Fusion model using resting neurophysiological data to help mass screening of methamphetamine use disorder

• Published in: IEEE journal of translational engineering in health and medicine Vol. 13; p. 1
• Main Authors: Chen, Chun-Chuan, Tsai, Meng-Chang, Wu, Eric Hsiao-Kuang, Sheng, Shao-Rong, Lee, Jia-Jeng, Lu, Yung-En, Yeh, Shih-Ching
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Accuracy; Adult; Amphetamine-Related Disorders - diagnosis; Amphetamine-Related Disorders - physiopathology; Bio-signal; Brain modeling; Data Fusion; Diseases; Drugs; Electrocardiography; Electrocardiography (ECG); Electroencephalography; Electroencephalography (EEG); Electroencephalography - methods; Feature extraction; Female; Galvanic skin response; Galvanic Skin Response (GSR); Galvanic Skin Response - physiology; Heart rate; Heart Rate - physiology; Heart rate variability; Heart Rate Variability (HRV); Hospitals; Humans; Machine Learning; Male; Mass Screening - methods; Methamphetamine; Methamphetamine (MA); Methamphetamine - adverse effects; Middle Aged; Mud; Multimodal Data; Multiuser detection; Separation; Signal Processing, Computer-Assisted; Virtual reality; Virtual Reality (VR); Young Adult; data fusion; electroencephalography (EEG); bio-signal; Methamphetamine (MA); galvanic skin response (GSR); heart rate variability (HRV); multimodal data; virtual reality (VR); machine learning; electrocardiography (ECG)
• ISSN: 2168-2372; 2168-2372
• DOI: 10.1109/JTEHM.2024.3522356

Methamphetamine use disorder (MUD) is a substance use disorder. Because MUD has become more prevalent due to the COVID-19 pandemic, alternative ways to help the efficiency of mass screening of MUD are important. Previous studies used electroencephalogram (EEG), heart rate variability (HRV), and galvanic skin response (GSR) aberrations during the virtual reality (VR) induction of drug craving to accurately separate patients with MUD from the healthy controls. However, whether these abnormalities present without induction of drug-cue reactivity to enable separation between patients and healthy subjects remains unclear. Here, we propose a clinically comparable intelligent system using the fusion of 5-channel EEG, HRV, and GSR data during resting state to aid in detecting MUD. Forty-six patients with MUD and 26 healthy controls were recruited and machine learning methods were employed to systematically compare the classification results of different fusion models. The analytic results revealed that the fusion of HRV and GSR features leads to the most accurate separation rate of 79%. The use of EEG, HRV, and GSR features provides more robust information, leading to relatively similar and enhanced accuracy across different classifiers. In conclusion, we demonstrated that a clinically applicable intelligent system using resting-state EEG, ECG, and GSR features without the induction of drug cue reactivity enhances the detection of MUD. This system is easy to implement in the clinical setting and can save a lot of time on setting up and experimenting while maintaining excellent accuracy to assist in mass screening of MUD.