Epileptic EEG signal classification using optimum allocation based power spectral density estimation

Al Ghayab, Hadi Ratham and Li, Yan and Siuly, Siuly and Abdulla, Shahab (2018) Epileptic EEG signal classification using optimum allocation based power spectral density estimation. IET Signal Processing. ISSN 1751-9683


This paper proposes a novel approach blending optimum allocation (OA) technique and spectral density estimation to analyse and classify epileptic EEG signals. This study employs the OA to determine representative sample points from the original EEG data and then applies Periodogram (PD), Autoregressive (AR), and the mixture of PD and AR to extract the discriminative features from each OA sample group. The obtained feature sets are evaluated by three popular machine learning methods: support vector machine (SVM), quadratic discriminant analysis (QDA), and k-nearest neighbor (k-NN). Several output coding approaches of the SVM classifier are tested for selecting the best feature sets. This scheme was implemented on a benchmark epileptic EEG database for evaluation and also compared with existing methods. The experimental results show that the OA_AR feature set yields better performances by the SVM with an overall accuracy of 100%, and outperforms the state-of-the-art works with a 14.1% improvement. Thus the findings of this study prove that the proposed OA based AR scheme has significant potential to extract features from EEG signals. The proposed method will assist experts to automatically analyse a large volume of EEG data and benefit epilepsy research.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online 26 Feb 2018. Permanent restricted access to ArticleFirst version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 12 Jun 2018 00:30
Last Modified: 11 Feb 2019 05:55
Uncontrolled Keywords: Electroencephalogram (EEG); optimum allocation technique; power spectral density estimation method; support vector machine; quadratic discriminant analysis; k-nearest neighbor
Fields of Research : 08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
Socio-Economic Objective: C Society > 92 Health > 9202 Health and Support Services > 920299 Health and Support Services not elsewhere classified
Identification Number or DOI: 10.1049/iet-spr.2017.0140
URI: http://eprints.usq.edu.au/id/eprint/33834

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