Comparative Analysis of Non-linear Behaviour with Power Spectral Intensity Response Between Normal and Epileptic EEG Signals

Abstract

Epilepsy is a neurological condition which affects the nervous system. It is a general term used for a group of disorders in which nerve cells of the brain discharge anomalous electrical impulses from time to time, causing a temporary malfunction of the other nerve cells of the brain. EEG signal provides an important cue for diagnosis and interpretation related to prognosis of epilepsy. In this work we envisage to provide novel tool which can be used to detect the prognosis of epileptic disorder by comparing linear and nonlinear modalities of EEG analysis conventionally used Power spectral analysis and a robust non linear method, Detrended Fluctuation Analysis (DFA). Publicly available dataset is used for this work consisting of 100 normal patients EEG data as control group and 100 epileptic patients EEG data for comparison. Response for different frequency bands (alpha, theta, beta) of the EEG spectrum have been analyzed using Detrended Fluctuation Analysis (DFA) and Power Spectral Intensity (PSI). The comparison of the DFA scaling exponent with the spectral power data is calculated for all the 3 different frequency bands of EEG signal provide new and interesting results which have been discussed in detail.