臺灣博碩士論文加值系統

海洛因是一種容易高度成癮的毒品，並且會對社會產生相當大的負擔，也因此近年它得到了更多的重視。如何去分辨海洛因使用疾患(HUD)的嚴重程度以及給予他們適當的治療變成一個非常重要的議題。虛擬實境心率生理回饋(VR HRV biofeedback)訓練過去已被證明能夠有效改善毒癮患者的病情，這篇研究中，我們便採用此系統去對HUD患者進行治療，並同時收集他們的生理資訊，包括心電、皮膚電及呼吸訊號。
在這次研究中，我們收集了11位毒癮患者的六次訓練資料當作實驗組，以及10位正常人的一次訓練資料當作對照組，並利用統計以及機器學習的方式進行不同程度的患者生理訊號之分析。統計結果中，可以看到訓練前後比較起來，患者比起正常人有更多的顯著差異指標。而在機器學習結果可以發現，病人第一次訓練與正常人的資料進行分類可以達到86%的準確率，而病人第六次與正常人的分類結果變得較差，只有0.81的準確率。
此外，我們也採用了殘差網路(ResNet)對前述的分組進行分類，為了確認其直接用機器學習找出的時域資訊，是否能達到如同前面機器學習的結果，而結果中，顯示ResNet在分類患者第一次訓練以及正常人時，有達到接近的準確率。

Heroin is the highly addictive drug which produce numerous burdens to the society, so it became more and more important to distinct the different level of Heroin use disorder patients and give them proper treatment.
The VR HRV biofeedback training, which was proved that it is enable to improve the HUD patients, was adopted as a treatment in this study, and the physiological signals, including electrocardiogram, galvanic skin response, and respiration, would be recorded during it. In this study, 11 HUD patients and 10 normal controls were invited to receive the biofeedback training, and the HUD patients would have 6 times of training. Through statistics and machine learning analysis of the signals, the differences of the signals of patient with different severity could be found. In the statistics analysis results, the HUD patient data had much more significant differences between pre- and post-test than the one of normal controls, which means the training affected the patients more than the normal controls. The machine learning results showed us the performance of classified pre-test of different group were better than the post-test, which means the patients became similar with normal controls after the training. Moreover, the results also presented the accuracies of classified HUD’s first training with the normal controls’ are higher than the one of HUD’s 6th training with normal controls. The ResNet methods were also employed in this study to find if it can have the better performance when only the time domain data be used as the input of model. It could reach the accuracies for 0.86 to classified the HUD patients and normal controls.

摘要 I
List of Figures VI
List of Tables VII
1. Introduction 1
2. Related Works 6
3. Methodologies 10
4. Results 20
5. Discussions 27
6. Conclusion & Future Work 31
Reference 33

[1] World Health Organization, “World drug report 2020,” [Online]. Available: https://www.unodc.org/wdr2020/en/exsum.html
[2] Lin, I-Mei, et al. "Heart rate variability and the efficacy of biofeedback in heroin users with depressive symptoms." Clinical Psychopharmacology and Neuroscience 14.2 (2016): 168.
[3] Arani, Fateme Dehghani, Reza Rostami, and Masoud Nostratabadi. "Effectiveness of neurofeedback training as a treatment for opioid-dependent patients." Clinical EEG and neuroscience 41.3 (2010): 170-177.
[4] Eddie, David, et al. "A pilot study of brief heart rate variability biofeedback to reduce craving in young adult men receiving inpatient treatment for substance use disorders." Applied psychophysiology and biofeedback 39.3 (2014): 181-192.
[5] Du, Jiang, et al. "Biofeedback combined with cue-exposure as a treatment for heroin addicts." Physiology & behavior 130 (2014): 34-39.
[6] M. -C. Tsai et al., "An Intelligent Virtual-Reality System With Multi-Model Sensing for Cue-Elicited Craving in Patients With Methamphetamine Use Disorder," in IEEE Transactions on Biomedical Engineering, vol. 68, no. 7, pp. 2270-2280, July 2021
[7] Wang, Yong-Guang, Zhi-Hua Shen, and Xuan-Chen Wu, "Detection of patients with methamphetamine dependence with cue-elicited heart rate variability in a virtual social environment," in Psychiatry research 270, 2018
[8] S. Zhu, D. Wang, K. Yu, T. Li and Y. Gong, "Feature Selection for Gene Expression Using Model-Based Entropy," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 1, pp. 25-36, Jan.-March 2010, doi: 10.1109/TCBB.2008.35.
[9] Eddie, David, et al. "Heart rate variability biofeedback: Theoretical basis, delivery, and its potential for the treatment of substance use disorders." Addiction research & theory 23.4 (2015): 266-272.
[10] Petmezas, Georgios, et al. "Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets." Biomedical Signal Processing and Control 63 (2021): 102194.
[11] Shoeibi, Afshin, et al., "Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models," in Frontiers in Neuroinformatics, 15, 2021
[12] Cai, Hanshu, et al. "Feature-level fusion approaches based on multimodal EEG data for depression recognition," in Information Fusion 59, 2020
[13] Zhang-James, Y., Chen, Q., Kuja-Halkola, R., Lichtenstein, P., Larsson, H. and Faraone. S.V., “Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data,” in J. Child Psychol. Psychiatr., 61: 1370-1379, 2020
[14] Fatima, M. and Pasha, M, “Survey of Machine Learning Algorithms for Disease Diagnostic,” in Journal of Intelligent Learning Systems and Applications, 9, 1-16. doi: 10.4236/jilsa.2017.91001.
[15] Mary R. Lee, et al., "Using Machine Learning to Classify Individuals With Alcohol Use Disorder Based on Treatment Seeking Status", in EClinicalMedicine, Volume 12, Pages 70-78, 2019

[16] Chen, Chun-Chuan, et al. "Neuronal Abnormalities Induced by an Intelligent Virtual Reality System for Methamphetamine Use Disorder." IEEE Journal of Biomedical and Health Informatics 26.7 (2022): 3458-3465.
[17] Makowski, Dominique, et al. "NeuroKit2: A Python toolbox for neurophysiological signal processing." Behavior research methods 53.4 (2021): 1689-1696.
[18] Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.
[19] Barr A.M. et al., "The need for speed: An update on methamphetamine addiction," Journal of Psychiatry and Neuroscience, 31 (5), pp. 301 - 313, 2006
[20] American Psychiatric Association (COR), “Substance-Related and Addictive Disorders,” in Diagnostic and Statistical Manual of Mental Disorders: Dsm-5, American, American Psychiatric Publishing, 2013, pp. 481-591
[21] Mumtaz, W., Vuong, P.L., Xia, L. et al. “An EEG-based machine learning method to screen alcohol use disorder,” in Cogn Neurodyn 11, 161–171, 2017
[22] M. G. Doborjeh, et al., "A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects," in IEEE Transactions on Biomedical Engineering, vol. 63, no. 9, pp. 1830-1841, Sept. 2016
[23] Kemp Andrew H., Quintana Daniel S., Quinn Candice R., Hopkinson Patrick, Harris Anthony W. F., “Major depressive disorder with melancholia displays robust alterations in resting state heart rate and its variability: implications for future morbidity and mortality,” in Frontiers in Psychology, 5, 2014
[24] A. Arsalan, M. Majid, S. M. Anwar and U. Bagci, "Classification of Perceived Human Stress using Physiological Signals," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019
[25] Anuragi, Arti, and Dilip Singh Sisodia, "Alcohol use disorder detection using EEG Signal features and flexible analytical wavelet transform," in Biomedical Signal Processing and Control 52, 2019
[26] S. Mohan, C. Thirumalai and G. Srivastava, "Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques," in IEEE Access, vol. 7, pp. 81542-81554, 2019
[27] H. Kim, L. Kim, and C.-H. Im, “Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use,” in Sensors, vol. 19, no. 16, p. 3475, Aug. 2019
[28] Erguzel, Turker Tekin, Cumhur Tas, and Merve Cebi, "A wrapper-based approach for feature selection and classification of major depressive disorder–bipolar disorders," in Computers in biology and medicine 64 (2015): 127-137.
[29] D. Sagga, A. Echtioui, R. Khemakhem and M. Ghorbel, "Epileptic Seizure Detection using EEG Signals based on 1D-CNN Approach," in 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2020
[30] Toraman, Suat, "Automatic recognition of preictal and interictal EEG signals using 1D-capsule networks," in Computers & Electrical Engineering 91, 2021
[31] BioSppy, [Online].
Available: https://biosppy.readthedocs.io/en/stable/index.html
[32] pyHRB, [Online].
Available: https://pyhrv.readthedocs.io/en/latest/