Majority voting-based hybrid feature selection in machine learning paradigm for epilepsy detection using EEG

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.