A novel automatic classification detection for epileptic seizure based on dictionary learning and sparse representation

Abstract

Electroencephalogram (EEG) signals play an important role in the epilepsy detection. In the past decades, the automatic detection system of epilepsy has emerged and performed well. In this paper, a novel sparse representation-based epileptic seizure classification based on the dictionary learning with homotopy (DLWH) algorithm is proposed. The performance of the proposed method evaluates on two public EEG databases provided by the Bonn University and Childrens Hospital Boston-Massachusetts Institute of Technology (CHB-MIT), various classification cases that include health and seizure; non-seizure and seizure; inter ictal (seizure-free interval) and ictal (seizure). The results show that the DLWH only completes the test with 19.671 s compared with the traditional sparse representation methods with high degree of automation, which are better than those obtained using the well-known dictionary learning method. Besides, two publicly available benchmark databases recognition rates are as high as 100%, 99.5%, with average of 99.5% and 95.06%,% respectively, and the results show that the epileptic detection system based on the dictionary learning has a high application value.