Detection of Epileptic Seizures by the Analysis of EEG Signals Using Empirical Mode Decomposition

Abstract

The detection of epileptic seizure has a primary role in patient diagnosis with epilepsy. Epilepsy causes sudden and uncontrolled electrical discharges in brain cells. Recordings of the abnormal brain activities are time consuming and outcomes are very subjective, so automated detection systems are highly recommended. In this study, it is aimed to classify EEG signals for the detection of epileptic seizures using intrinsic mode functions (IMF) and feature extraction based on Empirical Mode Decomposition (EMD). These records have been acquired from the database of the Epileptology Department of Bonn University and consisting of 5 marker groups A, B, C, D, E in this study. These records taken from healthy individuals and patients are decomposed into IMFs by EMD method. Feature vectors have been extracted based on Tsallis Entropy, Renyi Entropy, Relative Entropy and Coherence methods. These features are then classified by K-Nearest Neighbors Classification (KNN), Linear Discriminant Analysis (LDA) and Naive Bayes Classification (NBC). Significant differences were determined between healthy and patient EEG data at the end of the study.