Fast Fourier Transform and Ensemble Model to Classify Epileptic EEG Signals

The analysis of electroencephalogram (EEG) signals can provide valuable insights to the nature of many diseases such as Alzheimer, epilepsy, sleep problems and thus can improve our understating and treatment about them. One of the major EEG signal applications is related to Epilepsy. The main contribution of this work is the proposal of an effective scheme for classifying the EEG signals for the study of epilepsy based on Fast Fourier Transform (FFT) under an ensemble model. The EEG signals are decomposed into frequency bands by using Fast Fourier Transform to extract the key statistical features, which are then fed into an ensemble model to classify the epileptic patients. Three base classifiers – Naïve Bayes, Least Square Support Vector Machine and Neural Networks - are utilized to construct the ensemble framework. The final decision of classification is dependent on the aggregation of the three classifiers decisions. The experimental results demonstrated that the proposed technique is a promising tool in accurately classifying the epileptic EEG signals.