Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder
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...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
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| Online Access: | https://ieeexplore.ieee.org/document/10816093/ |
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| author | Chun-Chuan Chen Meng-Chang Tsai Eric Hsiao-Kuang Wu Shao-Rong Sheng Jia-Jeng Lee Yung-En Lu Shih-Ching Yeh |
| author_facet | Chun-Chuan Chen Meng-Chang Tsai Eric Hsiao-Kuang Wu Shao-Rong Sheng Jia-Jeng Lee Yung-En Lu Shih-Ching Yeh |
| author_sort | Chun-Chuan Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ceff6acf494044b39d25fd265913351a |
| institution | Kabale University |
| issn | 2168-2372 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-ceff6acf494044b39d25fd265913351a2025-01-09T00:00:35ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722025-01-01131810.1109/JTEHM.2024.352235610816093Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use DisorderChun-Chuan Chen0https://orcid.org/0000-0002-3224-200XMeng-Chang Tsai1https://orcid.org/0000-0002-1041-7593Eric Hsiao-Kuang Wu2https://orcid.org/0000-0002-1767-2773Shao-Rong Sheng3Jia-Jeng Lee4Yung-En Lu5Shih-Ching Yeh6https://orcid.org/0000-0001-8856-8685Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, TaiwanDepartment of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, TaiwanComputer Science and Information Engineering Department, National Central University, Taoyuan, TaiwanComputer Science and Information Engineering Department, National Central University, Taoyuan, TaiwanDepartment of Biomedical Sciences and Engineering, National Central University, Taoyuan, TaiwanDepartment of Computer Science and Information Engineering, National Cheng Kung University, Tainan, TaiwanComputer Science and Information Engineering Department, National Central University, Taoyuan, TaiwanMethamphetamine 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.https://ieeexplore.ieee.org/document/10816093/Methamphetamine (MA)bio-signalheart rate variability (HRV)electrocardiography (ECG)electroencephalography (EEG)galvanic skin response (GSR) |
| spellingShingle | Chun-Chuan Chen Meng-Chang Tsai Eric Hsiao-Kuang Wu Shao-Rong Sheng Jia-Jeng Lee Yung-En Lu Shih-Ching Yeh Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder IEEE Journal of Translational Engineering in Health and Medicine Methamphetamine (MA) bio-signal heart rate variability (HRV) electrocardiography (ECG) electroencephalography (EEG) galvanic skin response (GSR) |
| title | Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder |
| title_full | Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder |
| title_fullStr | Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder |
| title_full_unstemmed | Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder |
| title_short | Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder |
| title_sort | fusion model using resting neurophysiological data to help mass screening of methamphetamine use disorder |
| topic | Methamphetamine (MA) bio-signal heart rate variability (HRV) electrocardiography (ECG) electroencephalography (EEG) galvanic skin response (GSR) |
| url | https://ieeexplore.ieee.org/document/10816093/ |
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