Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications

Al-Hadeethi, Hanan and Abdulla, Shahab ORCID: https://orcid.org/0000-0002-1193-6969 and Diykh, Mohammed and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Green, Jonathan H. ORCID: https://orcid.org/0000-0003-1468-1970 (2020) Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications. Expert Systems with Applications, 161:113676. ISSN 0957-4174


Abstract

Epileptic seizures are characterised by abnormal neuronal discharge, causing notable disturbances in electrical activities of the human brain. Traditional methods based on manual approaches applied in seizure detection in electroencephalograms (EEG) have drawbacks (e.g., time constraint, lack of effective feature identification relative to disease symptoms and susceptibility to human errors) that can lead to inadequate treatment options. Designing an automated expert system to detect epileptic seizures can proactively support a neurologist’s effort to improve authenticity, speed and accuracy of detecting signs of a seizure. We propose a novel two-phase EEG classification technique to detect seizures from EEG by employing covariance matrix coupled with Adaptive Boosting Least Square-Support Vector Machine (i.e., AdaBoost LS-SVM) framework. In first phase, the covariance matrix is employed as a dimensionality reduction tool with feature extraction applied to analyse epileptic patients’ EEG records. Initially, each single EEG channel is partitioned into respective k segment with m clusters. Subsequently, covariance matrix is adopted with eigenvalues of each cluster extracted and tested through statistical metrics to identify the most representative, optimally classified features. In the second phase, a robust classifier (i.e., AB-LS-SVM) is proposed to resolve issues of unbalanced data, to detect epileptic events, yielding a high classification accuracy compared to its competing counterparts. The results demonstrates that AB-LS-SVM (optimised by a covariance matrix) is able to achieve satisfactory results (>99% accuracy) for eleven prominent features in EEG signals. The results are compared with state-of-art algorithms (i.e., k-means, SVM, k-nearest neighbour, Random Forest) on identical databases, demonstrating the capability of AB-LS-SVM method as a promising diagnostic tool and its practicality for implementation in seizure detection. The study avers that the proposed approach can aid clinicians in diagnosis or interventions to treat epileptic disease, including a potential use in expert systems where EEG needs to be classified through pattern recognition.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - USQ College (8 Jun 2020 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Date Deposited: 16 Jul 2020 03:29
Last Modified: 17 Jul 2020 05:02
Uncontrolled Keywords: epileptic seizure; health informatics; electroencephalogram; covariance; eigenvalues; Adaptive Boosting Least Square-Support Vector Machine; AB-LS-SVM
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
11 Medical and Health Sciences > 1102 Cardiovascular Medicine and Haematology > 110299 Cardiovascular Medicine and Haematology not elsewhere classified
Socio-Economic Objectives (2008): C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: 10.1016/j.eswa.2020.113676
URI: http://eprints.usq.edu.au/id/eprint/39033

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