Title: Real-time epileptic detection from EEG signals using statistical features optimisation and neural networks classification

Authors: Badreddine Mandhouj; Sami Bouzaiane; Mohamed Ali Cherni; Ines Ben Abdelaziz; Slim Yacoub; Mounir Sayadi

Addresses: SIME Laboratory, Tunis University, ENSIT, 1008, Tunis, Tunisia ' Naval Academy, Menzel Bourguiba, 7050, Bizerte, Tunisia ' SIME Laboratory, Tunis University, ENSIT, 1008, Tunis, Tunisia ' National Institute of Neurology Mongi Ben Hmida, La Rabta, 1007, Tunis, Tunisia ' SITI Laboratory, Tunis El-Manar University, ENIT, 1032, Tunis, Tunisia ' SIME Laboratory, Tunis University, ENSIT, 1008, Tunis, Tunisia

Abstract: This paper describes a completely automated approach in order to enhance the diagnosis of epilepsy disease which is one of the most prevalent neurological disorders. The major aim of this work is to be a potential contribution to the domain. The present paper is divided into three main parts. In the first part, we optimise the statistical features extracted from the EEG signals by a characterisation degree. Then, these features are applied to a multilayer neural network (MNN) classifier. In the third part, we use a digital signal peripheral interface controller (dsPIC) for the implementation of the real-time EEG classification process. The used EEG data are taken from the publicly available database of the University of Bonn and are classified into healthy and epileptic subjects. To assess the performance of this classification method, several performance measures (sensitivity, specificity and accuracy) have been evaluated and have provided interesting results.

Keywords: electroencephalogram; EEG; statistical features; classification; epilepsy; characterisation degree; optimisation; multilayer neural network; MNN; dsPIC; real-time.

DOI: 10.1504/IJBET.2021.120190

International Journal of Biomedical Engineering and Technology, 2021 Vol.37 No.4, pp.348 - 367

Received: 05 Oct 2018
Accepted: 18 Dec 2018
Published online: 11 Jan 2022 *

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