Non linear analysis of epileptic EEG

Abstract

EEG contains immense information about the brain activity which cannot be understood completely by visual inspection. Powerful signal processing algorithms in EEG analysis can greatly assist the physicians and neurologists to extract such hidden information. It has been found that EEG being a time-varying and non-stationary signal, can be analyzed by non-linear methods. In this paper we tried to evaluate the non linear features, Correlation dimension, Approximate Entropy, Sample Entropy, and Hurst Exponent in epileptic and normal EEG and has obtained clear discrimination between them.