A combined linear & nonlinear approach for classification of epileptic EEG signals

The use of both linear autoregressive model coefficients and nonlinear measures for classification of EEG signals recorded from healthy subjects and epilepsy patients is investigated. A total of seven nonlinear measures namely the approximate entropy, largest lyapunov exponent, correlation dimension, nonlinear prediction error, hurst exponent, third order autocovariance, asymmetry due to time reversal, are used in this study. The class separability of individual and combined feature sets is measured using Linear Discriminant Analysis (LDA) algorithm where the multiple features are selected by sequential floating forward search (SFFS) algorithm. The results have shown that the use of combined feature sets provide a better characterization of EEG signals compared to individual features