Epileptic seizure detection from EEG signals using logistic model trees

Kabir, Enamul and Siuly, and Zhang, Yanchun (2016) Epileptic seizure detection from EEG signals using logistic model trees. Brain Informatics, 3. ISSN 2198-4018

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Reliable analysis of electroencephalogram (EEG) signals is crucial that could lead the way to correct diagnostic and therapeutic methods for the treatment of patients with neurological abnormalities, especially epilepsy. This paper presents a novel analysis system for detecting epileptic seizure from EEG signals, which uses statistical features based on optimum allocation technique (OAT) with logistic model trees (LMT). The analysis involves applying the OAT to select representative EEG signals that reflect the entire database. Then, some statistical features are extracted from these EEG signals and the obtained feature set is fed into the LMT classification model to detect epileptic seizure. To test the consistency of the proposed method, all experiments are carried out on a benchmark EEG dataset and repeated twenty times with the same parameters in the detection process, and the average values of the performance parameters are reported. The results show very high detection performances for each class, and also confirm the consistency of the proposed method in the repeating process. The proposed method outperforms some state-of-the-art methods of epileptic EEG signal detection using the same EEG dataset.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online 21 January 2016. Access to ArticleFirst version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 18 Mar 2016 00:05
Last Modified: 16 Jan 2018 04:34
Uncontrolled Keywords: electroencephalogram (EEG), epileptic seizure, optimum allocation technique (OAT), logistic model trees (LMT), classification, feature extraction
Fields of Research : 09 Engineering > 0903 Biomedical Engineering > 090302 Biomechanical Engineering
Identification Number or DOI: 10.1007/s40708-015-0030-2
URI: http://eprints.usq.edu.au/id/eprint/28796

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