Chaos Based Nonlinear Analysis of Epileptic Seizure

Feature extraction and classification of electro-physiological signals is an important issue in development of disease diagnostic expert system (DDES). In this paper we propose a method based on chaos methodology for EEG signal classification. The nonlinear dynamics of original EEGs are quantified in the form of entropy, largest Lyapunov exponent (LLE), correlation dimension (CD), capacity dimension (CAD) and were considered for discrimination of various categories of EEG signals. After calculating the above mentioned parameters for signals, we found that without going for rigorous time-frequency domain analysis, only chaos based parameters is also suitable to classify various EEG signals.