This special issue of The Journal of Biomedical Research features novel studies on epileptic seizure detection and prediction based on advanced EEG signal processing and machine learning algorithms.The articles select...This special issue of The Journal of Biomedical Research features novel studies on epileptic seizure detection and prediction based on advanced EEG signal processing and machine learning algorithms.The articles selected present important findings including new experimental results and theoretical studies.展开更多
Epilepsy is the most common neurological disorder of the brain that affects people worldwide at any age from newborn to adult. It is characterized by recurrent seizures, which are brief episodes of signs or symptoms d...Epilepsy is the most common neurological disorder of the brain that affects people worldwide at any age from newborn to adult. It is characterized by recurrent seizures, which are brief episodes of signs or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. The electroencephalogram, or EEG, is a physiological method to measure and record the electrical展开更多
Autism spectrum disorder(ASD)is a neurodevelopmental disorder affecting social,communicative,and repetitive behavior.The phenotypic heterogeneity of ASD makes timely and accurate diagnosis challenging,requiring highly...Autism spectrum disorder(ASD)is a neurodevelopmental disorder affecting social,communicative,and repetitive behavior.The phenotypic heterogeneity of ASD makes timely and accurate diagnosis challenging,requiring highly trained clinical practitioners.The development of automated approaches to ASD classification,based on integrated psychophysiological measures,may one day help expedite the diagnostic process.This paper provides a novel contribution for classifing ASD using both thermographic and EEG data.The methodology used in this study extracts a variety of feature sets and evaluates the possibility of using several learning models.Mean,standard deviation,and entropy values of the EEG signals and mean temperature values of regions of interest(ROIs)in facial thermographic images were extracted as features.Feature selection is performed to filter less informative features based on correlation.The classification process utilizes Naive Bayes,random forest,logistic regression,and multi-layer perceptron algorithms.The integration of EEG and thermographic features have achieved an accuracy of 94%with both logistic regression and multi-layer perceptron classifiers.The results have shown that the classification accuracies of most of the learning models have increased after integrating facial thermographic data with EEG.展开更多
文摘This special issue of The Journal of Biomedical Research features novel studies on epileptic seizure detection and prediction based on advanced EEG signal processing and machine learning algorithms.The articles selected present important findings including new experimental results and theoretical studies.
文摘Epilepsy is the most common neurological disorder of the brain that affects people worldwide at any age from newborn to adult. It is characterized by recurrent seizures, which are brief episodes of signs or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. The electroencephalogram, or EEG, is a physiological method to measure and record the electrical
基金This work was supported by Old Dominion University,Norfolk,Virginia and University of Moratuwa,Sri Lanka.
文摘Autism spectrum disorder(ASD)is a neurodevelopmental disorder affecting social,communicative,and repetitive behavior.The phenotypic heterogeneity of ASD makes timely and accurate diagnosis challenging,requiring highly trained clinical practitioners.The development of automated approaches to ASD classification,based on integrated psychophysiological measures,may one day help expedite the diagnostic process.This paper provides a novel contribution for classifing ASD using both thermographic and EEG data.The methodology used in this study extracts a variety of feature sets and evaluates the possibility of using several learning models.Mean,standard deviation,and entropy values of the EEG signals and mean temperature values of regions of interest(ROIs)in facial thermographic images were extracted as features.Feature selection is performed to filter less informative features based on correlation.The classification process utilizes Naive Bayes,random forest,logistic regression,and multi-layer perceptron algorithms.The integration of EEG and thermographic features have achieved an accuracy of 94%with both logistic regression and multi-layer perceptron classifiers.The results have shown that the classification accuracies of most of the learning models have increased after integrating facial thermographic data with EEG.
