Discretization approach to EEG signal classification using Multilayer Perceptron Neural Network model

Abstract

Electroencephalogram (EEG) recording systems have been frequently used as the sources of information in diagnosis of epilepsy by several researchers. In this study, rearranged EEG signals were classified by Multilayer Perceptron Neural Network (MLPNN) model. Used data consists of five groups (A, B, C, D, and E) each containing 100 EEG segments. In this study, center points with equal interval were selected on amplitude axis of each EEG segment. EEG signals were rearranged by way of that each amplitude value was shifted to the center point closest to itself. Equal width discretization (EWD) method was used for rearrangement process. Wavelet coefficients of each segment of EEG signals were computed by using discrete wavelet transform (DWT). The mean, the standard deviation and the entropy of these coefficients was used as the inputs of MLPNN model. The model was protected from the overfitting by cross validation. Two different classification experiments were implemented by the same MLPNN model: 1) the classification of healthy volunteers, epilepsy patients during seizure and epilepsy patients during a seizure-free interval, 2) the classification of epilepsy patients during seizure and seizure-free interval. MLPNN model classified EEG signals with the accuracy of 99.60% in first experiment and 100% in second experiment. It is observed that MLPNN classification of EEG signals after rearrangement in amplitude axis provides better results.