Two-Stage Feature Selection Algorithm Based on Supervised Classification Approach for Automated Epilepsy Diagnosis

Abstract

Epileptic diagnosis is generally achieved by visual scanning of Interictal Epileptiform Discharges (IEDs) using EEG recordings. The main objective of this research is to select a smallest relevant feature subset from the original dataset in order to reduce the diagnosis time and increase classification accuracy by removing irrelevant and redundant features. For this purpose we suggest a two-stage feature selection algorithm based on supervised classification approach adopting successively a wrapper feature selection and a wrapper feature subset selection method. Matlab simulation results illustrate that through comparing the two classifiers, the high-dimensionality is reduced at only one relevant feature that showed classification metrics of 100%. The epilepsy diagnosis is successfully tested in the discriminant Fisher-space with the single-best relevant feature.