Multilevel Wavelet Packet Entropy and Support Vector Machine for Epileptic EEG Classification

Electroencephalogram (EEG) is a bioelectric signal produced by brain activity. The abnormalities that occur in the brain, such as epilepsy, can be seen through a particular pattern on the EEG signal. A recurrent unprovoked seizure occurs in epilepsy patients as a result of excessive brain cell activity. EEG is a non-linear and non-stationary signal, so a visual interpretation is difficult to conduct. One method to measure EEG characteristics is the entropy that quantifies the signal complexity. Several studies have been conducted to classify epileptic EEG signal using entropy as the feature set. Previous studies has shown a promising result for epileptic EEG signal classification. However, to achieve effectiveness for the classification process, we propose a new method to reduce the number of features with a competitive accuracy. In this research, we propose a wavelet-based entropy method named multilevel wavelet packet entropy (MWPE) for automatic EEG signal analysis. MWPE is calculated from the wavelet packet entropy (WPE) which performed at some decomposition level. WPE was calculated from wavelet packet decomposition (WPD) which give more informations in every signal subbands compared to discrete wavelet transform (DWT). Using MWPE, we got informations about the distribution of subband energy in every level of signal decomposition. MWPE and support vector machine (SVM) are used as the feature extraction and classifier respectively. The result showed that the method is able to classify three classes of the EEG data set (normal, interictal, seizure). The best accuracy is 94.3% which achieved by using a 1-5 decomposition level with biorthogonal 2.8 wavelet, and cubic or quadratic SVM. MWPE provides high accuracy with relatively few features.