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Editorial commentary on special issue of Advances in EEG Signal Processing and Machine Learning for Epileptic Seizure Detection and Prediction

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The Journal of Biomedical Research 2020年第3期34卷 149-150页

作者： larbi boubchir Laboratoire d'Informatique Avancee de Saint-Denis Research Lab. University of Paris&Saint-DenisCedex 93526France

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 *** articles selected present ... 详细信息

关键词： epileptic seizure electroencephalography(EEG) EEG signal processing machine learning feature extraction

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Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states

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The Journal of Biomedical Research 2020年第3期34卷 162-169页

作者： Itaf Ben Slimen larbi boubchir Hassene Seddik Centre de Recherche et de Production Research Lab. Ecole Nationale Superieure des Ingenieurs de TunisUniversity of TunisTunis 1008Tunisia Laboratoire d'Informatique Avancee de Saint-Denis Research Lab. University of Paris 8Saint-DenisCedex 93526France

Epileptic seizures are known for their unpredictable ***,recent research provides that the transition to seizure event is not random but the result of evidence ***,a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic *** periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes,spikes,and the *** this study,spike rate is used as the indicator to anticipate seizures in electroencephalogram(EEG) *** detection step is used in EEG signal during interictal,preictal,and ictal periods followed by a mean filter to smooth the spike *** maximum spike rate in interictal periods is used as an indicator to predict *** the spike number in the preictal period exceeds the threshold,an alarm is *** the CHB-MIT database,the proposed approach has ensured92% accuracy in seizure prediction for all patients.

关键词： electroencephalogram epilepsy seizure prediction spikes detection

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EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms

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The Journal of Biomedical Research 2020年第3期34卷 151-161页

作者： Itaf Ben Slimen larbi boubchir Zouhair Mbarki Hassene Seddik Centre de Recherche et de Production Research Lab. Ecole Nationale Superieure des Ingenieurs de TunisUniversity of TunisTunis 1008Tunisia Laboratoire d^nfbrmatique Avancee de Saint-Denis Research Lab. University of Paris 8Saint-DenisCedex 93526France

The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography(EEG) is an oversensitive operation and prone to errors,which has motivated the researchers to develop effective automated seizure detection *** paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these *** proposed method consists of three steps:(i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis(MSPCA),(ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition(EMD),discrete wavelet transform(DWT),and dual-tree complex wavelet transform(DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals,and(iii) allocate the feature vector to the relevant class(i.e.,seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine(SVM),k-nearest neighbor(k-NN),and linear discriminant analysis(LDA).The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping *** results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.

关键词： electroencephalography epileptic seizure detection feature extraction dual-tree complex wavelet transform machine learning

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