EEG signals classification using a hybrid method based on negative selection and particle swarm optimization

Abstract

The diagnosis of epilepsy from EEG signals by a human scorer is a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the large amount of data needs to be scored. In this paper, a hybrid method called adaptive particle swarm negative selection (APSNS) was introduced to automate the process of epileptic seizures detection in EEG signals. In the proposed method, an adaptive negative selection creates a set of artificial lymphocytes (ALCs) that are tolerant to normal patterns. However, the particle swarm optimization (PSO) algorithm forces these ALCs to explore the space of epileptic signals and maintain diversity and generality among them. The EEG signals were analyzed using discrete wavelet transform (DWT) to extract the most important information needed for decision making. The features extracted have been used to investigate the performance of the proposed APSNS algorithm in classifying the EEG signals. The Experimental results confirm effectiveness and stability of the proposed method. Its classification accuracy outperforms many of the methods in the literature.