UTHM Institutional Repository

A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure‑free electroencephalography signals

Ong, Pauline and Zainuddin, Zarita and Lai, Kee Huong (2018) A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure‑free electroencephalography signals. Pattern Analysis and Applications, 21 (2). pp. 515-527. ISSN 1433755X

Full text not available from this repository.

Abstract

Properly determining the discriminative features which characterize the inherent behaviors of electroencephalography (EEG) signals remains a great challenge for epileptic seizure detection. In this present study, a novel feature selection scheme based on the discrete wavelet packet decomposition and cuckoo search algorithm (CSA) was proposed. The normal as well as epileptic EEG recordings were first decomposed into various frequency bands by means of wavelet packet decomposition, and subsequently, statistical features at all developed nodes in the wavelet packet decomposition tree were derived. Instead of using the complete set of the extracted features to construct a wavelet neural networks-based classifier, an optimal feature subset that maximizes the predictive competence of the classifier was selected by using the CSA. Experimental results on the publicly available benchmarks demonstrated that the proposed feature subset selection scheme achieved promising recognition accuracies of 98.43–100%, and the results were statistically significant using z-test with ρ value <0.0001.

Item Type: Article
Uncontrolled Keywords: Cuckoo search algorithm; discrete wavelet packet decomposition; EEG signals; epileptic seizure classification; wavelet neural networks
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Engineering Mechanics
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 18 Feb 2019 06:56
Last Modified: 18 Feb 2019 06:56
URI: http://eprints.uthm.edu.my/id/eprint/10673
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item