Automated EEG-based epilepsy detection using BA_SVM classifiers. (15th October 2020) - Naser, Aya; Tantawi, Manal; Shedeed, Howida A.; Tolba, M.F.
Epilepsy is a neurological disorder which affects individuals all around the world. The presence of epilepsy is recognised by seizures attacks. EEG signals can provide useful information about epileptic seizures. Unlike most of the existing studies which consider only two classes, this paper proposes an automatic EEG-based method for epilepsy detection which has the ability to distinguish between the three classes; normal, interictal (out of seizure time) and ictal (during seizure). In the proposed method, Rènyi entropy, line length and energy are computed from each of the five sub-bands extracted from an EEG segment using digital wavelet transform (DWT). Thereafter, the extracted features are fed into BA-SVM classifiers trained using divide and conquer strategy for classification. The BA-SVM classifier is a support vector machine (SVM) classifier whose parameters are optimised using BAT algorithm. The popular Andrzejak database was utilised for training and testing purposes. The average accuracies for all considered cases are more than or equal 95%. Thus, the various experiments and comparisons accomplished in this study reveal the efficacy of the proposed method.