Epileptic EEG signal classification using optimum allocation based power spectral density estimation
Article
Article Title | Epileptic EEG signal classification using optimum allocation based power spectral density estimation |
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ERA Journal ID | 41674 |
Article Category | Article |
Authors | Al Ghayab, Hadi Ratham (Author), Li, Yan (Author), Siuly, Siuly (Author) and Abdulla, Shahab (Author) |
Journal Title | IET Signal Processing |
Journal Citation | 12 (6), pp. 738-747 |
Number of Pages | 13 |
Year | 2018 |
Place of Publication | United Kingdom |
ISSN | 1751-9675 |
Digital Object Identifier (DOI) | https://doi.org/10.1049/iet-spr.2017.0140 |
Web Address (URL) | http://digital-library.theiet.org/content/journals/10.1049/iet-spr.2017.0140 |
Abstract | 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. |
Keywords | Electroencephalogram (EEG); optimum allocation technique; power spectral density estimation method; support vector machine; quadratic discriminant analysis; k-nearest neighbor |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Faculty of Health, Engineering and Sciences |
Victoria University | |
Open Access College | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q49v0/epileptic-eeg-signal-classification-using-optimum-allocation-based-power-spectral-density-estimation
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