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Early Epileptic Seizure Detection

In this paper, we have implemented a special case of Blind Source Separation (BSS) algorithm i.e. AMUSE algorithm using Independent Component Analysis (ICA) methodology in addition with Empirical Wavelet Transform (EWT) to detect epileptic seizures in advance. The proposed method has been studied on EEG Signals obtained from CHB-MIT Scalp EEG Database. Here, we have extracted independent components and chose five components having the least standard deviation after applying BSS Algorithm. Decomposition of signals into epochs, each of two seconds, is performed and three features are extracted from each one second of the two second long joint instantaneous amplitudes and frequencies, which has been obtained after employing Hilbert-Huang Transform. The extracted features of different oscillatory levels have been processed and joint feature vectors have been computed using Hadamard product for better distinction between seizure and nonseizure instances. Two well-known classifiers, Random Forest and KNN has been applied with the help of WEKA software upon 4 patients which resulted in average accuracy of 99.46% with RF classifier and 99.32% using KNN classifier.