Title: Majority voting-based hybrid feature selection in machine learning paradigm for epilepsy detection using EEG
Authors: Sunandan Mandal; Bikesh Kumar Singh; Kavita Thakur
Addresses: School of Studies in Electronics and Photonics, Pt. Ravishankar Shukla University, Raipur, 492010, (C.G), India ' Department of Biomedical Engineering, National Institute of Technology Raipur, 492010, (C.G), India ' School of Studies in Electronics and Photonics, Pt. Ravishankar Shukla University, Raipur, 492010, (C.G), India
Abstract: This article presents a combination of statistical and discrete wavelet transform (DWT)-based features for the identification of epileptic seizures in electroencephalogram (EEG) signals. A total of 150 quantitative features are extracted from EEG signals. A multi-criteria hybrid feature selection is proposed by combining six feature ranking methods using the majority voting technique to identify the most relevant EEG markers. Kernel-based support vector machine is used to evaluate the proposed approach along with a hybrid classifier namely support vector neural network (SVNN) which is a combination of support vector machine (SVM) and artificial neural network (ANN). For performance evaluation of the proposed method, a benchmarked database is used. A comparative study of various types of SVM and SVNN with ten-fold and hold-out cross-validation techniques is conducted. The highest classification accuracy (CA) of 98.18% and 100% sensitivity is achieved with a fine Gaussian SVM classifier with hold-out data division protocol.
Keywords: EEG quantitative features; epilepsy; wavelet transform; multi-criteria feature selection; classification.
International Journal of Computational Vision and Robotics, 2021 Vol.11 No.4, pp.385 - 400
Received: 01 Dec 2019
Accepted: 18 Mar 2020
Published online: 29 Apr 2021 *