Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment.

ABSTRACT

BACKGROUND: The aim of this study was to evaluate whether machine learning (ML) can be used to distinguish patients with methamphetamine dependence from healthy controls by using their surface electroencephalography (EEG) and galvanic skin response (GSR) in a drug-simulated virtual reality (VR) environment. METHODS: A total of 333 participants with methamphetamine (METH) dependence and 332 healthy control subjects were recruited between January 2018 and January 2019. EEG (five electrodes) and GSR signals were collected under four VR environments one neutral scenario and three METH-simulated scenarios. Three ML classification techniques were evaluated random forest (RF), support vector machine (SVM), and logistic regression (LR). RESULTS: The MANOVA showed no interaction effects among the two subject groups and the 4 VR scenarios. Taking patient groups as the main effect, the METH user group had significantly lower GSR, lower EEG power in delta (p < .001), and alpha bands (p < .001) than healthy subjects. The EEG power of beta band (p < .001) and gamma band (p < .001) was significantly higher in METH group than the control group. Taking the VR scenarios (Neutral versus METH-VR) as the main effects, the GSR, EEG power in delta, theta, and alpha bands in neutral scenario were significantly higher than in the METH-VR scenario (p < .001). The LR algorithm showed the highest specificity and sensitivity in distinguishing methamphetamine-dependent patients from healthy controls. CONCLUSION: The study shows the potential of using machine learning to distinguish methamphetamine-dependent patients from healthy subjects by using EEG and GSR data. The LR algorithm shows the best performance comparing with SVM and RF algorithm.