Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm

In this study, a new scheme was presented for the optimal classification of epileptic seizures in EEG using wavelet analysis and the genetic algorithm (GA). In the proposed scheme, normal and epileptic EEG epochs (windows) were decomposed into various frequency bands through a fourth-level wavelet packet decomposition. Approximate entropy (ApEn) values of the wavelet coefficients at all nodes of the decomposition tree were used as a feature set to characterize the predictability of the EEG data within the corresponding frequency bands. Then, the GA was used to find the optimal feature subset that maximizes the classification performance of a learning vector quantization (LVQ)-based normal and epileptic EEG classifier. Clinical EEG data recorded from normal subjects and epileptic patients were used to test the performance of the new scheme. It was demonstrated that the new scheme was able to classify the normal and epileptic EEG epochs with 94.3% and 98% accuracy, respectively. It was also shown that, if the GA was not used for the optimal feature selection, the classification accuracies dropped noticeably.