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

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 methods.This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases.The 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 process.The 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.

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

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

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

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.