Automated Classification of Epileptic EEG Signals Based on Multi-Feature Extraction

摘要

In order to realize the fast and accurate automated detection and classification of EEG signals during the normal, inter-ictal and ictal periods of patients, we propose an automated classification method for feature extraction of epileptic EEG signals based on the sample entropy and fast-slow-wave energy ratio (FSR)of each frequency sub-band in this paper. EEG signals are decomposed into frequency sub-bands using wavelet packet decomposition (WPD)in this method. The SampEn and FSR of different sub-bands are calculated, which are used to form feature vectors and these vectors are used as inputs to three different classifiers: support vector machine (SVM), k-nearest neighbor (KNN) and probabilistic neural network (PNN), to evaluate four famous classification problems. Our results show that the SVM classifier using radial basis function (RBF)is able to distinguish the above four problems with high accuracy more than 98.67%.