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)

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 galv...