected deviations to limit the number of false positive and false negative authentication attempts. The result of the described holistic design effort is the first practical implementation of biometric authentication in BANs that reflects timing and data uncertainties in the physical and cyber parts of the system. PMID:27110785

Design of Secure ECG-Based Biometric Authentication in Body Area Sensor Networks

Directory of Open Access Journals (Sweden)

Steffen Peter

2016-04-01

Full Text Available Body area sensor networks (BANs utilize wireless communicating sensor nodes attached to a human body for convenience, safety, and health applications. Physiological characteristics of the body, such as the heart rate or Electrocardiogram (ECG signals, are promising means to simplify the setup process and to improve security of BANs. This paper describes the design and implementation steps required to realize an ECG-based authentication protocol to identify sensor nodes attached to the same human body. Therefore, the first part of the paper addresses the design of a body-area sensor system, including the hardware setup, analogue and digital signal processing, and required ECG feature detection techniques. A model-based design flow is applied, and strengths and limitations of each design step are discussed. Real-world measured data originating from the implemented sensor system are then used to set up and parametrize a novel physiological authentication protocol for BANs. The authentication protocol utilizes statistical properties of expected and detected deviations to limit the number of false positive and false negative authentication attempts. The result of the described holistic design effort is the first practical implementation of biometric authentication in BANs that reflects timing and data uncertainties in the physical and cyber parts of the system.

Heart Rate Variability and Wavelet-based Studies on ECG Signals from Smokers and Non-smokers

Science.gov (United States)

Pal, K.; Goel, R.; Champaty, B.; Samantray, S.; Tibarewala, D. N.

2013-12-01

The current study deals with the heart rate variability (HRV) and wavelet-based ECG signal analysis of smokers and non-smokers. The results of HRV indicated dominance towards the sympathetic nervous system activity in smokers. The heart rate was found to be higher in case of smokers as compared to non-smokers ( p smokers from the non-smokers. The results indicated that when RMSSD, SD1 and RR-mean features were used concurrently a classification efficiency of > 90 % was achieved. The wavelet decomposition of the ECG signal was done using the Daubechies (db 6) wavelet family. No difference was observed between the smokers and non-smokers which apparently suggested that smoking does not affect the conduction pathway of heart.

ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study

Czech Academy of Sciences Publication Activity Database

Maršánová, L.; Ronzhina, M.; Smíšek, Radovan; Vítek, M.; Němcová, A.; Smítal, L.; Nováková, M.

2017-01-01

Roč. 7, SEP (2017), s. 1-11, č. článku 11239. ISSN 2045-2322 R&D Projects: GA ČR GAP102/12/2034 Institutional support: RVO:68081731 Keywords : stress-induced ischemia * ECG * arrhytmias Subject RIV: FS - Medical Facilities ; Equipment OBOR OECD: Medical engineering Impact factor: 4.259, year: 2016 http://www.nature.com/ articles /s41598-017-10942-6

Microcontroller-based underwater acoustic ECG telemetry system.

Science.gov (United States)

Istepanian, R S; Woodward, B

1997-06-01

This paper presents a microcontroller-based underwater acoustic telemetry system for digital transmission of the electrocardiogram (ECG). The system is designed for the real time, through-water transmission of data representing any parameter, and it was used initially for transmitting in multiplexed format the heart rate, breathing rate and depth of a diver using self-contained underwater breathing apparatus (SCUBA). Here, it is used to monitor cardiovascular reflexes during diving and swimming. The programmable capability of the system provides an effective solution to the problem of transmitting data in the presence of multipath interference. An important feature of the paper is a comparative performance analysis of two encoding methods, Pulse Code Modulation (PCM) and Pulse Position Modulation (PPM).

Development of a portable wireless system for bipolar concentric ECG recording

International Nuclear Information System (INIS)

Prats-Boluda, G; Ye-Lin, Y; Bueno Barrachina, J M; Senent, E; Rodriguez de Sanabria, R; Garcia-Casado, J

2015-01-01

Cardiovascular diseases (CVDs) remain the biggest cause of deaths worldwide. ECG monitoring is a key tool for early diagnosis of CVDs. Conventional monitors use monopolar electrodes resulting in poor spatial resolution surface recordings and requiring extensive wiring. High-spatial resolution surface electrocardiographic recordings provide valuable information for the diagnosis of a wide range of cardiac abnormalities, including infarction and arrhythmia. The aim of this work was to develop and test a wireless recording system for acquiring high spatial resolution ECG signals, based on a flexible tripolar concentric electrode (TCE) without cable wiring or external reference electrode which would make more comnfortable its use in clinical practice. For this, a portable, wireless sensor node for analogue conditioning, digitalization and transmission of a bipolar concentric ECG signal (BC-ECG) using a TCE and a Mason-likar Lead-I ECG (ML-Lead-I ECG) signal was developed. Experimental results from a total of 32 healthy volunteers showed that the ECG fiducial points in the BC-ECG signals, recorded with external and internal reference electrode, are consistent with those of simultaneous ML-Lead-I ECG. No statistically significant difference was found in either signal amplitude or morphology, regardless of the reference electrode used, being the signal-to-noise similar to that of ML-Lead-I ECG. Furthermore, it has been observed that BC-ECG signals contain information that could not available in conventional records, specially related to atria activity. The proposed wireless sensor node provides non-invasive high-local resolution ECG signals using only a TCE without additional wiring, which would have great potential in medical diagnosis of diseases such as atrial or ventricular fibrillations or arrhythmias that currently require invasive diagnostic procedures (catheterization). (paper)

FastICA peel-off for ECG interference removal from surface EMG.

Science.gov (United States)

Chen, Maoqi; Zhang, Xu; Chen, Xiang; Zhu, Mingxing; Li, Guanglin; Zhou, Ping

2016-06-13

Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart. A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a "peel off" processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded "clean" surface EMG and "pure" ECG interference. It was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference. The proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings.

[Study for portable dynamic ECG monitor and recorder].

Science.gov (United States)

Yang, Pengcheng; Li, Yongqin; Chen, Bihua

2012-09-01

This Paper presents a portable dynamic ECG monitor system based on MSP430F149 microcontroller. The electrocardiogram detecting system consists of ECG detecting circuit, man-machine interaction module, MSP430F149 and upper computer software. The ECG detecting circuit including a preamplifier, second-order Butterworth low-pass filter, high-pass filter, and 50Hz trap circuit to detects electrocardiogram and depresses various kinds of interference effectively. A microcontroller is used to collect three channel analog signals which can be displayed on TFT LCD. A SD card is used to record real-time data continuously and implement the FTA16 file system. In the end, a host computer system interface is also designed to analyze the ECG signal and the analysis results can provide diagnosis references to clinical doctors.

Feature extraction from mammographic images using fast marching methods

International Nuclear Information System (INIS)

Bottigli, U.; Golosio, B.

2002-01-01

Features extraction from medical images represents a fundamental step for shape recognition and diagnostic support. The present work faces the problem of the detection of large features, such as massive lesions and organ contours, from mammographic images. The regions of interest are often characterized by an average grayness intensity that is different from the surrounding. In most cases, however, the desired features cannot be extracted by simple gray level thresholding, because of image noise and non-uniform density of the surrounding tissue. In this work, edge detection is achieved through the fast marching method (Level Set Methods and Fast Marching Methods, Cambridge University Press, Cambridge, 1999), which is based on the theory of interface evolution. Starting from a seed point in the shape of interest, a front is generated which evolves according to an appropriate speed function. Such function is expressed in terms of geometric properties of the evolving interface and of image properties, and should become zero when the front reaches the desired boundary. Some examples of application of such method to mammographic images from the CALMA database (Nucl. Instr. and Meth. A 460 (2001) 107) are presented here and discussed

Feature extraction for SAR target recognition based on supervised manifold learning

International Nuclear Information System (INIS)

Du, C; Zhou, S; Sun, J; Zhao, J

2014-01-01

On the basis of manifold learning theory, a new feature extraction method for Synthetic aperture radar (SAR) target recognition is proposed. First, the proposed algorithm estimates the within-class and between-class local neighbourhood surrounding each SAR sample. After computing the local tangent space for each neighbourhood, the proposed algorithm seeks for the optimal projecting matrix by preserving the local within-class property and simultaneously maximizing the local between-class separability. The use of uncorrelated constraint can also enhance the discriminating power of the optimal projecting matrix. Finally, the nearest neighbour classifier is applied to recognize SAR targets in the projected feature subspace. Experimental results on MSTAR datasets demonstrate that the proposed method can provide a higher recognition rate than traditional feature extraction algorithms in SAR target recognition

Near Field Communication-based telemonitoring with integrated ECG recordings.

Science.gov (United States)

Morak, J; Kumpusch, H; Hayn, D; Leitner, M; Scherr, D; Fruhwald, F M; Schreier, G

2011-01-01

Telemonitoring of vital signs is an established option in treatment of patients with chronic heart failure (CHF). In order to allow for early detection of atrial fibrillation (AF) which is highly prevalent in the CHF population telemonitoring programs should include electrocardiogram (ECG) signals. It was therefore the aim to extend our current home monitoring system based on mobile phones and Near Field Communication technology (NFC) to enable patients acquiring their ECG signals autonomously in an easy-to-use way. We prototypically developed a sensing device for the concurrent acquisition of blood pressure and ECG signals. The design of the device equipped with NFC technology and Bluetooth allowed for intuitive interaction with a mobile phone based patient terminal. This ECG monitoring system was evaluated in the course of a clinical pilot trial to assess the system's technical feasibility, usability and patient's adherence to twice daily usage. 21 patients (4f, 54 ± 14 years) suffering from CHF were included in the study and were asked to transmit two ECG recordings per day via the telemonitoring system autonomously over a monitoring period of seven days. One patient dropped out from the study. 211 data sets were transmitted over a cumulative monitoring period of 140 days (overall adherence rate 82.2%). 55% and 8% of the transmitted ECG signals were sufficient for ventricular and atrial rhythm assessment, respectively. Although ECG signal quality has to be improved for better AF detection the developed communication design of joining Bluetooth and NFC technology in our telemonitoring system allows for ambulatory ECG acquisition with high adherence rates and system usability in heart failure patients.

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

Directory of Open Access Journals (Sweden)

Kai Liu

2018-03-01

Full Text Available The separation of coal and gangue is an important process of the coal preparation technology. The conventional way of manual selection and separation of gangue from the raw coal can be replaced by computer vision technology. In the literature, research on image recognition and classification of coal and gangue is mainly based on the grayscale and texture features of the coal and gangue. However, there are few studies on characteristics of coal and gangue from the perspective of their outline differences. Therefore, the multifractal detrended fluctuation analysis (MFDFA method is introduced in this paper to extract the geometric features of coal and gangue. Firstly, the outline curves of coal and gangue in polar coordinates are detected and achieved along the centroid, thereby the multifractal characteristics of the series are analyzed and compared. Subsequently, the modified local singular spectrum widths Δ h of the outline curve series are extracted as the characteristic variables of the coal and gangue for pattern recognition. Finally, the extracted geometric features by MFDFA combined with the grayscale and texture features of the images are compared with other methods, indicating that the recognition rate of coal gangue images can be increased by introducing the geometric features.

Electrocardiographic features of sarcomere mutation carriers with and without clinically overt hypertrophic cardiomyopathy

DEFF Research Database (Denmark)

Lakdawala, Neal K; Thune, Jens Jakob; Maron, Barry J

2011-01-01

In hypertrophic cardiomyopathy (HC), electrocardiographic (ECG) changes have been postulated to be an early marker of disease, detectable in sarcomere mutation carriers when left ventricular (LV) wall thickness is still normal. However, the ECG features of mutation carriers have not been fully...

[Implementation of ECG Monitoring System Based on Internet of Things].

Science.gov (United States)

Lu, Liangliang; Chen, Minya

2015-11-01

In order to expand the capabilities of hospital's traditional ECG device and enhance medical staff's work efficiency, an ECG monitoring system based on internet of things is introduced. The system can monitor ECG signals in real time and analyze data using ECG sensor, PDA, Web servers, which embeds C language, Android systems, .NET, wireless network and other technologies. After experiments, it can be showed that the system has high reliability and stability and can bring the convenience to medical staffs.

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)

Sticker-type ECG/PPG concurrent monitoring system hybrid integration of CMOS SoC and organic sensor device.

Science.gov (United States)

Yongsu Lee; Hyeonwoo Lee; Seunghyup Yoo; Hoi-Jun Yoo

2016-08-01

The sticker-type sensor system is proposed targeting ECG/PPG concurrent monitoring for cardiovascular diseases. The stickers are composed of two types: Hub and Sensor-node (SN) sticker. Low-power CMOS SoC for measuring ECG and PPG signal is hybrid integrated with organic light emitting diodes (OLEDs) and organic photo detector (OPD). The sticker has only 2g weight and only consumes 141μW. The optical calibration loop is adopted for maintaining SNR of PPG signal higher than 30dB. The pulse arrival time (PAT) and SpO2 value can be extracted from various body parts and verified comparing with the reference device from 20 people in-vivo experiments.

Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces.

Science.gov (United States)

Yang, Banghua; Li, Huarong; Wang, Qian; Zhang, Yunyuan

2016-06-01

Feature extraction of electroencephalogram (EEG) plays a vital role in brain-computer interfaces (BCIs). In recent years, common spatial pattern (CSP) has been proven to be an effective feature extraction method. However, the traditional CSP has disadvantages of requiring a lot of input channels and the lack of frequency information. In order to remedy the defects of CSP, wavelet packet decomposition (WPD) and CSP are combined to extract effective features. But WPD-CSP method considers less about extracting specific features that are fitted for the specific subject. So a subject-based feature extraction method using fisher WPD-CSP is proposed in this paper. The idea of proposed method is to adapt fisher WPD-CSP to each subject separately. It mainly includes the following six steps: (1) original EEG signals from all channels are decomposed into a series of sub-bands using WPD; (2) average power values of obtained sub-bands are computed; (3) the specified sub-bands with larger values of fisher distance according to average power are selected for that particular subject; (4) each selected sub-band is reconstructed to be regarded as a new EEG channel; (5) all new EEG channels are used as input of the CSP and a six-dimensional feature vector is obtained by the CSP. The subject-based feature extraction model is so formed; (6) the probabilistic neural network (PNN) is used as the classifier and the classification accuracy is obtained. Data from six subjects are processed by the subject-based fisher WPD-CSP, the non-subject-based fisher WPD-CSP and WPD-CSP, respectively. Compared with non-subject-based fisher WPD-CSP and WPD-CSP, the results show that the proposed method yields better performance (sensitivity: 88.7±0.9%, and specificity: 91±1%) and the classification accuracy from subject-based fisher WPD-CSP is increased by 6-12% and 14%, respectively. The proposed subject-based fisher WPD-CSP method can not only remedy disadvantages of CSP by WPD but also discriminate

ECG changes after a session of regional intraarterial hyperglycemia

International Nuclear Information System (INIS)

Korobchenko, Z.A.; Livshits, L.I.

1988-01-01

ECG changes after a session of regional intraarterial hyperglycemia (RIH) in 13 patients (the mean age of 49 years) with locally advanced cancer of the tongue, oral mucosa and oropharynx were presented. Taking into account the mean age of patients and the negative ECG time course after a RIH session, the necessity of patients' examination (including ECG after a RIH session and, when indicated, a consultation by a cardiologist) was emphasized

Feasibility of epicardial adipose tissue quantification in non-ECG-gated low-radiation-dose CT: comparison with prospectively ECG-gated cardiac CT

Energy Technology Data Exchange (ETDEWEB)

Simon-Yarza, Isabel; Viteri-Ramirez, Guillermo; Saiz-Mendiguren, Ramon; Slon-Roblero, Pedro J.; Paramo, Maria [Dept. of Radiology, Clinica Univ. de Navarra, Pamplona (Spain); Bastarrika, Gorka [Dept. of Radiology, Clinica Univ. de Navarra, Pamplona (Spain); Cardiac Imaging Unit, Clinica Univ. de Navarra, Pamplona (Spain)], e-mail: bastarrika@unav.es

2012-06-15

Background: Epicardial adipose tissue (EAT) is an important indicator of cardiovascular risk. This parameter is generally assessed on ECG-gated computed tomography (CT) images. Purpose: To evaluate feasibility and reliability of EAT quantification on non-gated thoracic low-radiation-dose CT examinations with respect to prospectively ECG-gated cardiac CT acquisition. Material and Methods: Sixty consecutive asymptomatic smokers (47 men; mean age 64 {+-} 9.8 years) underwent low-dose CT of the chest and prospectively ECG-gated cardiac CT acquisitions (64-slice dual-source CT). The two examinations were reconstructed with the same range, field of view, slice thickness, and convolution algorithm. Two independent observers blindly quantified EAT volume using commercially available software. Data were compared with paired sample Student t-test, concordance correlation coefficients (CCC), and Bland-Altman plots. Results: No statistically significant difference was observed for EAT volume quantification with low-dose-CT (141.7 {+-} 58.3 mL) with respect to ECG-gated CT (142.7 {+-} 57.9 mL). Estimation of CCC showed almost perfect concordance between the two techniques for EAT-volume assessment (CCC, 0.99; mean difference, 0.98 {+-} 5.1 mL). Inter-observer agreement for EAT volume estimation was CCC: 0.96 for low-dose-CT examinations and 0.95 for ECG-gated CT. Conclusion: Non-gated low-dose CT allows quantifying EAT with almost the same concordance and reliability as using dedicated prospectively ECG-gated cardiac CT acquisition protocols.

Feasibility of epicardial adipose tissue quantification in non-ECG-gated low-radiation-dose CT: comparison with prospectively ECG-gated cardiac CT

International Nuclear Information System (INIS)

Simon-Yarza, Isabel; Viteri-Ramirez, Guillermo; Saiz-Mendiguren, Ramon; Slon-Roblero, Pedro J.; Paramo, Maria; Bastarrika, Gorka

2012-01-01

Background: Epicardial adipose tissue (EAT) is an important indicator of cardiovascular risk. This parameter is generally assessed on ECG-gated computed tomography (CT) images. Purpose: To evaluate feasibility and reliability of EAT quantification on non-gated thoracic low-radiation-dose CT examinations with respect to prospectively ECG-gated cardiac CT acquisition. Material and Methods: Sixty consecutive asymptomatic smokers (47 men; mean age 64 ± 9.8 years) underwent low-dose CT of the chest and prospectively ECG-gated cardiac CT acquisitions (64-slice dual-source CT). The two examinations were reconstructed with the same range, field of view, slice thickness, and convolution algorithm. Two independent observers blindly quantified EAT volume using commercially available software. Data were compared with paired sample Student t-test, concordance correlation coefficients (CCC), and Bland-Altman plots. Results: No statistically significant difference was observed for EAT volume quantification with low-dose-CT (141.7 ± 58.3 mL) with respect to ECG-gated CT (142.7 ± 57.9 mL). Estimation of CCC showed almost perfect concordance between the two techniques for EAT-volume assessment (CCC, 0.99; mean difference, 0.98 ± 5.1 mL). Inter-observer agreement for EAT volume estimation was CCC: 0.96 for low-dose-CT examinations and 0.95 for ECG-gated CT. Conclusion: Non-gated low-dose CT allows quantifying EAT with almost the same concordance and reliability as using dedicated prospectively ECG-gated cardiac CT acquisition protocols

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

International Nuclear Information System (INIS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-01-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification. (paper)

Breast cancer mitosis detection in histopathological images with spatial feature extraction

Science.gov (United States)

Albayrak, Abdülkadir; Bilgin, Gökhan

2013-12-01

In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

Artifact reduction in maternal abdominal ECG recordings for fetal ECG estimation.

NARCIS (Netherlands)

Vullings, R.; Peters, C.H.L.; Mischi, M.; Sluijter, R.J.; Oei, S.G.; Bergmans, J.W.M.

2010-01-01

Monitoring the fetal electrocardiogram (1ECG) is currently one of the most promising methods to assess fetal health. However, the main problem associated with this method is that the signals recorded from the maternal abdomen are affected by noise and interferences: the maternal electrocardiogram

Sparse kernel orthonormalized PLS for feature extraction in large datasets

DEFF Research Database (Denmark)

Arenas-García, Jerónimo; Petersen, Kaare Brandt; Hansen, Lars Kai

2006-01-01

In this paper we are presenting a novel multivariate analysis method for large scale problems. Our scheme is based on a novel kernel orthonormalized partial least squares (PLS) variant for feature extraction, imposing sparsity constrains in the solution to improve scalability. The algorithm...... is tested on a benchmark of UCI data sets, and on the analysis of integrated short-time music features for genre prediction. The upshot is that the method has strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS, and therefore is an appealing method...

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

Science.gov (United States)

Sivaraks, Haemwaan

2015-01-01

Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. PMID:25688284

Extraction of multi-scale landslide morphological features based on local Gi* using airborne LiDAR-derived DEM

Science.gov (United States)

Shi, Wenzhong; Deng, Susu; Xu, Wenbing

2018-02-01

For automatic landslide detection, landslide morphological features should be quantitatively expressed and extracted. High-resolution Digital Elevation Models (DEMs) derived from airborne Light Detection and Ranging (LiDAR) data allow fine-scale morphological features to be extracted, but noise in DEMs influences morphological feature extraction, and the multi-scale nature of landslide features should be considered. This paper proposes a method to extract landslide morphological features characterized by homogeneous spatial patterns. Both profile and tangential curvature are utilized to quantify land surface morphology, and a local Gi* statistic is calculated for each cell to identify significant patterns of clustering of similar morphometric values. The method was tested on both synthetic surfaces simulating natural terrain and airborne LiDAR data acquired over an area dominated by shallow debris slides and flows. The test results of the synthetic data indicate that the concave and convex morphologies of the simulated terrain features at different scales and distinctness could be recognized using the proposed method, even when random noise was added to the synthetic data. In the test area, cells with large local Gi* values were extracted at a specified significance level from the profile and the tangential curvature image generated from the LiDAR-derived 1-m DEM. The morphologies of landslide main scarps, source areas and trails were clearly indicated, and the morphological features were represented by clusters of extracted cells. A comparison with the morphological feature extraction method based on curvature thresholds proved the proposed method's robustness to DEM noise. When verified against a landslide inventory, the morphological features of almost all recent (historical (> 10 years) landslides were extracted. This finding indicates that the proposed method can facilitate landslide detection, although the cell clusters extracted from curvature images should

A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

Directory of Open Access Journals (Sweden)

Dhanoa Jaspreet Singh

2016-01-01

Full Text Available With the advent of technology and multimedia information, digital images are increasing very quickly. Various techniques are being developed to retrieve/search digital information or data contained in the image. Traditional Text Based Image Retrieval System is not plentiful. Since it is time consuming as it require manual image annotation. Also, the image annotation differs with different peoples. An alternate to this is Content Based Image Retrieval (CBIR system. It retrieves/search for image using its contents rather the text, keywords etc. A lot of exploration has been compassed in the range of Content Based Image Retrieval (CBIR with various feature extraction techniques. Shape is a significant image feature as it reflects the human perception. Moreover, Shape is quite simple to use by the user to define object in an image as compared to other features such as Color, texture etc. Over and above, if applied alone, no descriptor will give fruitful results. Further, by combining it with an improved classifier, one can use the positive features of both the descriptor and classifier. So, a tryout will be made to establish an algorithm for accurate feature (Shape extraction in Content Based Image Retrieval (CBIR. The main objectives of this project are: (a To propose an algorithm for shape feature extraction using CBIR, (b To evaluate the performance of proposed algorithm and (c To compare the proposed algorithm with state of art techniques.

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

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Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

2017-09-01

Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

The optimal extraction of feature algorithm based on KAZE

Science.gov (United States)

Yao, Zheyi; Gu, Guohua; Qian, Weixian; Wang, Pengcheng

2015-10-01

As a novel method of 2D features extraction algorithm over the nonlinear scale space, KAZE provide a special method. However, the computation of nonlinear scale space and the construction of KAZE feature vectors are more expensive than the SIFT and SURF significantly. In this paper, the given image is used to build the nonlinear space up to a maximum evolution time through the efficient Additive Operator Splitting (AOS) techniques and the variable conductance diffusion. Changing the parameter can improve the construction of nonlinear scale space and simplify the image conductivities for each dimension space, with the predigest computation. Then, the detection for points of interest can exhibit a maxima of the scale-normalized determinant with the Hessian response in the nonlinear scale space. At the same time, the detection of feature vectors is optimized by the Wavelet Transform method, which can avoid the second Gaussian smoothing in the KAZE Features and cut down the complexity of the algorithm distinctly in the building and describing vectors steps. In this way, the dominant orientation is obtained, similar to SURF, by summing the responses within a sliding circle segment covering an angle of π/3 in the circular area of radius 6σ with a sampling step of size σ one by one. Finally, the extraction in the multidimensional patch at the given scale, centered over the points of interest and rotated to align its dominant orientation to a canonical direction, is able to simplify the description of feature by reducing the description dimensions, just as the PCA-SIFT method. Even though the features are somewhat more expensive to compute than SIFT due to the construction of nonlinear scale space, but compared to SURF, the result revels a step forward in performance in detection, description and application against the previous ways by the following contrast experiments.

Automated Feature Extraction of Foredune Morphology from Terrestrial Lidar Data

Science.gov (United States)

Spore, N.; Brodie, K. L.; Swann, C.

2014-12-01

Foredune morphology is often described in storm impact prediction models using the elevation of the dune crest and dune toe and compared with maximum runup elevations to categorize the storm impact and predicted responses. However, these parameters do not account for other foredune features that may make them more or less erodible, such as alongshore variations in morphology, vegetation coverage, or compaction. The goal of this work is to identify other descriptive features that can be extracted from terrestrial lidar data that may affect the rate of dune erosion under wave attack. Daily, mobile-terrestrial lidar surveys were conducted during a 6-day nor'easter (Hs = 4 m in 6 m water depth) along 20km of coastline near Duck, North Carolina which encompassed a variety of foredune forms in close proximity to each other. This abstract will focus on the tools developed for the automated extraction of the morphological features from terrestrial lidar data, while the response of the dune will be presented by Brodie and Spore as an accompanying abstract. Raw point cloud data can be dense and is often under-utilized due to time and personnel constraints required for analysis, since many algorithms are not fully automated. In our approach, the point cloud is first projected into a local coordinate system aligned with the coastline, and then bare earth points are interpolated onto a rectilinear 0.5 m grid creating a high resolution digital elevation model. The surface is analyzed by identifying features along each cross-shore transect. Surface curvature is used to identify the position of the dune toe, and then beach and berm morphology is extracted shoreward of the dune toe, and foredune morphology is extracted landward of the dune toe. Changes in, and magnitudes of, cross-shore slope, curvature, and surface roughness are used to describe the foredune face and each cross-shore transect is then classified using its pre-storm morphology for storm-response analysis.

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

Directory of Open Access Journals (Sweden)

Chen Liu

2018-05-01

Full Text Available How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the “bad”, “good”, “poor”, “pretty good”, “not bad” these general collocation words.

Low-power analog integrated circuits for wireless ECG acquisition systems.

Science.gov (United States)

Tsai, Tsung-Heng; Hong, Jia-Hua; Wang, Liang-Hung; Lee, Shuenn-Yuh

2012-09-01

This paper presents low-power analog ICs for wireless ECG acquisition systems. Considering the power-efficient communication in the body sensor network, the required low-power analog ICs are developed for a healthcare system through miniaturization and system integration. To acquire the ECG signal, a low-power analog front-end system, including an ECG signal acquisition board, an on-chip low-pass filter, and an on-chip successive-approximation analog-to-digital converter for portable ECG detection devices is presented. A quadrature CMOS voltage-controlled oscillator and a 2.4 GHz direct-conversion transmitter with a power amplifier and upconversion mixer are also developed to transmit the ECG signal through wireless communication. In the receiver, a 2.4 GHz fully integrated CMOS RF front end with a low-noise amplifier, differential power splitter, and quadrature mixer based on current-reused folded architecture is proposed. The circuits have been implemented to meet the specifications of the IEEE 802.15.4 2.4 GHz standard. The low-power ICs of the wireless ECG acquisition systems have been fabricated using a 0.18 μm Taiwan Semiconductor Manufacturing Company (TSMC) CMOS standard process. The measured results on the human body reveal that ECG signals can be acquired effectively by the proposed low-power analog front-end ICs.

Respiratory rate extraction from pulse oximeter and electrocardiographic recordings

International Nuclear Information System (INIS)

Lee, Jinseok; Chon, Ki H; Florian, John P

2011-01-01

We present an algorithm of respiratory rate extraction using particle filter (PF), which is applicable to both photoplethysmogram (PPG) and electrocardiogram (ECG) signals. For the respiratory rate estimation, 1 min data are analyzed with combination of a PF method and an autoregressive model where among the resultant coefficients, the corresponding pole angle with the highest magnitude is searched since this reflects the closest approximation of the true breathing rate. The PPG data were collected from 15 subjects with the metronome breathing rate ranging from 24 to 36 breaths per minute in the supine and upright positions. The ECG data were collected from 11 subjects with spontaneous breathing ranging from 36 to 60 breaths per minute during treadmill exercises. Our method was able to accurately extract respiratory rates for both metronome and spontaneous breathing even during strenuous exercises. More importantly, despite slow increases in breathing rates concomitant with greater exercise vigor with time, our method was able to accurately track these progressive increases in respiratory rates. We quantified the accuracy of our method by using the mean, standard deviation and interquartile range of the error rates which all reflected high accuracy in estimating the true breathing rates. We are not aware of any other algorithms that are able to provide accurate respiratory rates directly from either ECG signals or PPG signals with spontaneous breathing during strenuous exercises. Our method is near real-time realizable because the computational time on 1 min data segment takes only 10 ms on a 2.66 GHz Intel Core2 microprocessor; the data are subsequently shifted every 10 s to obtain near-continuous breathing rates. This is an attractive feature since most other techniques require offline data analyses to estimate breathing rates

Respiratory rate extraction from pulse oximeter and electrocardiographic recordings.

Science.gov (United States)

Lee, Jinseok; Florian, John P; Chon, Ki H

2011-11-01

We present an algorithm of respiratory rate extraction using particle filter (PF), which is applicable to both photoplethysmogram (PPG) and electrocardiogram (ECG) signals. For the respiratory rate estimation, 1 min data are analyzed with combination of a PF method and an autoregressive model where among the resultant coefficients, the corresponding pole angle with the highest magnitude is searched since this reflects the closest approximation of the true breathing rate. The PPG data were collected from 15 subjects with the metronome breathing rate ranging from 24 to 36 breaths per minute in the supine and upright positions. The ECG data were collected from 11 subjects with spontaneous breathing ranging from 36 to 60 breaths per minute during treadmill exercises. Our method was able to accurately extract respiratory rates for both metronome and spontaneous breathing even during strenuous exercises. More importantly, despite slow increases in breathing rates concomitant with greater exercise vigor with time, our method was able to accurately track these progressive increases in respiratory rates. We quantified the accuracy of our method by using the mean, standard deviation and interquartile range of the error rates which all reflected high accuracy in estimating the true breathing rates. We are not aware of any other algorithms that are able to provide accurate respiratory rates directly from either ECG signals or PPG signals with spontaneous breathing during strenuous exercises. Our method is near real-time realizable because the computational time on 1 min data segment takes only 10 ms on a 2.66 GHz Intel Core2 microprocessor; the data are subsequently shifted every 10 s to obtain near-continuous breathing rates. This is an attractive feature since most other techniques require offline data analyses to estimate breathing rates.

Semantic feature extraction for interior environment understanding and retrieval

Science.gov (United States)

Lei, Zhibin; Liang, Yufeng

1998-12-01

In this paper, we propose a novel system of semantic feature extraction and retrieval for interior design and decoration application. The system, V2ID(Virtual Visual Interior Design), uses colored texture and spatial edge layout to obtain simple information about global room environment. We address the domain-specific segmentation problem in our application and present techniques for obtaining semantic features from a room environment. We also discuss heuristics for making use of these features (color, texture, edge layout, and shape), to retrieve objects from an existing database. The final resynthesized room environment, with the original scene and objects from the database, is created for the purpose of animation and virtual walk-through.

Diagnostic Role of ECG Recording Simultaneously With EEG Testing.

Science.gov (United States)

Kendirli, Mustafa Tansel; Aparci, Mustafa; Kendirli, Nurten; Tekeli, Hakan; Karaoglan, Mustafa; Senol, Mehmet Guney; Togrol, Erdem

2015-07-01

Arrhythmia is not uncommon in the etiology of syncope which mimics epilepsy. Data about the epilepsy induced vagal tonus abnormalities have being increasingly reported. So we aimed to evaluate what a neurologist may gain by a simultaneous electrocardiogram (ECG) and electroencephalogram (EEG) recording in the patients who underwent EEG testing due to prediagnosis of epilepsy. We retrospectively evaluated and detected ECG abnormalities in 68 (18%) of 376 patients who underwent EEG testing. A minimum of 20 of minutes artifact-free recording were required for each patient. Standard 1-channel ECG was simultaneously recorded in conjunction with the EEG. In all, 28% of females and 14% of males had ECG abnormalities. Females (mean age 49 years, range 18-88 years) were older compared with the male group (mean age 28 years, range 16-83 years). Atrial fibrillation was more frequent in female group whereas bradycardia and respiratory sinus arrhythmia was higher in male group. One case had been detected a critical asystole indicating sick sinus syndrome in the female group and treated with a pacemaker implantation in the following period. Simultaneous ECG recording in conjunction with EEG testing is a clinical prerequisite to detect and to clarify the coexisting ECG and EEG abnormalities and their clinical relevance. Potentially rare lethal causes of syncope that mimic seizure or those that could cause resistance to antiepileptic therapy could effectively be distinguished by detecting ECG abnormalities coinciding with the signs and abnormalities during EEG recording. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Fetal electrocardiogram (ECG) for fetal monitoring during labour.

Science.gov (United States)

Neilson, James P

2015-12-21

Hypoxaemia during labour can alter the shape of the fetal electrocardiogram (ECG) waveform, notably the relation of the PR to RR intervals, and elevation or depression of the ST segment. Technical systems have therefore been developed to monitor the fetal ECG during labour as an adjunct to continuous electronic fetal heart rate monitoring with the aim of improving fetal outcome and minimising unnecessary obstetric interference. To compare the effects of analysis of fetal ECG waveforms during labour with alternative methods of fetal monitoring. The Cochrane Pregnancy and Childbirth Group's Trials Register (latest search 23 September 2015) and reference lists of retrieved studies. Randomised trials comparing fetal ECG waveform analysis with alternative methods of fetal monitoring during labour. One review author independently assessed trials for inclusion and risk of bias, extracted data and checked them for accuracy. One review author assessed the quality of the evidence using the GRADE approach. Seven trials (27,403 women) were included: six trials of ST waveform analysis (26,446 women) and one trial of PR interval analysis (957 women). The trials were generally at low risk of bias for most domains and the quality of evidence for ST waveform analysis trials was graded moderate to high. In comparison to continuous electronic fetal heart rate monitoring alone, the use of adjunctive ST waveform analysis made no obvious difference to primary outcomes: births by caesarean section (risk ratio (RR) 1.02, 95% confidence interval (CI) 0.96 to 1.08; six trials, 26,446 women; high quality evidence); the number of babies with severe metabolic acidosis at birth (cord arterial pH less than 7.05 and base deficit greater than 12 mmol/L) (average RR 0.72, 95% CI 0.43 to 1.20; six trials, 25,682 babies; moderate quality evidence); or babies with neonatal encephalopathy (RR 0.61, 95% CI 0.30 to 1.22; six trials, 26,410 babies; high quality evidence). There were, however, on average

Homomorphic encryption-based secure SIFT for privacy-preserving feature extraction

Science.gov (United States)

Hsu, Chao-Yung; Lu, Chun-Shien; Pei, Soo-Chang

2011-02-01

Privacy has received much attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario, where the server is resource-abundant and is capable of finishing the designated tasks, it is envisioned that secure media retrieval and search with privacy-preserving will be seriously treated. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first to address the problem of secure SIFT feature extraction and representation in the encrypted domain. Since all the operations in SIFT must be moved to the encrypted domain, we propose a homomorphic encryption-based secure SIFT method for privacy-preserving feature extraction and representation based on Paillier cryptosystem. In particular, homomorphic comparison is a must for SIFT feature detection but is still a challenging issue for homomorphic encryption methods. To conquer this problem, we investigate a quantization-like secure comparison strategy in this paper. Experimental results demonstrate that the proposed homomorphic encryption-based SIFT performs comparably to original SIFT on image benchmarks, while preserving privacy additionally. We believe that this work is an important step toward privacy-preserving multimedia retrieval in an environment, where privacy is a major concern.

Burned out myocardium in biventricular hypertrophic cardiomyopathy presenting with congestive heart failure: Importance of ECG changes

Directory of Open Access Journals (Sweden)

Christer Backman

2014-01-01

Full Text Available A 60 year old man was found to have a heart murmur and ECG features of ventricular hypertrophy on a medical check up for military recruitment at age of 20, despite having swimming as the only exercise. His mother had 3 survived children out of 9 pregnancies.

Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit

OpenAIRE

Yuehai Wang; Yongzheng Yan; Qinyong Wang

2016-01-01

Fault diagnosis for analog circuit has become a prominent factor in improving the reliability of integrated circuit due to its irreplaceability in modern integrated circuits. In fact fault diagnosis based on intelligent algorithms has become a popular research topic as efficient feature extraction and selection are a critical and intricate task in analog fault diagnosis. Further, it is extremely important to propose some general guidelines for the optimal feature extraction and selection. In ...

The Effect of Creative Tasks on Electrocardiogram: Using Linear and Nonlinear Features in Combination with Classification Approaches

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Sahar Zakeri

2017-02-01

Full Text Available Objective: Interest in the subject of creativity and its impacts on human life is growing extensively. However, only a few surveys pay attention to the relation between creativity and physiological changes. This paper presents a novel approach to distinguish between creativity states from electrocardiogram signals. Nineteen linear and nonlinear features of the cardiac signal were extracted to detect creativity states. Method: ECG signals of 52 participants were recorded while doing three tasks of Torrance Tests of Creative Thinking (TTCT/ figural B. To remove artifacts, notch filter 50 Hz and Chebyshev II were applied. According to TTCT scores, participants were categorized into the high and low creativity groups: Participants with scores higher than 70 were assigned into the high creativity group and those with scores less than 30 were considered as low creativity group. Some linear and nonlinear features were extracted from the ECGs. Then, Support Vector Machine (SVM and Adaptive Neuro-Fuzzy Inference System (ANFIS were used to classify the groups.Results: Applying the Wilcoxon test, significant differences were observed between rest and each three tasks of creativity. However, better discrimination was performed between rest and the first task. In addition, there were no statistical differences between the second and third task of the test. The results indicated that the SVM effectively detects all the three tasks from the rest, particularly the task 1 and reached the maximum accuracy of 99.63% in the linear analysis. In addition, the high creative group was separated from the low creative group with the accuracy of 98.41%.Conclusion: the combination of SVM classifier with linear features can be useful to show the relation between creativity and physiological changes.

A novel automated spike sorting algorithm with adaptable feature extraction.

Science.gov (United States)

Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

2012-10-15

To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

Effects of Feature Extraction and Classification Methods on Cyberbully Detection

OpenAIRE

ÖZEL, Selma Ayşe; SARAÇ, Esra

2016-01-01

Cyberbullying is defined as an aggressive, intentional action against a defenseless person by using the Internet, or other electronic contents. Researchers have found that many of the bullying cases have tragically ended in suicides; hence automatic detection of cyberbullying has become important. In this study we show the effects of feature extraction, feature selection, and classification methods that are used, on the performance of automatic detection of cyberbullying. To perform the exper...

Feature extraction & image processing for computer vision

CERN Document Server

Nixon, Mark

2012-01-01

This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, ""The main strength of the proposed book is the exemplar code of the algorithms."" Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filt

A Method of Road Extraction from High-resolution Remote Sensing Images Based on Shape Features

Directory of Open Access Journals (Sweden)

LEI Xiaoqi

2016-02-01

Full Text Available Road extraction from high-resolution remote sensing image is an important and difficult task.Since remote sensing images include complicated information,the methods that extract roads by spectral,texture and linear features have certain limitations.Also,many methods need human-intervention to get the road seeds(semi-automatic extraction,which have the great human-dependence and low efficiency.The road-extraction method,which uses the image segmentation based on principle of local gray consistency and integration shape features,is proposed in this paper.Firstly,the image is segmented,and then the linear and curve roads are obtained by using several object shape features,so the method that just only extract linear roads are rectified.Secondly,the step of road extraction is carried out based on the region growth,the road seeds are automatic selected and the road network is extracted.Finally,the extracted roads are regulated by combining the edge information.In experiments,the images that including the better gray uniform of road and the worse illuminated of road surface were chosen,and the results prove that the method of this study is promising.

Live ECG readings using Google Glass in emergency situations.

Science.gov (United States)

Schaer, Roger; Salamin, Fanny; Jimenez Del Toro, Oscar Alfonso; Atzori, Manfredo; Muller, Henning; Widmer, Antoine

2015-01-01

Most sudden cardiac problems require rapid treatment to preserve life. In this regard, electrocardiograms (ECG) shown on vital parameter monitoring systems help medical staff to detect problems. In some situations, such monitoring systems may display information in a less than convenient way for medical staff. For example, vital parameters are displayed on large screens outside the field of view of a surgeon during cardiac surgery. This may lead to losing time and to mistakes when problems occur during cardiac operations. In this paper we present a novel approach to display vital parameters such as the second derivative of the ECG rhythm and heart rate close to the field of view of a surgeon using Google Glass. As a preliminary assessment, we run an experimental study to verify the possibility for medical staff to identify abnormal ECG rhythms from Google Glass. This study compares 6 ECG rhythms readings from a 13.3 inch laptop screen and from the prism of Google Glass. Seven medical residents in internal medicine participated in the study. The preliminary results show that there is no difference between identifying these 6 ECG rhythms from the laptop screen versus Google Glass. Both allow close to perfect identification of the 6 common ECG rhythms. This shows the potential of connected glasses such as Google Glass to be useful in selected medical applications.

Extracting product features and opinion words using pattern knowledge in customer reviews.

Science.gov (United States)

Htay, Su Su; Lynn, Khin Thidar

2013-01-01

Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of product.

Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews

Directory of Open Access Journals (Sweden)

Su Su Htay

2013-01-01

Full Text Available Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of product.

Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews

Science.gov (United States)

Lynn, Khin Thidar

2013-01-01

Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of product. PMID:24459430

The GC/MS Analysis of Volatile Components Extracted by Different Methods from Exocarpium Citri Grandis

Directory of Open Access Journals (Sweden)

Zhisheng Xie

2013-01-01

Full Text Available Volatile components from Exocarpium Citri Grandis (ECG were, respectively, extracted by three methods, that is, steam distillation (SD, headspace solid-phase microextraction (HS-SPME, and solvent extraction (SE. A total of 81 compounds were identified by gas chromatography-mass spectrometry including 77 (SD, 56 (HS-SPME, and 48 (SE compounds, respectively. Despite of the extraction method, terpenes (39.98~57.81% were the main volatile components of ECG, mainly germacrene-D, limonene, 2,6,8,10,14-hexadecapentaene, 2,6,11,15-tetramethyl-, (E,E,E-, and trans-caryophyllene. Comparison was made among the three methods in terms of extraction profile and property. SD relatively gave an entire profile of volatile in ECG by long-time extraction; SE enabled the analysis of low volatility and high molecular weight compounds but lost some volatiles components; HS-SPME generated satisfactory extraction efficiency and gave similar results to those of SD at analytical level when consuming less sample amount, shorter extraction time, and simpler procedure. Although SD and SE were treated as traditionally preparative extractive techniques for volatiles in both small batches and large scale, HS-SPME coupled with GC/MS could be useful and appropriative for the rapid extraction and qualitative analysis of volatile components from medicinal plants at analytical level.

Scale-invariant feature extraction of neural network and renormalization group flow

Science.gov (United States)

Iso, Satoshi; Shiba, Shotaro; Yokoo, Sumito

2018-05-01

Theoretical understanding of how a deep neural network (DNN) extracts features from input images is still unclear, but it is widely believed that the extraction is performed hierarchically through a process of coarse graining. It reminds us of the basic renormalization group (RG) concept in statistical physics. In order to explore possible relations between DNN and RG, we use the restricted Boltzmann machine (RBM) applied to an Ising model and construct a flow of model parameters (in particular, temperature) generated by the RBM. We show that the unsupervised RBM trained by spin configurations at various temperatures from T =0 to T =6 generates a flow along which the temperature approaches the critical value Tc=2.2 7 . This behavior is the opposite of the typical RG flow of the Ising model. By analyzing various properties of the weight matrices of the trained RBM, we discuss why it flows towards Tc and how the RBM learns to extract features of spin configurations.

Bubble feature extracting based on image processing of coal flotation froth

Energy Technology Data Exchange (ETDEWEB)

Wang, F.; Wang, Y.; Lu, M.; Liu, W. [China University of Mining and Technology, Beijing (China). Dept of Chemical Engineering and Environment

2001-11-01

Using image processing the contrast ratio between the bubble on the surface of flotation froth and the image background was enhanced, and the edges of bubble were extracted. Thus a model about the relation between the statistic feature of the bubbles in the image and the cleaned coal can be established. It is feasible to extract the bubble by processing the froth image of coal flotation on the basis of analysing the shape of the bubble. By means of processing the 51 group images sampled from laboratory column, it is thought that the use of the histogram equalization of image gradation and the medium filtering can obviously improve the dynamic contrast range and the brightness of bubbles. Finally, the method of threshold value cut and the bubble edge detecting for extracting the bubble were also discussed to describe the bubble feature, such as size and shape, in the froth image and to distinguish the froth image of coal flotation. 6 refs., 3 figs.

Feature Extraction on Brain Computer Interfaces using Discrete Dyadic Wavelet Transform: Preliminary Results

International Nuclear Information System (INIS)

Gareis, I; Gentiletti, G; Acevedo, R; Rufiner, L

2011-01-01

The purpose of this work is to evaluate different feature extraction alternatives to detect the event related evoked potential signal on brain computer interfaces, trying to minimize the time employed and the classification error, in terms of sensibility and specificity of the method, looking for alternatives to coherent averaging. In this context the results obtained performing the feature extraction using discrete dyadic wavelet transform using different mother wavelets are presented. For the classification a single layer perceptron was used. The results obtained with and without the wavelet decomposition were compared; showing an improvement on the classification rate, the specificity and the sensibility for the feature vectors obtained using some mother wavelets.

Dose reduction in multi-slice CT of the heart by use of ECG-controlled tube current modulation (''ECG pulsing''): phantom measurements

International Nuclear Information System (INIS)

Poll, L.W.; Cohnen, M.; Brachten, S.; Moedder, U.; Ewen, K.

2002-01-01

To evaluate the effect of ECG-controlled tube current modulation on radiation exposure in retrospectively-ECG-gated multislice CT (MSCT) of the heart. Material and methods: Three different cardiac MSCT protocols with different slice collimation (4 x 1, and 4 x 2.5 mm), and a pitch-factor of 1.5 and 1.8 were investigated at a multi-slice CT scanner Somatom Volume Zoom, Siemens. An anthropomorphic Alderson-Rando phantom was equipped with LiF-Thermoluminescence dosimeters at several organ sites, and effective doses were calculated using ICRP-weighting factors. Scan protocols were performed with ECG-controlled tube current modulation ('ECG pulsing') at two different heart rates (60 and 80 bpm). These data were compared to previous data from MSCT of the heart without use of 'ECG pulsing'. Results: Radiation exposure with (60 bpm) and without tube current modulation using a 2.5 mm collimation was 1.8 mSv and 2.9 mSv for females, and 1.5 mSv and 2.4 mSv for males, respectively. For protocols using a 1 mm collimation with a pitch-factor of 1.5 (1.8), radiation exposure with and without tube current modulation was 5.6 (6.3) mSv and 9.5 (11.2) mSv for females, and 4.6 (5.2) mSv and 7.7 (9.2) mSv for males, respectively. At higher heart rates (80 bpm) radiation exposure is increased from 1.5-1.8 mSv to 1.8-2.1 mSv, using the 2.5 mm collimation, and from 4.6-5.6 mSv to 5.9-7.2 mSv, for protocols using 1 mm collimation. Conclusions: The ECG-controlled tube current modulation allows a dose reduction of 37% to 44% when retrospectively ECG-gated MSCT of the heart is performed. The tube current - as a function over time - and therefore the radiation exposure is dependent on the heart rate. (orig.) [de

Threshold-based system for noise detection in multilead ECG recordings

International Nuclear Information System (INIS)

Jekova, Irena; Krasteva, Vessela; Christov, Ivaylo; Abächerli, Roger

2012-01-01

This paper presents a system for detection of the most common noise types seen on the electrocardiogram (ECG) in order to evaluate whether an episode from 12-lead ECG is reliable for diagnosis. It implements criteria for estimation of the noise corruption level in specific frequency bands, aiming to identify the main sources of ECG quality disruption, such as missing signal or limited dynamics of the QRS components above 4 Hz; presence of high amplitude and steep artifacts seen above 1 Hz; baseline drift estimated at frequencies below 1 Hz; power–line interference in a band ±2 Hz around its central frequency; high-frequency and electromyographic noises above 20 Hz. All noise tests are designed to process the ECG series in the time domain, including 13 adjustable thresholds for amplitude and slope criteria which are evaluated in adjustable time intervals, as well as number of leads. The system allows flexible extension toward application-specific requirements for the noise levels in acceptable quality ECGs. Training of different thresholds’ settings to determine different positive noise detection rates is performed with the annotated set of 1000 ECGs from the PhysioNet database created for the Computing in Cardiology Challenge 2011. Two implementations are highlighted on the receiver operating characteristic (area 0.968) to fit to different applications. The implementation with high sensitivity (Se = 98.7%, Sp = 80.9%) appears as a reliable alarm when there are any incidental problems with the ECG acquisition, while the implementation with high specificity (Sp = 97.8%, Se = 81.8%) is less susceptible to transient problems but rather validates noisy ECGs with acceptable quality during a small portion of the recording. (paper)

Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery

Directory of Open Access Journals (Sweden)

CAO Yungang

2016-10-01

Full Text Available A novel approach for road centerline extraction from high spatial resolution satellite imagery is proposed by fusing both pixel-based and object-based features. Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps. Then, extracted multiple features are utilized in the fusion framework of Dempster-Shafer evidence theory to roughly identify the road network regions. Finally, an automatic noise removing algorithm combined with the tensor voting strategy is presented to accurately extract the road centerline. Experimental results using high-resolution satellite imageries with different scenes and spatial resolutions showed that the proposed approach compared favorably with the traditional methods, particularly in the aspect of eliminating the salt noise and conglutination phenomenon.

HEART RATE VARIABILITY CLASSIFICATION USING SADE-ELM CLASSIFIER WITH BAT FEATURE SELECTION

Directory of Open Access Journals (Sweden)

R Kavitha

2017-07-01

Full Text Available The electrical activity of the human heart is measured by the vital bio medical signal called ECG. This electrocardiogram is employed as a crucial source to gather the diagnostic information of a patient’s cardiopathy. The monitoring function of cardiac disease is diagnosed by documenting and handling the electrocardiogram (ECG impulses. In the recent years many research has been done and developing an enhanced method to identify the risk in the patient’s body condition by processing and analysing the ECG signal. This analysis of the signal helps to find the cardiac abnormalities, arrhythmias, and many other heart problems. ECG signal is processed to detect the variability in heart rhythm; heart rate variability is calculated based on the time interval between heart beats. Heart Rate Variability HRV is measured by the variation in the beat to beat interval. The Heart rate Variability (HRV is an essential aspect to diagnose the properties of the heart. Recent development enhances the potential with the aid of non-linear metrics in reference point with feature selection. In this paper, the fundamental elements are taken from the ECG signal for feature selection process where Bat algorithm is employed for feature selection to predict the best feature and presented to the classifier for accurate classification. The popular machine learning algorithm ELM is taken for classification, integrated with evolutionary algorithm named Self- Adaptive Differential Evolution Extreme Learning Machine SADEELM to improve the reliability of classification. It combines Effective Fuzzy Kohonen clustering network (EFKCN to be able to increase the accuracy of the effect for HRV transmission classification. Hence, it is observed that the experiment carried out unveils that the precision is improved by the SADE-ELM method and concurrently optimizes the computation time.

ECG-triggered MDR-CT for the detection of pulmonary metastases

International Nuclear Information System (INIS)

Pauls, S.; Wahl, J.; Aschoff, A.J.; Brambs, H.J.; Fleiter, T.R.

2003-01-01

Purpose: Comparison of multidetector-row CT (MDR-CT) of the chest with and without ECG triggering for the detection of pulmonary metastases. Materials and Methods: Fifty patients with malignant tumors underwent CT of the chest. The unenhanced phase was performed with ECG-triggered MDR-CT and the contrast-enhanced phase with helical MDR-CT. The ECG-triggered and standard helical scans were interpreted in separate sessions, with the analysis determining the number and demarcation of the intrapulmonary nodules and the delineation of the mediastinal structure (rated 1 = excellent to 5 = poor). Results: ECG-MDR-CT images detected 38% more pulmonary nodules than MDR-CT. The detection rate for tumors [de

Heart rhythm analysis using ECG recorded with a novel sternum based patch technology

DEFF Research Database (Denmark)

Saadi, Dorthe Bodholt; Fauerskov, Inge; Osmanagic, Armin

2013-01-01

, reliable long-term ECG recordings. The device is designed for high compliance and low patient burden. This novel patch technology is CE approved for ambulatory ECG recording of two ECG channels on the sternum. This paper describes a clinical pilot study regarding the usefulness of these ECG signals...... for heart rhythm analysis. A clinical technician with experience in ECG interpretation selected 200 noise-free 7 seconds ECG segments from 25 different patients. These 200 ECG segments were evaluated by two medical doctors according to their usefulness for heart rhythm analysis. The first doctor considered...... 98.5% of the segments useful for rhythm analysis, whereas the second doctor considered 99.5% of the segments useful for rhythm analysis. The conclusion of this pilot study indicates that two channel ECG recorded on the sternum is useful for rhythm analysis and could be used as input to diagnosis...

Low-cost compact ECG with graphic LCD and phonocardiogram system design.

Science.gov (United States)

Kara, Sadik; Kemaloğlu, Semra; Kirbaş, Samil

2006-06-01

Till today, many different ECG devices are made in developing countries. In this study, low cost, small size, portable LCD screen ECG device, and phonocardiograph were designed. With designed system, heart sounds that take synchronously with ECG signal are heard as sensitive. Improved system consist three units; Unit 1, ECG circuit, filter and amplifier structure. Unit 2, heart sound acquisition circuit. Unit 3, microcontroller, graphic LCD and ECG signal sending unit to computer. Our system can be used easily in different departments of the hospital, health institution and clinics, village clinic and also in houses because of its small size structure and other benefits. In this way, it is possible that to see ECG signal and hear heart sounds as synchronously and sensitively. In conclusion, heart sounds are heard on the part of both doctor and patient because sounds are given to environment with a tiny speaker. Thus, the patient knows and hears heart sounds him/herself and is acquainted by doctor about healthy condition.

Aircraft micro-doppler feature extraction from high range resolution profiles

CSIR Research Space (South Africa)

Berndt, RJ

2015-10-01

Full Text Available The use of high range resolution measurements and the micro-Doppler effect produced by rotating or vibrating parts of a target has been well documented. This paper presents a technique for extracting features related to helicopter rotors...

Risk stratifying asymptomatic aortic stenosis: role of the resting 12-lead ECG.

Science.gov (United States)

Greve, Anders M

2014-02-01

Despite being routinely performed in the clinical follow-up of asymptomatic AS patients, little or no evidence describes the prognostic value of ECG findings in asymptomatic AS populations. This PhD thesis examined the correlates of resting 12-lead ECG variables with echocardiographic measures of AS severity and cardiovascular outcomes in the till date largest cohort (n=1,563) of asymptomatic patients with mild-to-moderate AS. Most importantly, this PhD thesis demonstrated that QRS-duration adds independent predictive value of sudden cardiac death and that the additional presence of ECG LVH/strain for fixed AS severity represents a lethal risk attribute. Finally, ECG abnormalities displayed low/moderate concordance with echocardiographic parameters. This argues that the ECG should be regarded as a separate tool for obtaining prognostically important information. Treatment was not randomized by ECG findings, future studies should therefore examine if and which ECG variables should elicit closer follow-up and/or earlier intervention to improve prognosis in asymptomatic AS populations.

Bedside identification of patients at risk for PVC-induced cardiomyopathy: Is ECG useful?

Science.gov (United States)

Garster, Noelle C; Henrikson, Charles A

2017-07-01

Premature ventricular complexes (PVCs) are an underrecognized cause of cardiomyopathy. Standard 12-lead electrocardiogram (ECG) has potential to direct attention toward at-risk patients. We performed a single-center, retrospective chart review of 1,240 patients who completed ECG and Holter monitoring at Oregon Health and Science University Hospital between January 1, 2011 and December 31, 2013 to investigate the relationship of PVC frequency on ECG with burden on Holter. Primary outcome measures included PVC quantity on ECG, mean PVC quantity on Holter, and percentage of total beats on Holter recorded as PVCs. High PVC burden was defined as ≥10% of total beats. Weighted mean percentages of total beats on Holter monitor recorded as PVCs were calculated for 0, 1, 2, and ≥3 PVCs on ECG and found to be 1.4% (n = 1,128), 3.5% (n = 32), 4.3% (n = 25), and 16.6% (n = 55), respectively, which represent statistically significant differences (P ECG for ≥10% PVC Holter burden was 58%. Negative predictive value for 0 PVCs on ECG was 98%. The sensitivity and specificity of ECG to identify high PVC burden on Holter was 72% and 93.6%, respectively, when utilizing a positive ECG result as one PVC or more, and 44% and 98.9%, respectively, with ≥3 PVCs on ECG. The positive likelihood ratio corresponding to ≥3 PVCs on ECG was 40. These findings demonstrate that the number of PVCs on ECG can be utilized for quick bedside estimation of high PVC burden. © 2017 Wiley Periodicals, Inc.

Low-Level Color and Texture Feature Extraction of Coral Reef Components

Directory of Open Access Journals (Sweden)

Ma. Sheila Angeli Marcos

2003-06-01

Full Text Available The purpose of this study is to develop a computer-based classifier that automates coral reef assessmentfrom digitized underwater video. We extract low-level color and texture features from coral images toserve as input to a high-level classifier. Low-level features for color were labeled blue, green, yellow/brown/orange, and gray/white, which are described by the normalized chromaticity histograms of thesemajor colors. The color matching capability of these features was determined through a technique called“Histogram Backprojection”. The low-level texture feature marks a region as coarse or fine dependingon the gray-level variance of the region.

A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification

Directory of Open Access Journals (Sweden)

Sudeep Thepade

2014-01-01

Full Text Available A number of techniques have been proposed earlier for feature extraction using image binarization. Efficiency of the techniques was dependent on proper threshold selection for the binarization method. In this paper, a new feature extraction technique using image binarization has been proposed. The technique has binarized the significant bit planes of an image by selecting local thresholds. The proposed algorithm has been tested on a public dataset and has been compared with existing widely used techniques using binarization for extraction of features. It has been inferred that the proposed method has outclassed all the existing techniques and has shown consistent classification performance.

Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey

OpenAIRE

Saleem Riaz; Hassan Elahi; Kashif Javaid; Tufail Shahzad

2017-01-01

Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, deve...

Dynamic segmentation and linear prediction for maternal ECG removal in antenatal abdominal recordings

International Nuclear Information System (INIS)

Vullings, R; Sluijter, R J; Mischi, M; Bergmans, J W M; Peters, C H L; Oei, S G

2009-01-01

Monitoring the fetal heart rate (fHR) and fetal electrocardiogram (fECG) during pregnancy is important to support medical decision making. Before labor, the fHR is usually monitored using Doppler ultrasound. This method is inaccurate and therefore of limited clinical value. During labor, the fHR can be monitored more accurately using an invasive electrode; this method also enables monitoring of the fECG. Antenatally, the fECG and fHR can also be monitored using electrodes on the maternal abdomen. The signal-to-noise ratio of these recordings is, however, low, the maternal electrocardiogram (mECG) being the main interference. Existing techniques to remove the mECG from these non-invasive recordings are insufficiently accurate or do not provide all spatial information of the fECG. In this paper a new technique for mECG removal in antenatal abdominal recordings is presented. This technique operates by the linear prediction of each separate wave in the mECG. Its performance in mECG removal and fHR detection is evaluated by comparison with spatial filtering, adaptive filtering, template subtraction and independent component analysis techniques. The new technique outperforms the other techniques in both mECG removal and fHR detection (by more than 3%)

Improving ELM-Based Service Quality Prediction by Concise Feature Extraction

Directory of Open Access Journals (Sweden)

Yuhai Zhao

2015-01-01

Full Text Available Web services often run on highly dynamic and changing environments, which generate huge volumes of data. Thus, it is impractical to monitor the change of every QoS parameter for the timely trigger precaution due to high computational costs associated with the process. To address the problem, this paper proposes an active service quality prediction method based on extreme learning machine. First, we extract web service trace logs and QoS information from the service log and convert them into feature vectors. Second, by the proposed EC rules, we are enabled to trigger the precaution of QoS as soon as possible with high confidence. An efficient prefix tree based mining algorithm together with some effective pruning rules is developed to mine such rules. Finally, we study how to extract a set of diversified features as the representative of all mined results. The problem is proved to be NP-hard. A greedy algorithm is presented to approximate the optimal solution. Experimental results show that ELM trained by the selected feature subsets can efficiently improve the reliability and the earliness of service quality prediction.

A system for intelligent home care ECG upload and priorisation.

Science.gov (United States)

D'Angelo, Lorenzo T; Tarita, Eugeniu; Zywietz, Tosja K; Lueth, Tim C

2010-01-01

In this contribution, a system for internet based, automated home care ECG upload and priorisation is presented for the first time. It unifies the advantages of existing telemonitoring ECG systems adding functionalities such as automated priorisation and usability for home care. Chronic cardiac diseases are a big group in the geriatric field. Most of them can be easily diagnosed with help of an electrocardiogram. A frequent or long-term ECG analysis allows early diagnosis of e.g. a cardiac infarction. Nevertheless, patients often aren't willing to visit a doctor for prophylactic purposes. Possible solutions of this problem are home care devices, which are used to investigate patients at home without the presence of a doctor on site. As the diffusion of such systems leads to a huge amount of data which has to be managed and evaluated, the presented approach focuses on an easy to use software for ECG upload from home, a web based management application and an algorithm for ECG preanalysis and priorisation.

Hidden discriminative features extraction for supervised high-order time series modeling.

Science.gov (United States)

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2016-11-01

In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative subspaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step. Two real third-order tensor-structures of time series datasets (an epilepsy electroencephalogram (EEG) that is modeled as channel×frequency bin×time frame and a microarray data that is modeled as gene×sample×time) were used for the evaluation of the TDFE. The experiment results corroborate the advantages of the proposed method with averages of 98.26% and 89.63% for the classification accuracies of the epilepsy dataset and the microarray dataset, respectively. These performance averages represent an improvement on those of the matrix-based algorithms and recent tensor-based, discriminant-decomposition approaches; this is especially the case considering the small number of samples that are used in practice. Copyright © 2016 Elsevier Ltd. All rights reserved.

Extracting BI-RADS Features from Portuguese Clinical Texts.

Science.gov (United States)

Nassif, Houssam; Cunha, Filipe; Moreira, Inês C; Cruz-Correia, Ricardo; Sousa, Eliana; Page, David; Burnside, Elizabeth; Dutra, Inês

2012-01-01

In this work we build the first BI-RADS parser for Portuguese free texts, modeled after existing approaches to extract BI-RADS features from English medical records. Our concept finder uses a semantic grammar based on the BIRADS lexicon and on iterative transferred expert knowledge. We compare the performance of our algorithm to manual annotation by a specialist in mammography. Our results show that our parser's performance is comparable to the manual method.

Micro-Doppler Feature Extraction and Recognition Based on Netted Radar for Ballistic Targets

Directory of Open Access Journals (Sweden)

Feng Cun-qian

2015-12-01

Full Text Available This study examines the complexities of using netted radar to recognize and resolve ballistic midcourse targets. The application of micro-motion feature extraction to ballistic mid-course targets is analyzed, and the current status of application and research on micro-motion feature recognition is concluded for singlefunction radar networks such as low- and high-resolution imaging radar networks. Advantages and disadvantages of these networks are discussed with respect to target recognition. Hybrid-mode radar networks combine low- and high-resolution imaging radar and provide a specific reference frequency that is the basis for ballistic target recognition. Main research trends are discussed for hybrid-mode networks that apply micromotion feature extraction to ballistic mid-course targets.

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

Directory of Open Access Journals (Sweden)

Jinlu Sheng

2016-07-01

Full Text Available To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

ECG-Based Measurements of Drug-induced Repolarization Changes

DEFF Research Database (Denmark)

Bhuiyan, Tanveer Ahmed

The purpose of this thesis is to investigate the abnormal repolarization both in the cellular and the surface ECG along with their relationship. It has been identified that the certain morphological changes of the monophasic action potential are predictor of TdP arrhythmia. Therefore the proporti......The purpose of this thesis is to investigate the abnormal repolarization both in the cellular and the surface ECG along with their relationship. It has been identified that the certain morphological changes of the monophasic action potential are predictor of TdP arrhythmia. Therefore...... the proportional changes of the surface ECG which corresponds to the arrhythmia-triggering MAP morphology is warranted to increase the confidence of determining cardiotoxicity of drugs....

ECG changes in gamma-therapy of esophagus cancer

International Nuclear Information System (INIS)

Khajrushev, Zh.A.; Abdrakhmanov, Zh.N.

1978-01-01

Effect of ionizing radiation dose distribution with time in gamma therapy of esophagus cancer has been studied on the basis of the results obtained with electrocardiography. 700 persons were examined before treatment and after completing the full course of irradiation, 426 persons were examined repeatedly. Radiation treatment methods used are given. In most cases ECG changes result in the quickened systole rhythm and diffuse changes in the myocardium due to intoxication. ECG changes associated with the irradiation for patients with esophagus cancer amounted to 16%. Frequency of postirradiation ECG changes depends on the position of esophagus area under irradiation. Different variants of mean dose fractionation were the most sparing with respect to the heart

Microprocessor-based simulator of surface ECG signals

International Nuclear Information System (INIS)

MartInez, A E; Rossi, E; Siri, L Nicola

2007-01-01

In this work, a simulator of surface electrocardiogram recorded signals (ECG) is presented. The device, based on a microcontroller and commanded by a personal computer, produces an analog signal resembling actual ECGs, not only in time course and voltage levels, but also in source impedance. The simulator is a useful tool for electrocardiograph calibration and monitoring, to incorporate as well in educational tasks and in clinical environments for early detection of faulty behaviour

Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Directory of Open Access Journals (Sweden)

Ming-ai Li

2017-01-01

Full Text Available Electroencephalography (EEG is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG not only has a close correlation with the human imagination and movement intention but also contains a large amount of physiological or disease information. As a result, it has been fully studied in the field of rehabilitation. To correctly interpret and accurately extract the features of MI-EEG signals, many nonlinear dynamic methods based on entropy, such as Approximate Entropy (ApEn, Sample Entropy (SampEn, Fuzzy Entropy (FE, and Permutation Entropy (PE, have been proposed and exploited continuously in recent years. However, these entropy-based methods can only measure the complexity of MI-EEG based on a single scale and therefore fail to account for the multiscale property inherent in MI-EEG. To solve this problem, Multiscale Sample Entropy (MSE, Multiscale Permutation Entropy (MPE, and Multiscale Fuzzy Entropy (MFE are developed by introducing scale factor. However, MFE has not been widely used in analysis of MI-EEG, and the same parameter values are employed when the MFE method is used to calculate the fuzzy entropy values on multiple scales. Actually, each coarse-grained MI-EEG carries the characteristic information of the original signal on different scale factors. It is necessary to optimize MFE parameters to discover more feature information. In this paper, the parameters of MFE are optimized independently for each scale factor, and the improved MFE (IMFE is applied to the feature extraction of MI-EEG. Based on the event-related desynchronization (ERD/event-related synchronization (ERS phenomenon, IMFE features from multi channels are fused organically to construct the feature vector. Experiments are conducted on a public dataset by using Support Vector Machine (SVM as a classifier. The experiment results of 10-fold cross-validation show that the proposed method yields

A computational environment for long-term multi-feature and multi-algorithm seizure prediction.

Science.gov (United States)

Teixeira, C A; Direito, B; Costa, R P; Valderrama, M; Feldwisch-Drentrup, H; Nikolopoulos, S; Le Van Quyen, M; Schelter, B; Dourado, A

2010-01-01

The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors one should consider long-term recordings and several features and algorithms integrated in one software tool. A computational environment, based on Matlab (®), is presented, aiming to be an innovative tool for seizure prediction. It results from the need of a powerful and flexible tool for long-term EEG/ECG analysis by multiple features and algorithms. After being extracted, features can be subjected to several reduction and selection methods, and then used for prediction. The predictions can be conducted based on optimized thresholds or by applying computational intelligence methods. One important aspect is the integrated evaluation of the seizure prediction characteristic of the developed predictors.

Feature Selection for Nonstationary Data: Application to Human Recognition Using Medical Biometrics.

Science.gov (United States)

Komeili, Majid; Louis, Wael; Armanfard, Narges; Hatzinakos, Dimitrios

2018-05-01

Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions. It uses local information in terms of sample margins while enforcing an across-session measure. This makes it a perfect fit for aforementioned biometric recognition problem. Comprehensive experiments on ECG and TEOAE variability due to time lapse and body posture are done. Performance of the proposed method is compared against seven state-of-the-art feature selection algorithms as well as another six approaches in the area of ECG and TEOAE biometric recognition. Experimental results demonstrate that the proposed method performs noticeably better than other algorithms.

The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

Science.gov (United States)

Ma, Teng; Li, Hui; Yang, Hao; Lv, Xulin; Li, Peiyang; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2017-01-01

Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control. In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance. The deep learning and compressed sensing approach can generate the multi-modality features which can effectively improve the BCI performance with approximately 3.5% accuracy incensement over all 11 subjects and is more effective for those subjects with relatively poor performance when using the conventional features. Compared with the conventional amplitude-based mVEP feature extraction approach, the deep learning and compressed sensing approach has a higher classification accuracy and is more effective for subjects with relatively poor performance. According to the results, the deep learning and compressed sensing approach is more effective for extracting the mVEP feature to construct the corresponding BCI system, and the proposed feature extraction framework is easy to extend to other types of BCIs, such as motor imagery (MI), steady-state visual evoked potential (SSVEP)and P300. Copyright © 2016 Elsevier B.V. All rights reserved.

Clinical evaluation of the Tl-201 ECG-gated myocardial SPECT

International Nuclear Information System (INIS)

Mochizuki, Teruhito

1989-01-01

In order to evaluate the clinical usefulness of the Tl-201 ECG-gated myocardial single photon emission computed tomography (SPECT), we compared the wall motion and the grade of the Tl-201 uptake of the ECG-gated myocardial SPECT with the wall motion of the ECG-gated blood pool SPECT. Materials were 87 patients of 50 old myocardial infarctions (OMIs), 19 hypertrophic cardiomyopathies (HCMs), 2 dilated cardiomyopathies (DCMs) and 16 others. After intravenous injection of 111-185 MBq (3-5 mCi) of Tl-201 at rest, the projection data were acquired using a rotating gamma-camera through 180deg, from RAO 45deg in 24 directions, each of which consisted of 80-100 beats. For the reconstruction of ED, ES and non-gated images, R-R interval was divided into about 20 (18-22) fractions. In 348 regions of interest (anterior, septal, lateral and inferior wall) in 87 cases, wall motion and the Tl-201 uptake were evaluated to three grades (normal, hypokinesis and akinesis; normal, low and defect, respectively), which were compared with the wall motion of the ECG-gated blood pool SPECT. The wall motion and the grade of the Tl-201 uptake of the ECG-gated myocardial SPECT correlated well with the wall motion of the ECG-gated blood pool SPECT (96.6% and 87.9%, respectively). In conclusion, the ECG-gated myocardial SPECT can provide clear perfusion images and is a very useful diagnostic strategy to evaluate the regional wall motion and perfusion simultaneously. (author)

Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.

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Zhu, Jianwei; Zhang, Haicang; Li, Shuai Cheng; Wang, Chao; Kong, Lupeng; Sun, Shiwei; Zheng, Wei-Mou; Bu, Dongbo

2017-12-01

Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge. In this study, we present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. DCNN has showed excellent performance in image feature extraction and image recognition; the rational underlying the application of DCNN for fold recognition is that contact likelihood maps are essentially analogy to images, as they both display compositional hierarchy. Experimental results on the LINDAHL dataset suggest that even using the extracted fold-specific features alone, our approach achieved success rate comparable to the state-of-the-art approaches. When further combining these features with traditional alignment-related features, the success rate of our approach increased to 92.3%, 82.5% and 78.8% at family, superfamily and fold levels, respectively, which is about 18% higher than the state-of-the-art approach at fold level, 6% higher at superfamily level and 1% higher at family level. An independent assessment on SCOP_TEST dataset showed consistent

A novel framework for feature extraction in multi-sensor action potential sorting.

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Wu, Shun-Chi; Swindlehurst, A Lee; Nenadic, Zoran

2015-09-30

Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently. In this paper, we propose a novel framework for multi-sensor AP feature extraction based on the so-called Matched Subspace Detector (MSD), which is shown to be a natural generalization of standard single-sensor algorithms. Clustering using both simulated data and real AP recordings taken in the locust antennal lobe demonstrates that the proposed approach yields features that are discriminatory and lead to promising results. Unlike existing methods, the proposed algorithm finds joint spatio-temporal feature vectors that match the dominant subspace observed in the two-dimensional data without needs for a forward propagation model and AP templates. The proposed MSD approach provides more discriminatory features for unsupervised AP sorting applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Alexander fractional differential window filter for ECG denoising.

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Verma, Atul Kumar; Saini, Indu; Saini, Barjinder Singh

2018-06-01

The electrocardiogram (ECG) non-invasively monitors the electrical activities of the heart. During the process of recording and transmission, ECG signals are often corrupted by various types of noises. Minimizations of these noises facilitate accurate detection of various anomalies. In the present paper, Alexander fractional differential window (AFDW) filter is proposed for ECG signal denoising. The designed filter is based on the concept of generalized Alexander polynomial and the R-L differential equation of fractional calculus. This concept is utilized to formulate a window that acts as a forward filter. Thereafter, the backward filter is constructed by reversing the coefficients of the forward filter. The proposed AFDW filter is then obtained by averaging of the forward and backward filter coefficients. The performance of the designed AFDW filter is validated by adding the various type of noise to the original ECG signal obtained from MIT-BIH arrhythmia database. The two non-diagnostic measure, i.e., SNR, MSE, and one diagnostic measure, i.e., wavelet energy based diagnostic distortion (WEDD) have been employed for the quantitative evaluation of the designed filter. Extensive experimentations on all the 48-records of MIT-BIH arrhythmia database resulted in average SNR of 22.014 ± 3.806365, 14.703 ± 3.790275, 13.3183 ± 3.748230; average MSE of 0.001458 ± 0.00028, 0.0078 ± 0.000319, 0.01061 ± 0.000472; and average WEDD value of 0.020169 ± 0.01306, 0.1207 ± 0.061272, 0.1432 ± 0.073588, for ECG signal contaminated by the power line, random, and the white Gaussian noise respectively. A new metric named as morphological power preservation measure (MPPM) is also proposed that account for the power preservance (as indicated by PSD plots) and the QRS morphology. The proposed AFDW filter retained much of the original (clean) signal power without any significant morphological distortion as validated by MPPM measure that were 0

a Landmark Extraction Method Associated with Geometric Features and Location Distribution

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Zhang, W.; Li, J.; Wang, Y.; Xiao, Y.; Liu, P.; Zhang, S.

2018-04-01

Landmark plays an important role in spatial cognition and spatial knowledge organization. Significance measuring model is the main method of landmark extraction. It is difficult to take account of the spatial distribution pattern of landmarks because that the significance of landmark is built in one-dimensional space. In this paper, we start with the geometric features of the ground object, an extraction method based on the target height, target gap and field of view is proposed. According to the influence region of Voronoi Diagram, the description of target gap is established to the geometric representation of the distribution of adjacent targets. Then, segmentation process of the visual domain of Voronoi K order adjacent is given to set up target view under the multi view; finally, through three kinds of weighted geometric features, the landmarks are identified. Comparative experiments show that this method has a certain coincidence degree with the results of traditional significance measuring model, which verifies the effectiveness and reliability of the method and reduces the complexity of landmark extraction process without losing the reference value of landmark.

A protocol for a prospective observational study using chest and thumb ECG: transient ECG assessment in stroke evaluation (TEASE) in Sweden.

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Magnusson, Peter; Koyi, Hirsh; Mattsson, Gustav

2018-04-03

Atrial fibrillation (AF) causes ischaemic stroke and based on risk factor evaluation warrants anticoagulation therapy. In stroke survivors, AF is typically detected with short-term ECG monitoring in the stroke unit. Prolonged continuous ECG monitoring requires substantial resources while insertable cardiac monitors are invasive and costly. Chest and thumb ECG could provide an alternative for AF detection poststroke.The primary objective of our study is to assess the incidence of newly diagnosed AF during 28 days of chest and thumb ECG monitoring in cryptogenic stroke. Secondary objectives are to assess health-related quality of life (HRQoL) using short-form health survey (SF-36) and the feasibility of the Coala Heart Monitor in patients who had a stroke. Stroke survivors in Region Gävleborg, Sweden, will be eligible for the study from October 2017. Patients with a history of ischaemic stroke without documented AF before or during ECG evaluation in the stroke unit will be evaluated by the chest and thumb ECG system Coala Heart Monitor. The monitoring system is connected to a smartphone application which allows for remote monitoring and prompt advice on clinical management. Over a period of 28 days, patients will be monitored two times a day and may activate the ECG recording at symptoms. On completion, the system is returned by mail. This system offers a possibility to evaluate the presence of AF poststroke, but the feasibility of this system in patients who recently suffered from a stroke is unknown. In addition, HRQoL using SF-36 in comparison to Swedish population norms will be assessed. The feasibility of the Coala Heart Monitor will be assessed by a self-developed questionnaire. The study was approved by The Regional Ethical Committee in Uppsala (2017/321). The database will be closed after the last follow-up, followed by statistical analyses, interpretation of results and dissemination to a scientific journal. NCT03301662; Pre-results. © Article author

Human Identification by Cross-Correlation and Pattern Matching of Personalized Heartbeat: Influence of ECG Leads and Reference Database Size.

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Jekova, Irena; Krasteva, Vessela; Schmid, Ramun

2018-01-27

Human identification (ID) is a biometric task, comparing single input sample to many stored templates to identify an individual in a reference database. This paper aims to present the perspectives of personalized heartbeat pattern for reliable ECG-based identification. The investigations are using a database with 460 pairs of 12-lead resting electrocardiograms (ECG) with 10-s durations recorded at time-instants T1 and T2 > T1 + 1 year. Intra-subject long-term ECG stability and inter-subject variability of personalized PQRST (500 ms) and QRS (100 ms) patterns is quantified via cross-correlation, amplitude ratio and pattern matching between T1 and T2 using 7 features × 12-leads. Single and multi-lead ID models are trained on the first 230 ECG pairs. Their validation on 10, 20, ... 230 reference subjects (RS) from the remaining 230 ECG pairs shows: (i) two best single-lead ID models using lead II for a small population RS = (10-140) with identification accuracy AccID = (89.4-67.2)% and aVF for a large population RS = (140-230) with AccID = (67.2-63.9)%; (ii) better performance of the 6-lead limb vs. the 6-lead chest ID model-(91.4-76.1)% vs. (90.9-70)% for RS = (10-230); (iii) best performance of the 12-lead ID model-(98.4-87.4)% for RS = (10-230). The tolerable reference database size, keeping AccID > 80%, is RS = 30 in the single-lead ID scenario (II); RS = 50 (6 chest leads); RS = 100 (6 limb leads), RS > 230-maximal population in this study (12-lead ECG).

Left Ventricular Hypertrophy: An allometric comparative analysis of different ECG markers

International Nuclear Information System (INIS)

Bonomini, MP; Valentinuzzi, M E; Arini, P D; Ingallina, F; Barone, V

2011-01-01

Allometry, in general biology, measures the relative growth of a part in relation to the whole living organism. Left ventricular hypertrophy (LVH) is the heart adaptation to excessive load (systolic or diastolic). The increase in left ventricular mass leads to an increase in the electrocardiographic voltages. Based on clinical data, we compared the allometric behavior of three different ECG markers of LVH. To do this, the allometric fit AECG δ + β (VM) relating left ventricular mass (estimated from ecocardiographic data) and ECG amplitudes (expressed as the Cornell-Voltage, Sokolow and the ECG overall voltage indexes) were compared. Besides, sensitivity and specificity for each index were analyzed. The more sensitive the ECG criteria, the better the allometric fit. In conclusion: The allometric paradigm should be regarded as the way to design new and more sensitive ECG-based LVH markers.

Texture Feature Extraction and Classification for Iris Diagnosis

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Ma, Lin; Li, Naimin

Appling computer aided techniques in iris image processing, and combining occidental iridology with the traditional Chinese medicine is a challenging research area in digital image processing and artificial intelligence. This paper proposes an iridology model that consists the iris image pre-processing, texture feature analysis and disease classification. To the pre-processing, a 2-step iris localization approach is proposed; a 2-D Gabor filter based texture analysis and a texture fractal dimension estimation method are proposed for pathological feature extraction; and at last support vector machines are constructed to recognize 2 typical diseases such as the alimentary canal disease and the nerve system disease. Experimental results show that the proposed iridology diagnosis model is quite effective and promising for medical diagnosis and health surveillance for both hospital and public use.

Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos

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Li Yao

2016-01-01

Full Text Available Both static features and motion features have shown promising performance in human activities recognition task. However, the information included in these features is insufficient for complex human activities. In this paper, we propose extracting relational information of static features and motion features for human activities recognition. The videos are represented by a classical Bag-of-Word (BoW model which is useful in many works. To get a compact and discriminative codebook with small dimension, we employ the divisive algorithm based on KL-divergence to reconstruct the codebook. After that, to further capture strong relational information, we construct a bipartite graph to model the relationship between words of different feature set. Then we use a k-way partition to create a new codebook in which similar words are getting together. With this new codebook, videos can be represented by a new BoW vector with strong relational information. Moreover, we propose a method to compute new clusters from the divisive algorithm’s projective function. We test our work on the several datasets and obtain very promising results.

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR FACE RECOGNITION USING GABOR FEATURE EXTRACTION

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Muthukannan K

2013-11-01

Full Text Available Face detection and recognition is the first step for many applications in various fields such as identification and is used as a key to enter into the various electronic devices, video surveillance, and human computer interface and image database management. This paper focuses on feature extraction in an image using Gabor filter and the extracted image feature vector is then given as an input to the neural network. The neural network is trained with the input data. The Gabor wavelet concentrates on the important components of the face including eye, mouth, nose, cheeks. The main requirement of this technique is the threshold, which gives privileged sensitivity. The threshold values are the feature vectors taken from the faces. These feature vectors are given into the feed forward neural network to train the network. Using the feed forward neural network as a classifier, the recognized and unrecognized faces are classified. This classifier attains a higher face deduction rate. By training more input vectors the system proves to be effective. The effectiveness of the proposed method is demonstrated by the experimental results.

A Pilot Study Assessing ECG versus ECHO Ventriculoventricular Optimization in Pediatric Resynchronization Patients.

Science.gov (United States)

Punn, Rajesh; Hanisch, Debra; Motonaga, Kara S; Rosenthal, David N; Ceresnak, Scott R; Dubin, Anne M

2016-02-01

Cardiac resynchronization therapy indications and management are well described in adults. Echocardiography (ECHO) has been used to optimize mechanical synchrony in these patients; however, there are issues with reproducibility and time intensity. Pediatric patients add challenges, with diverse substrates and limited capacity for cooperation. Electrocardiographic (ECG) methods to assess electrical synchrony are expeditious but have not been extensively studied in children. We sought to compare ECHO and ECG CRT optimization in children. Prospective, pediatric, single-center cross-over trial comparing ECHO and ECG optimization with CRT. Patients were assigned to undergo either ECHO or ECG optimization, followed for 6 months, and crossed-over to the other assignment for another 6 months. ECHO pulsed-wave tissue Doppler and 12-lead ECG were obtained for 5 VV delays. ECG optimization was defined as the shortest QRSD and ECHO optimization as the lowest dyssynchrony index. ECHOs/ECGs were interpreted by readers blinded to optimization technique. After each 6 month period, these data were collected: ejection fraction, velocimetry-derived cardiac index, quality of life, ECHO-derived stroke distance, M-mode dyssynchrony, study cost, and time. Outcomes for each optimization method were compared. From June 2012 to December 2013, 19 patients enrolled. Mean age was 9.1 ± 4.3 years; 14 (74%) had structural heart disease. The mean time for optimization was shorter using ECG than ECHO (9 ± 1 min vs. 68 ± 13 min, P cost for charges was $4,400 ± 700 less for ECG. No other outcome differed between groups. ECHO optimization of synchrony was not superior to ECG optimization in this pilot study. ECG optimization required less time and cost than ECHO optimization. © 2015 Wiley Periodicals, Inc.

[An Algorithm to Eliminate Power Frequency Interference in ECG Using Template].

Science.gov (United States)

Shi, Guohua; Li, Jiang; Xu, Yan; Feng, Liang

2017-01-01

Researching an algorithm to eliminate power frequency interference in ECG. The algorithm first creates power frequency interference template, then, subtracts the template from the original ECG signals, final y, the algorithm gets the ECG signals without interference. Experiment shows the algorithm can eliminate interference effectively and has none side effect to normal signal. It’s efficient and suitable for practice.

ECG biometric identification: A compression based approach.

Science.gov (United States)

Bras, Susana; Pinho, Armando J

2015-08-01

Using the electrocardiogram signal (ECG) to identify and/or authenticate persons are problems still lacking satisfactory solutions. Yet, ECG possesses characteristics that are unique or difficult to get from other signals used in biometrics: (1) it requires contact and liveliness for acquisition (2) it changes under stress, rendering it potentially useless if acquired under threatening. Our main objective is to present an innovative and robust solution to the above-mentioned problem. To successfully conduct this goal, we rely on information-theoretic data models for data compression and on similarity metrics related to the approximation of the Kolmogorov complexity. The proposed measure allows the comparison of two (or more) ECG segments, without having to follow traditional approaches that require heartbeat segmentation (described as highly influenced by external or internal interferences). As a first approach, the method was able to cluster the data in three groups: identical record, same participant, different participant, by the stratification of the proposed measure with values near 0 for the same participant and closer to 1 for different participants. A leave-one-out strategy was implemented in order to identify the participant in the database based on his/her ECG. A 1NN classifier was implemented, using as distance measure the method proposed in this work. The classifier was able to identify correctly almost all participants, with an accuracy of 99% in the database used.

Fractal Complexity-Based Feature Extraction Algorithm of Communication Signals

Science.gov (United States)

Wang, Hui; Li, Jingchao; Guo, Lili; Dou, Zheng; Lin, Yun; Zhou, Ruolin

How to analyze and identify the characteristics of radiation sources and estimate the threat level by means of detecting, intercepting and locating has been the central issue of electronic support in the electronic warfare, and communication signal recognition is one of the key points to solve this issue. Aiming at accurately extracting the individual characteristics of the radiation source for the increasingly complex communication electromagnetic environment, a novel feature extraction algorithm for individual characteristics of the communication radiation source based on the fractal complexity of the signal is proposed. According to the complexity of the received signal and the situation of environmental noise, use the fractal dimension characteristics of different complexity to depict the subtle characteristics of the signal to establish the characteristic database, and then identify different broadcasting station by gray relation theory system. The simulation results demonstrate that the algorithm can achieve recognition rate of 94% even in the environment with SNR of -10dB, and this provides an important theoretical basis for the accurate identification of the subtle features of the signal at low SNR in the field of information confrontation.

Real-Time ECG Simulation for Hybrid Mock Circulatory Loops.

Science.gov (United States)

Korn, Leonie; Rüschen, Daniel; Zander, Niklas; Leonhardt, Steffen; Walter, Marian

2018-02-01

Classically, mock circulatory loops only simulate mechanical properties of the circulation. To connect the hydraulic world with electrophysiology, we present a real-time electrical activity model of the heart and show how to integrate this model into a real-time mock loop simulation. The model incorporates a predefined conduction pathway and a simplified volume conductor to solve the bidomain equations and the forward problem of electrocardiography, resulting in a physiological simulation of the electrocardiogram (ECG) at arbitrary electrode positions. A complete physiological simulation of the heart's excitation would be too CPU intensive. Thus, in our model, complexity was reduced to allow real-time simulation of ECG-triggered medical systems in vitro; this decreases time and cost in the development process. Conversely, the presented model can still be adapted to various pathologies by locally changing the properties of the heart's conduction pathway. To simulate the ECG, the heart is divided into suitable areas, which are innervated by the hierarchically structured conduction system. To distinguish different cardiac regions, a segmentation of the heart was performed. In these regions, Prim's algorithm was applied to identify the directed minimal spanning trees for conduction orientation. Each node of the tree was assigned to a cardiac action potential generated by its hybrid automaton to represent the heart's conduction system by the spatial distribution of action potentials. To generate the ECG output, the bidomain equations were implemented and a simple model of the volume conductor of the body was used to solve the forward problem of electrocardiography. As a result, the model simulates potentials at arbitrary electrode positions in real-time. To verify the developed real-time ECG model, measurements were made within a hybrid mock circulatory loop, including a simple ECG-triggered ventricular assist device control. The model's potential value is to simulate

Skipping the real world: Classification of PolSAR images without explicit feature extraction

Science.gov (United States)

Hänsch, Ronny; Hellwich, Olaf

2018-06-01

The typical processing chain for pixel-wise classification from PolSAR images starts with an optional preprocessing step (e.g. speckle reduction), continues with extracting features projecting the complex-valued data into the real domain (e.g. by polarimetric decompositions) which are then used as input for a machine-learning based classifier, and ends in an optional postprocessing (e.g. label smoothing). The extracted features are usually hand-crafted as well as preselected and represent (a somewhat arbitrary) projection from the complex to the real domain in order to fit the requirements of standard machine-learning approaches such as Support Vector Machines or Artificial Neural Networks. This paper proposes to adapt the internal node tests of Random Forests to work directly on the complex-valued PolSAR data, which makes any explicit feature extraction obsolete. This approach leads to a classification framework with a significantly decreased computation time and memory footprint since no image features have to be computed and stored beforehand. The experimental results on one fully-polarimetric and one dual-polarimetric dataset show that, despite the simpler approach, accuracy can be maintained (decreased by only less than 2 % for the fully-polarimetric dataset) or even improved (increased by roughly 9 % for the dual-polarimetric dataset).

EEMD Independent Extraction for Mixing Features of Rotating Machinery Reconstructed in Phase Space

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Zaichao Ma

2015-04-01

Full Text Available Empirical Mode Decomposition (EMD, due to its adaptive decomposition property for the non-linear and non-stationary signals, has been widely used in vibration analyses for rotating machinery. However, EMD suffers from mode mixing, which is difficult to extract features independently. Although the improved EMD, well known as the ensemble EMD (EEMD, has been proposed, mode mixing is alleviated only to a certain degree. Moreover, EEMD needs to determine the amplitude of added noise. In this paper, we propose Phase Space Ensemble Empirical Mode Decomposition (PSEEMD integrating Phase Space Reconstruction (PSR and Manifold Learning (ML for modifying EEMD. We also provide the principle and detailed procedure of PSEEMD, and the analyses on a simulation signal and an actual vibration signal derived from a rubbing rotor are performed. The results show that PSEEMD is more efficient and convenient than EEMD in extracting the mixing features from the investigated signal and in optimizing the amplitude of the necessary added noise. Additionally PSEEMD can extract the weak features interfered with a certain amount of noise.

Three-Dimensional Precession Feature Extraction of Ballistic Targets Based on Narrowband Radar Network

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Zhao Shuang

2017-02-01

Full Text Available Micro-motion is a crucial feature used in ballistic target recognition. To address the problem that single-view observations cannot extract true micro-motion parameters, we propose a novel algorithm based on the narrowband radar network to extract three-dimensional precession features. First, we construct a precession model of the cone-shaped target, and as a precondition, we consider the invisible problem of scattering centers. We then analyze in detail the micro-Doppler modulation trait caused by the precession. Then, we match each scattering center in different perspectives based on the ratio of the top scattering center’s micro-Doppler frequency modulation coefficient and extract the 3D coning vector of the target by establishing associated multi-aspect equation systems. In addition, we estimate feature parameters by utilizing the correlation of the micro-Doppler frequency modulation coefficient of the three scattering centers combined with the frequency compensation method. We then calculate the coordinates of the conical point in each moment and reconstruct the 3D spatial portion. Finally, we provide simulation results to validate the proposed algorithm.

ECG-cryptography and authentication in body area networks.

Science.gov (United States)

Zhang, Zhaoyang; Wang, Honggang; Vasilakos, Athanasios V; Fang, Hua

2012-11-01

Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data communications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.

Joint Markov Blankets in Feature Sets Extracted from Wavelet Packet Decompositions

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Gert Van Dijck

2011-07-01

Full Text Available Since two decades, wavelet packet decompositions have been shown effective as a generic approach to feature extraction from time series and images for the prediction of a target variable. Redundancies exist between the wavelet coefficients and between the energy features that are derived from the wavelet coefficients. We assess these redundancies in wavelet packet decompositions by means of the Markov blanket filtering theory. We introduce the concept of joint Markov blankets. It is shown that joint Markov blankets are a natural extension of Markov blankets, which are defined for single features, to a set of features. We show that these joint Markov blankets exist in feature sets consisting of the wavelet coefficients. Furthermore, we prove that wavelet energy features from the highest frequency resolution level form a joint Markov blanket for all other wavelet energy features. The joint Markov blanket theory indicates that one can expect an increase of classification accuracy with the increase of the frequency resolution level of the energy features.

A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures

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T. M. McGinnity

2005-11-01

Full Text Available The paper presents an investigation into a time-frequency (TF method for extracting features from the electroencephalogram (EEG recorded from subjects performing imagination of left- and right-hand movements. The feature extraction procedure (FEP extracts frequency domain information to form features whilst time-frequency resolution is attained by localising the fast Fourier transformations (FFTs of the signals to specific windows localised in time. All features are extracted at the rate of the signal sampling interval from a main feature extraction (FE window through which all data passes. Subject-specific frequency bands are selected for optimal feature extraction and intraclass variations are reduced by smoothing the spectra for each signal by an interpolation (IP process. The TF features are classified using linear discriminant analysis (LDA. The FE window has potential advantages for the FEP to be applied in an online brain-computer interface (BCI. The approach achieves good performance when quantified by classification accuracy (CA rate, information transfer (IT rate, and mutual information (MI. The information that these performance measures provide about a BCI system is analysed and the importance of this is demonstrated through the results.

Development of a portable Linux-based ECG measurement and monitoring system.

Science.gov (United States)

Tan, Tan-Hsu; Chang, Ching-Su; Huang, Yung-Fa; Chen, Yung-Fu; Lee, Cheng

2011-08-01

This work presents a portable Linux-based electrocardiogram (ECG) signals measurement and monitoring system. The proposed system consists of an ECG front end and an embedded Linux platform (ELP). The ECG front end digitizes 12-lead ECG signals acquired from electrodes and then delivers them to the ELP via a universal serial bus (USB) interface for storage, signal processing, and graphic display. The proposed system can be installed anywhere (e.g., offices, homes, healthcare centers and ambulances) to allow people to self-monitor their health conditions at any time. The proposed system also enables remote diagnosis via Internet. Additionally, the system has a 7-in. interactive TFT-LCD touch screen that enables users to execute various functions, such as scaling a single-lead or multiple-lead ECG waveforms. The effectiveness of the proposed system was verified by using a commercial 12-lead ECG signal simulator and in vivo experiments. In addition to its portability, the proposed system is license-free as Linux, an open-source code, is utilized during software development. The cost-effectiveness of the system significantly enhances its practical application for personal healthcare.

Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey

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Saleem Riaz

2017-02-01

Full Text Available Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, developing effective bearing fault diagnostic method using different fault features at different steps becomes more attractive. Bearings are widely used in medical applications, food processing industries, semi-conductor industries, paper making industries and aircraft components. This paper review has demonstrated that the latest reviews applied to rotating machinery on the available a variety of vibration feature extraction. Generally literature is classified into two main groups: frequency domain, time frequency analysis. However, fault detection and diagnosis of rotating machine vibration signal processing methods to present their own limitations. In practice, most healthy ingredients faulty vibration signal from background noise and mechanical vibration signals are buried. This paper also reviews that how the advanced signal processing methods, empirical mode decomposition and interference cancellation algorithm has been investigated and developed. The condition for rotating machines based rehabilitation, prevent failures increase the availability and reduce the cost of maintenance is becoming necessary too. Rotating machine fault detection and diagnostics in developing algorithms signal processing based on a key problem is the fault feature extraction or quantification. Currently, vibration signal, fault detection and diagnosis of rotating machinery based techniques most widely used techniques. Furthermore, the researchers are widely interested to make automatic

Representation and Metrics Extraction from Feature Basis: An Object Oriented Approach

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Fausto Neri da Silva Vanin

2010-10-01

Full Text Available This tutorial presents an object oriented approach to data reading and metrics extraction from feature basis. Structural issues about basis are discussed first, then the Object Oriented Programming (OOP is aplied to modeling the main elements in this context. The model implementation is then discussed using C++ as programing language. To validate the proposed model, we apply on some feature basis from the University of Carolina, Irvine Machine Learning Database.

Integrated processing of ECG's in a hospital information system

NARCIS (Netherlands)

Helder, J.C.; Schram, P.H.; Verwey, H.; Meijler, F.L.; Robles de Medina, E.O.

The ECG handling in the University Hospital of Utrecht is composed by a system consisting of acquisition and storage of ECG signals, computer analysis, data management, and storage of readings in a patient data base. The last two modules are part of a Hospital Information System (HIS). The modular

Optimization of Ecg Gating in Quantitative Femoral Angiography

International Nuclear Information System (INIS)

Nilsson, S.; Berglund, I.; Erikson, U.; Johansson, J.; Walldius, G.

2003-01-01

Purpose: To determine which phase of the heart cycle would yield the highest reproducibility in measuring atherosclerosis-related variables such as arterial lumen volume and edge roughness. Material and Methods: 35 patients with hypercholesterolemia underwent select ive femoral angiography, repeated four times at 10-min intervals. The angiographies were performed with Ecg-gated exposures. In angiographies 1 and 2 the delay from R-wave maximum to each exposure was 0.1 s, in angiographies 3 and 4 the delay was 0.1, 0.3, 0.5 or 0.7 s or the exposures were performed 1/s without Ecg gating. Arterial lumen volume and edge roughness were measured in a 20-cm segment of the superficial femoral artery using a computer-based densitometric method. Measurement reproducibility was determined by comparing angiographies 1-2 and angiographies 3-4. Results: When measuring arterial lumen volume and edge roughness of a 20-cm segment of the femoral artery, reproducibility was not dependent on Ecg gating. In measuring single arterial diameters and cross-sectional areas, the reproducibility was better when exposures were made 0.1 s after the R-wave maximum than when using other settings of the Ecg gating device or without Ecg gating. Conclusion: The influence of pulsatile flow upon quantitative measurement in femoral angiograms seems to be the smallest possible in early systole, as can be demonstrated when measuring single diameters and cross-sectional areas. In variables based on integration over longer segments, measurement reproducibility seems to be independent of phase

Optimization of Ecg Gating in Quantitative Femoral Angiography

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Nilsson, S.; Berglund, I.; Erikson, U. [Univ. Hospital, Uppsala (Sweden). Dept. of Oncology, Radiology and Clinical Immunology; Johansson, J.; Walldius, G. [Karolinska Hospital, Stockholm (Sweden). King Gustav V Research Inst.

2003-09-01

Purpose: To determine which phase of the heart cycle would yield the highest reproducibility in measuring atherosclerosis-related variables such as arterial lumen volume and edge roughness. Material and Methods: 35 patients with hypercholesterolemia underwent select ive femoral angiography, repeated four times at 10-min intervals. The angiographies were performed with Ecg-gated exposures. In angiographies 1 and 2 the delay from R-wave maximum to each exposure was 0.1 s, in angiographies 3 and 4 the delay was 0.1, 0.3, 0.5 or 0.7 s or the exposures were performed 1/s without Ecg gating. Arterial lumen volume and edge roughness were measured in a 20-cm segment of the superficial femoral artery using a computer-based densitometric method. Measurement reproducibility was determined by comparing angiographies 1-2 and angiographies 3-4. Results: When measuring arterial lumen volume and edge roughness of a 20-cm segment of the femoral artery, reproducibility was not dependent on Ecg gating. In measuring single arterial diameters and cross-sectional areas, the reproducibility was better when exposures were made 0.1 s after the R-wave maximum than when using other settings of the Ecg gating device or without Ecg gating. Conclusion: The influence of pulsatile flow upon quantitative measurement in femoral angiograms seems to be the smallest possible in early systole, as can be demonstrated when measuring single diameters and cross-sectional areas. In variables based on integration over longer segments, measurement reproducibility seems to be independent of phase.

Smartphone ECG for evaluation of STEMI: results of the ST LEUIS Pilot Study.

Science.gov (United States)

Muhlestein, Joseph Boone; Le, Viet; Albert, David; Moreno, Fidela Ll; Anderson, Jeffrey L; Yanowitz, Frank; Vranian, Robert B; Barsness, Gregory W; Bethea, Charles F; Severance, Harry W; Ramo, Barry; Pierce, John; Barbagelata, Alejandro; Muhlestein, Joseph Brent

2015-01-01

12-lead ECG is a critical component of initial evaluation of cardiac ischemia, but has traditionally been limited to large, dedicated equipment in medical care environments. Smartphones provide a potential alternative platform for the extension of ECG to new care settings and to improve timeliness of care. To gain experience with smartphone electrocardiography prior to designing a larger multicenter study evaluating standard 12-lead ECG compared to smartphone ECG. 6 patients for whom the hospital STEMI protocol was activated were evaluated with traditional 12-lead ECG followed immediately by a smartphone ECG using right (VnR) and left (VnL) limb leads for precordial grounding. The AliveCor™ Heart Monitor was utilized for this study. All tracings were taken prior to catheterization or immediately after revascularization while still in the catheterization laboratory. The smartphone ECG had excellent correlation with the gold standard 12-lead ECG in all patients. Four out of six tracings were judged to meet STEMI criteria on both modalities as determined by three experienced cardiologists, and in the remaining two, consensus indicated a non-STEMI ECG diagnosis. No significant difference was noted between VnR and VnL. Smartphone based electrocardiography is a promising, developing technology intended to increase availability and speed of electrocardiographic evaluation. This study confirmed the potential of a smartphone ECG for evaluation of acute ischemia and the feasibility of studying this technology further to define the diagnostic accuracy, limitations and appropriate use of this new technology. Copyright © 2015 Elsevier Inc. All rights reserved.

Statistical performance evaluation of ECG transmission using wireless networks.

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Shakhatreh, Walid; Gharaibeh, Khaled; Al-Zaben, Awad

2013-07-01

This paper presents simulation of the transmission of biomedical signals (using ECG signal as an example) over wireless networks. Investigation of the effect of channel impairments including SNR, pathloss exponent, path delay and network impairments such as packet loss probability; on the diagnosability of the received ECG signal are presented. The ECG signal is transmitted through a wireless network system composed of two communication protocols; an 802.15.4- ZigBee protocol and an 802.11b protocol. The performance of the transmission is evaluated using higher order statistics parameters such as kurtosis and Negative Entropy in addition to the common techniques such as the PRD, RMS and Cross Correlation.

Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery

Directory of Open Access Journals (Sweden)

Lin Liang

2015-01-01

Full Text Available A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness. Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space. The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space. To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes. Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring.

An image-processing methodology for extracting bloodstain pattern features.

Science.gov (United States)

Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

2017-08-01

There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

Mobile application development for Tele-ECG

International Nuclear Information System (INIS)

Srivastava, Shikha; Bharade, Sandeep; Sinha, Vineet; Sarade, Bhagyashree; Jindal, G.D.; Ananthakrishnan, T.S.; Pithawa, C.K.

2010-01-01

Mobile computing has caught the attention of research community for quite some time. The constant improvement of hardware and software related to mobile computing (e.g. better computing power, larger wireless network bandwidth) clearly enhance capabilities of mobile devices. The acceptance of mobile technology by the population at large would suggest that this could be the basis of a system for the communication of medical data from patients to remote physician and vice versa. This paper presents a mobile solution, which makes use of a Tele-ECG unit with a mobile phone to collect, store and forward ECG data to a cardiologist for diagnosis and recommendation. (author)

Decomposition of ECG by linear filtering.

Science.gov (United States)

Murthy, I S; Niranjan, U C

1992-01-01

A simple method is developed for the delineation of a given electrocardiogram (ECG) signal into its component waves. The properties of discrete cosine transform (DCT) are exploited for the purpose. The transformed signal is convolved with appropriate filters and the component waves are obtained by computing the inverse transform (IDCT) of the filtered signals. The filters are derived from the time signal itself. Analysis of continuous strips of ECG signals with various arrhythmias showed that the performance of the method is satisfactory both qualitatively and quantitatively. The small amplitude P wave usually had a high percentage rms difference (PRD) compared to the other large component waves.

The extraction and use of facial features in low bit-rate visual communication.

Science.gov (United States)

Pearson, D

1992-01-29

A review is given of experimental investigations by the author and his collaborators into methods of extracting binary features from images of the face and hands. The aim of the research has been to enable deaf people to communicate by sign language over the telephone network. Other applications include model-based image coding and facial-recognition systems. The paper deals with the theoretical postulates underlying the successful experimental extraction of facial features. The basic philosophy has been to treat the face as an illuminated three-dimensional object and to identify features from characteristics of their Gaussian maps. It can be shown that in general a composite image operator linked to a directional-illumination estimator is required to accomplish this, although the latter can often be omitted in practice.

Average combination difference morphological filters for fault feature extraction of bearing

Science.gov (United States)

Lv, Jingxiang; Yu, Jianbo

2018-02-01

In order to extract impulse components from vibration signals with much noise and harmonics, a new morphological filter called average combination difference morphological filter (ACDIF) is proposed in this paper. ACDIF constructs firstly several new combination difference (CDIF) operators, and then integrates the best two CDIFs as the final morphological filter. This design scheme enables ACIDF to extract positive and negative impacts existing in vibration signals to enhance accuracy of bearing fault diagnosis. The length of structure element (SE) that affects the performance of ACDIF is determined adaptively by a new indicator called Teager energy kurtosis (TEK). TEK further improves the effectiveness of ACDIF for fault feature extraction. Experimental results on the simulation and bearing vibration signals demonstrate that ACDIF can effectively suppress noise and extract periodic impulses from bearing vibration signals.

[Experience in the use of equipment for ECG system analysis in municipal polyclinics].

Science.gov (United States)

Bondarenko, A A

2006-01-01

Two electrocardiographs, an analog-digital electrocardiograph with preliminary analog filtering of signal and a smart cardiograph implemented as a PC-compatible device without preliminary analog filtering, are considered. Advantages and disadvantages of ECG systems based on artificial intelligence are discussed. ECG interpretation modes provided by the two electrocardiographs are considered. The reliability of automatic ECG interpretation is assessed. Problems of rational use of automated ECG processing systems are discussed.

A Hygroscopic Sensor Electrode for Fast Stabilized Non-Contact ECG Signal Acquisition.

Science.gov (United States)

Fong, Ee-May; Chung, Wan-Young

2015-08-05

A capacitive electrocardiography (cECG) technique using a non-invasive ECG measuring technology that does not require direct contact between the sensor and the skin has attracted much interest. The system encounters several challenges when the sensor electrode and subject's skin are weakly coupled. Because there is no direct physical contact between the subject and any grounding point, there is no discharge path for the built-up electrostatic charge. Subsequently, the electrostatic charge build-up can temporarily contaminate the ECG signal from being clearly visible; a stabilization period (3-15 min) is required for the measurement of a clean, stable ECG signal at low humidity levels (below 55% relative humidity). Therefore, to obtain a clear ECG signal without noise and to reduce the ECG signal stabilization time to within 2 min in a dry ambient environment, we have developed a fabric electrode with embedded polymer (FEEP). The designed hygroscopic FEEP has an embedded superabsorbent polymer layer. The principle of FEEP as a conductive electrode is to provide humidity to the capacitive coupling to ensure strong coupling and to allow for the measurement of a stable, clear biomedical signal. The evaluation results show that hygroscopic FEEP is capable of rapidly measuring high-accuracy ECG signals with a higher SNR ratio.

An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare.

Science.gov (United States)

Yang, Zhe; Zhou, Qihao; Lei, Lei; Zheng, Kan; Xiang, Wei

2016-12-01

Public healthcare has been paid an increasing attention given the exponential growth human population and medical expenses. It is well known that an effective health monitoring system can detect abnormalities of health conditions in time and make diagnoses according to the gleaned data. As a vital approach to diagnose heart diseases, ECG monitoring is widely studied and applied. However, nearly all existing portable ECG monitoring systems cannot work without a mobile application, which is responsible for data collection and display. In this paper, we propose a new method for ECG monitoring based on Internet-of-Things (IoT) techniques. ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi. Both the HTTP and MQTT protocols are employed in the IoT cloud in order to provide visual and timely ECG data to users. Nearly all smart terminals with a web browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue. Experiments are carried out on healthy volunteers in order to verify the reliability of the entire system. Experimental results reveal that the proposed system is reliable in collecting and displaying real-time ECG data, which can aid in the primary diagnosis of certain heart diseases.

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach.

Science.gov (United States)

Elgendi, Mohamed; Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab

2018-01-16

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

Directory of Open Access Journals (Sweden)

Mohamed Elgendi

2018-01-01

Full Text Available Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR, percentage root-mean-square difference (PRD, and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects, the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm.

Science.gov (United States)

Qin, Qin; Li, Jianqing; Yue, Yinggao; Liu, Chengyu

2017-01-01

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

Accurate Interpretation of the 12-Lead ECG Electrode Placement: A Systematic Review

Science.gov (United States)

Khunti, Kirti

2014-01-01

Background: Coronary heart disease (CHD) patients require monitoring through ECGs; the 12-lead electrocardiogram (ECG) is considered to be the non-invasive gold standard. Examples of incorrect treatment because of inaccurate or poor ECG monitoring techniques have been reported in the literature. The findings that only 50% of nurses and less than…

Respiratory Information Extraction from Electrocardiogram Signals

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