Abstract
BackgroundThe 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.MethodsA 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).ResultsThe 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.ConclusionThe 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.
Highlights
Substance dependence brings serious problems to the society, including disease, crime, accidents, domestic violence, homelessness, etc
We evaluated and compared the accuracy of distinguishing patients with methamphetamine dependence and healthy control subjects of three popular supervised machine learning (ML) algorithms based on their EEG and galvanic skin response (GSR) data
The logistic regression (LR) algorithm showed highest accuracy (90.68%) and F1 Score (90.80%)
Summary
Substance dependence brings serious problems to the society, including disease, crime, accidents, domestic violence, homelessness, etc. We evaluated and compared the accuracy of distinguishing patients with methamphetamine dependence and healthy control subjects of three popular supervised ML algorithms based on their EEG and galvanic skin response (GSR) data. 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. 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. 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
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have