title: An EEG-based machine learning method to screen alcohol use disorder.
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.
authors.affiliation: Center for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Malaysia.
authors.forename: Wajid
authors.initials: W
authors.lastname: Mumtaz
authors.affiliation: Center for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Malaysia.
authors.forename: Pham Lam
authors.initials: PL
authors.lastname: Vuong
authors.affiliation: Beijing Institute of Technology, Beijing, 100081 China.
authors.forename: Likun
authors.initials: L
authors.lastname: Xia
authors.affiliation: Center for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Malaysia.
authors.forename: Aamir Saeed
authors.initials: AS
authors.lastname: Malik
authors.affiliation: University Malaya Centre of Addiction Sciences (UMCAS), Faculty of Medicine, Department of Psychological Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
authors.forename: Rusdi Bin Abd
authors.initials: RBA
authors.lastname: Rashid
keywords: Alcohol abuse (AA); Alcohol dependence (AD); Alcohol use disorder (AUD); Electroencephalography (EEG); Inter-hemispheric coherence; Resting-state EEG (REEG); Spectral powers of EEG data