Mumtaz, W. and Vuong, P.L. and Xia, L. and Malik, A.S. and Rashid, R.B.A. (2017) An EEG-based machine learning method to screen alcohol use disorder. Cognitive Neurodynamics, 11 (2). pp. 161-171. ISSN 18714080
Full text not available from this repository.Abstract
Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity involved in the process. Hence, robust and objective methods are needed to automate the screening of AUD patients. In this paper, a machine learning method is proposed that utilized resting-state electroencephalography (EEG)-derived features as input data to classify the AUD patients and healthy controls and to perform automatic screening of AUD patients. In this context, the EEG data were recorded during 5 min of eyes closed and 5 min of eyes open conditions. For this purpose, 30 AUD patients and 15 aged-matched healthy controls were recruited. After preprocessing the EEG data, EEG features such as inter-hemispheric coherences and spectral power for EEG delta, theta, alpha, beta and gamma bands were computed involving 19 scalp locations. The selection of most discriminant features was performed with a rank-based feature selection method assigning a weight value to each feature according to a criterion, i.e., receiver operating characteristics curve. For example, a feature with large weight was considered more relevant to the target labels than a feature with less weight. Therefore, a reduced set of most discriminant features was identified and further be utilized during classification of AUD patients and healthy controls. As results, the inter-hemispheric coherences between the brain regions were found significantly different between the study groups and provided high classification efficiency (Accuracy = 80.8, sensitivity = 82.5, and specificity = 80, F-Measure = 0.78). In addition, the power computed in different EEG bands were found significant and provided an overall classification efficiency as (Accuracy = 86.6, sensitivity = 95, specificity = 82.5, and F-Measure = 0.88). Further, the integration of these EEG feature resulted into even higher results (Accuracy = 89.3 , sensitivity = 88.5 , specificity = 91 , and F-Measure = 0.90). Based on the results, it is concluded that the EEG data (integration of the theta, beta, and gamma power and inter-hemispheric coherence) could be utilized as objective markers to screen the AUD patients and healthy controls. © 2016, Springer Science+Business Media Dordrecht.
Item Type: | Article |
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Additional Information: | cited By 52 |
Uncontrolled Keywords: | adult; alcoholism; alpha rhythm; Article; beta rhythm; brain region; clinical article; controlled study; delta rhythm; diagnostic accuracy; diagnostic test accuracy study; disease classification; electroencephalography; female; gamma rhythm; hemisphere; human; machine learning; male; receiver operating characteristic; resting state network; screening; sensitivity and specificity; theta rhythm |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 09 Nov 2023 16:20 |
Last Modified: | 09 Nov 2023 16:20 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/8763 |