Journal of Bioengineering & Biomedical Science

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

Abstract

Mechmeche S, Salah RB and Ellouze N

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.