An EEG-based functional connectivity measure for automatic detection of alcohol use disorder

• Main Authors: Mumtaz, W., Saad, M.N.B.M., Kamel, N., Ali, S.S.A., Malik, A.S.
• Format: Article
• Published: Elsevier B.V. 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034579872&doi=10.1016%2fj.artmed.2017.11.002&partnerID=40&md5=e558ec99a883c9676180209c1b47224e; http://eprints.utp.edu.my/21870/

Background The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics. Method In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used. Results The study resulted into SVM classification accuracy = 98, sensitivity = 99.9, specificity = 95, and f-measure = 0.97; LR classification accuracy = 91.7, sensitivity = 86.66, specificity = 96.6, and f-measure = 0.90; NB classification accuracy = 93.6, sensitivity = 100, specificity = 87.9, and f-measure = 0.95. Conclusion The SL features could be utilized as objective markers to screen the AUD patients and healthy controls. © 2017 Elsevier B.V.