Performance Analysis of Supervised Machine Learning Algorithms for Epileptic Seizure Detection with high variability EEG datasets: A Comparative Study

A wide variety of machine learning techniques have been developed which could aid in the analysis of continuous EEG time-series data and derive useful insights from it. The techniques which have been most popularly used for such analysis include Neural Networks, Support Vector Machines, and Linear Discriminant Analysis. Extreme Learning Machines are one of the newest additions to these classifiers. The present work compares the performance of these classifiers on both single channel and multi-channel EEG recordings. Two different datasets used for the experiments are (a) EEG database by University of Bonn, Germany for single channel recordings, (b) CHB-MIT dataset for multi-channel recordings. In all these experiments, we assume that the recordings, after preprocessing, are all free from such defects which could affect the performance of different classifiers differently. For preprocessing, filtered recordings are segmented and DWT is applied on them to use the transform coefficients for feature extraction. These extracted features were then fed into various classifiers. It was observed that ELM classifiers could perform at par or better than the conventional classification methods.