Empirical Wavelet Transform-Based Framework for Diagnosis of Epilepsy Using EEG Signals

Abstract

In the chapter, a novel yet simple method for classifying EEG signals associated with normal and epileptic seizure categories has been proposed. The proposed method is based on empirical wavelet transform (EWT). The non-stationarity in the EEG signal has been captured using EWT, and subsequently, the common minimum number of modes have been determined for each EEG signal. Features based on amplitude envelopes of EEG signals have been computed. The Kruskal-Wallis statistical test has been used to confirm the discrimination ability of feature space. For classification, various classifiers, namely K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT), have been used. The maximum classification accuracy of 98.67% is achieved with the K-nearest neighbor (KNN) classifier. The proposed approach has utilized only two features, which makes the proposed approach simpler. The proposed approach thus can be used in real-time applications.