博碩士論文 108522004 詳細資訊




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姓名 葉軍(Chun Yeh)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 基於數據融合模型的機器學習 對甲基苯丙胺使用障礙的多生理訊號號分析
(Multi-bio-signal analysis of Methamphetamine Use Disorder by Machine Learning on data fusion model)
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摘要(中) 根據我們之前在 2020 年發表的研究,我們通過皮膚電反應 (GSR)、心率變異性 (HRV) 和腦電圖 (EEG) 等生物信號,成功建立了甲基苯丙胺使用障礙 (MUD) 患者的分類模型並成功分類他們。
在以MUD患者為實驗組、健康人為對照組的參與者收集HRV、GSR、EEG等生物傳感器信號數據後,我們使用每個生物傳感器信號進行分類。 參與過我們的VR系統後,在分類MUD與正常人的模型中,於 HRV 中,我們獲得了 80.01% 的準確率;於 GSR 中,我們獲得了 78.12% 的準確率;於 EEG 中,我們獲得了 92.30% 的準確率。 在這項研究中,我們通過結合這三種類型的數據來提高準確性。 結果,我們的多模態生物傳感器模型獲得了 99.01% 的準確率。 有了這個虛擬現實系統和預測模型,我們能夠提供一個更有效的甲基苯丙胺治療系統。
摘要(英) According to our previous study published in 2020, We successfully established a classification model for patients with Methamphetamine Use Disorder (MUD) through biological signals such as Galvanic Skin Response (GSR), Heart Rate Variability (HRV) and Electroencephalogram (EEG) and successfully classified them in our VR system. After collecting bio-sensor signal data such as HRV, GSR, and EEG from participants with MUD patients as experiment group and healthy people as control group, we used each bio-sensor signal to classify. In the classification between MUD and healthy subjects after participating our VR system; in HRV, we got 80.01% accuracy. In GSR, we got 78.12% accuracy. And in EEG we got 92.30% accuracy. In this study, we recruited more participants and tried to improve the accuracy by combining these three types of data. As a result, we got 99.01% accuracy by our multimodal bio-sensor model. With this VR system and forecast model, we are able to provide a more effective system in Methamphetamine treatment.
關鍵字(中) ★ 甲基苯丙胺
★ 生物信號
★ 心率變異性
★ 心電圖
★ 腦電圖
★ 皮膚電反應
★ 數據融合
★ 多模態數據
★ 虛擬現實
★ 機器學習
關鍵字(英) ★ Methamphetamine
★ Bio-signal
★ Heart rate variability
★ Electrocardiography
★ Electroencephalography
★ Galvanic Skin Response
★ Data Fusion
★ Multimodal Data
★ Virtual reality
★ Machine Learning
論文目次 摘要 I
Table of Contents IV
List of Figures V
List of Tables VI
1. Introduction 1
2. Related Works 5
3. Methodologies 7
4. Results 24
5. Conclusion and Discussions 28
Reference 30
參考文獻 [1] World Health Organization. “World Drug Report 2020,
https://www.unodc.org/wdr2020/en/exsum.html.
[2] World Health Organization. “World Drug Report 2018,
https://www.unodc.org/wdr2018/en/exsum.html.
[3] Tsai MC, Chung CR, Chen CC, Chen JY, Yeh SC, Lin CH, Chen YJ, Tsai MC, Wang YL, Lin CJ, Wu EH. An Intelligent Virtual-Reality System With Multi-Model Sensing for Cue-Elicited Craving in Patients With Methamphetamine Use Disorder. IEEE Trans Biomed Eng. 2021 Jul;68(7):2270-2280. doi: 10.1109/TBME.2021.3058805. Epub 2021 Jun 17. PMID: 33571085.
[4] Lan-lan Chen , Yu Zhao , Peng-fei Ye , Jian Zhang , Jun-zhong Zou ,Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classiÞers, Expert Systems With Applications (2017), doi: 10.1016/j.eswa.2017.01.040
[5] Değer Ayata · Yusuf Yaslan · Mustafa E. Kamasak , Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems, Journal of Medical and Biological Engineering (2020) 40:149–157
[6] Ding X, Li Y, Li D, Ling Li C, Liu X. Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment. Brain Behav. 2020;10:e01814.https://doi.org/10.1002/brb3.1814
[7] Kim H, Kim L, Im CH. Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use. Sensors (Basel). 2019 Aug 9;19(16):3475. doi: 10.3390/s19163475. PMID: 31395802; PMCID: PMC6719101.
[8] Jonghwa Kim (June 1st 2007). Bimodal Emotion Recognition using Speech and Physiological Changes, Robust Speech Recognition and Understanding, Michael Grimm and Kristian Kroschel, IntechOpen, DOI: 10.5772/4754. Available from: https://www.intechopen.com/books/robust_speech_recognition_and_understanding/bimodal_emotion_recognition_using_speech_and_physiological_changes
[9] Huang, SC., Pareek, A., Zamanian, R. et al. Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection. Sci Rep 10, 22147 (2020). https://doi.org/10.1038/s41598-020-78888-w
[10] W. -L. Zheng, W. Liu, Y. Lu, B. -L. Lu and A. Cichocki, "EmotionMeter: A Multimodal Framework for Recognizing Human Emotions," in IEEE Transactions on Cybernetics, vol. 49, no. 3, pp. 1110-1122, March 2019, doi: 10.1109/TCYB.2018.2797176.
[11] X. Zhang et al., "Emotion Recognition From Multimodal Physiological Signals Using a Regularized Deep Fusion of Kernel Machine," in IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2020.2987575.
[12] Igor Kononenko, Machine learning for medical diagnosis: history, state of the art and perspective, Artificial Intelligence in Medicine, Volume 23, Issue 1, 2001, Pages 89-109, ISSN 0933-3657, https://doi.org/10.1016/S0933-3657(01)00077-X.
[13] Federico Castanedo, "A Review of Data Fusion Techniques", The Scientific World Journal, vol. 2013, Article ID 704504, 19 pages, 2013. https://doi.org/10.1155/2013/704504
[14] pyHRV – OpenSource Python Toolbox for Heart Rate Variability, https://pyhrv.readthedocs.io/en/latest/#
[15] Biosppy – toolbox for biosignal processing written in Python, https://biosppy.readthedocs.io/en/stable/
[16] EEGLAB - an interactive Matlab toolbox for processing continuous and event-related EEG, https://sccn.ucsd.edu/eeglab/index.php
[17] Newton TF, Cook IA, Kalechstein AD, Duran S, Monroy F, Ling W, Leuchter AF. Quantitative EEG abnormalities in recently abstinent methamphetamine dependent individuals. Clin Neurophysiol. 2003 Mar;114(3):410-5. doi: 10.1016/s1388-2457(02)00409-1. PMID: 12705421.
[18] Henry BL, Minassian A, Perry W. Effect of methamphetamine dependence on heart rate variability. Addict Biol. 2012;17(3):648-658. doi:10.1111/j.1369-1600.2010.00270.x
[19] Rawson RA, Gonzales R, Brethen P. Treatment of methamphetamine use disorders: an update. J Subst Abuse Treat. 2002 Sep;23(2):145-50. doi: 10.1016/s0740-5472(02)00256-8. PMID: 12220612.
[20] A. Lautieri, “Drug and Alcohol Withdrawal Symptoms, Timelines, and Treatment,”
[21] J. Du, C. Fan, H. Jiang, H. Sun, X. Li, and M. Zhao, “Biofeedback combined with cue-exposure as a treatment for heroin addicts,” Physiology & Behavior, vol. 130, pp. 34-39, 2014.
[22] P. L. A. Schoenberg and A. S. David, “Biofeedback for Psychiatric Disorders: A Systematic Review,” Applied Psychophysiology and Biofeedback, vol. 39, no. 2, pp. 109-135, 2014.
[23] M. Krijn et al., “Virtual reality exposure therapy of anxiety disorders: A review,” Clinical Psychology Review, vol. 24, no. 3, pp. 259-281, 2004/07/01/, 2004.
[24] J. Wald, and S. Taylor, “Preliminary Research on the Efficacy of Virtual Reality Exposure Therapy to Treat Driving Phobia,” CyberPsychology & Behavior, vol. 6, no. 5, pp. 459-465, 2003/10/01, 2003.
[25] P. H. Oskam, “Virtual Reality Exposure Therapy (VRET) effectiveness and improvement.”
[26] Chun-Chuan Chen, “Drug Cue Induced Neuraonal Abnormalities In Patients With Methamphetamine Use Disorder: A VR-EEG study”
指導教授 葉士青(Shih-Ching Yeh) 審核日期 2021-9-7
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