Detection of Epileptic Seizure using Discrete Wavelet Transform on Gamma band and Artificial Neural Network

Abstract

Electroencephalography (EEG) is a valuable instrument for acquiring brain signals from the scalp surface area that correlate to many states, one of which is epilepsy, which is defined as a central nervous system disorder that generates periods of abnormal brain activity, often known as seizures. Based on the EEG signal, frequencies are ranging from 0.1 Hz to more than 100 Hz, these signals are classified as delta, theta, alpha, beta, and gamma. This paper uses the four features extracted from EEG signal to detect Epileptic Seizures, by using a combination of both Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) mainly using MATLAB. Bonn University EEG database has been used. The data was obtained using 128 channels and is divided into five different data classes: Z, N, O, F, and S. Each dataset class contains 100 segments taken from individual channels with a period of 23.6 seconds, or in other words, each dataset class contains 4097 pulses/samples with a sampling frequency of 173.61 Hz. Important statistical features were computed such as mean, standard deviation, skewness, and kurtosis. In this work only the gamma-band of the decomposed signal is used to extract the four statistical features, two classifiers are applied one to detect epilepsy and one to detect the seizure. the epilepsy classifier is 90.3 % accurate and the seizure classifier is 98.7% accurate.