Comparative analysis of different classifiers on EEG signals for predicting epileptic seizure

Abstract

Epilepsy is a neurological disease that’s characterized by perennial seizures. In this neurological condition the transient electrical phenomenon within the brain occurs that produces an amendment in sensation, awareness, and behavior of an individuals that leads to risk. To understand the brain behavior Electroencephalogram (EEG) signals are used in six different sub-bands viz. Alpha (a ), Beta (ß ), Gamma1 (? 1), Gamma2 (? 2), Theta (? ) and Delta (d ). The Brainstorm software is used for visualizing, analyzing and filtration of EEG signals in each sub-band. This paper deals with the extraction of the various features in each sub-bands and different Machine Learning classifiers were used on these extracted features for comparative analysis in terms of Accuracy, prediction Speed and training time in MatLab. The various statistical and spectral methods are applied on EEG signals to obtained the distinct features in each sub-band. After compared these classifiers on the performance parameters.we have 8 best classifier trained Models that were utilized in checking effectiveness to clearly distinguish between Epileptic and Normal cases.