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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林啟萬(Chii-Wann Lin) | |
| dc.contributor.author | Chia-Hung Cho | en |
| dc.contributor.author | 卓嘉虹 | zh_TW |
| dc.date.accessioned | 2023-03-19T21:18:27Z | - |
| dc.date.copyright | 2022-08-23 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-01 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83797 | - |
| dc.description.abstract | 帕金森氏症 (Parkinson’s disease, PD) 是一種影響中樞神經系統的慢性神經 退化性疾病，目前影響全球約一千萬人 [1]。深腦電刺激 (deep brain stimulation, DBS) 的技術在運動障礙和神經系統疾病中的應用，包括 PD、震顫、肌張力障礙、癲癇、強迫症等，已被證明是一種有效的治療方式 [2]。然而，廣泛使用的 開迴路系統仍然存在一些尚待修正的缺點，例如它們的個體依賴性、能量消耗 程度、頻繁回診和試錯性調整的特徵 [3]。閉迴路的策略採用具有判別性的訊 號或生物標誌物，從而使系統能夠透過算法自動調整 DBS 參數 [3]。我們設計強化學習 (reinforcement learning, RL) 與 Gym 框架，模擬基底神經節-丘腦 (basal ganglia-thalamic, BGT) 大腦網路作為訓練環境，並為任何輸入狀態找到適當的刺激參數(頻率和振幅)。特徵提取模塊則作為 BGT 大腦網路(動作電位訊號)與來自真實大腦的胞外訊號之間的映射工具，進而允許未來的動物實驗和臨床試 驗的測試。結果顯示，基於RL的DBS控制策略在能秏上較開迴路系統節省了 68.81% 的平均功率，並修正丘腦(thalamus, TH)中的錯誤響應(平均錯誤響應在正常情況為 0.0; 在PD下修正回 0.0258)，同時為未來應用奠定了基礎。 | zh_TW |
| dc.description.abstract | Parkinson’s disease (PD) is a chronic neurodegenerative disease affecting the central nervous system and currently influencing about 10 million people worldwide [1]. The usage of deep brain stimulation (DBS) technology in movement disorders and neurological diseases, including PD, tremor, dystonia, epilepsy, obsessive-compulsive disorder (OCD), etc., has proven to be an effective treatment modality [2]. However, general open-loop systems pose several shortcomings that have yet to be revised, such as their subject dependency, energy-consuming, frequent-clinic visiting, and trial-and-error adjusting features [3]. The closed-loop strategy employs discriminative signals/biomarkers to enable the system to tune parameters automatically through the designed algorithms [3]. We designed reinforcement learning (RL) with the Gym framework that models the basal ganglia-thalamic (BGT) brain network as a training environment and finds appropriate stimulation parameters (frequency and amplitude) for different input states. The feature extraction module was a mapping tool between the BGT brain network (AP signals) and extracellular signals from real brains, permitting future animal experiments and clinical trials. Results showed that the RL-based DBS control strategy significantly outperforms open-loop systems in energy efficiency, i.e., conserving 68.81% of average power dissipation, and revises error responses in the thalamus (i.e., an average EI of 0.0 in normal and 0.0258 in PD states) while establishing a foundation for future application. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T21:18:27Z (GMT). No. of bitstreams: 1 U0001-2001202216034000.pdf: 24879070 bytes, checksum: 891907f8e111f08652b7ba9757d020a7 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i Acknowledgements ii 摘要 iii Abstract iv Contents v List of Figures viii List of Tables xi Denotation xii Chapter 1 Introduction 1 1.1 Parkinson’sDisease .......................... 1 1.2 Deep Brain Stimulation ........................ 4 1.2.1 DBS Brief Overview ......................... 4 1.2.2 DBS System Features......................... 5 1.2.3 Principles of Electrical Stimulation.................. 7 1.2.4 Open-loop and Closed-loop DBS................... 15 1.2.5 Biomarkers and Features for PD ................... 17 1.2.6 Machine Learning in DBS ...................... 19 1.3 Reinforcement Learning ........................ 21 1.3.1 Preliminaries ............................. 22 1.3.2 Policy Gradient ............................ 24 1.3.3 Actor-Critic Algorithms........................ 25 1.3.4 RL Workflow ............................. 27 1.4 Aims and Objectives .......................... 29 Chapter 2 Methodology 31 2.1 PD Brain Model Simulation—The BGTnetwork .......................... 31 2.1.1 Model for Each Neuron........................ 32 2.1.2 Model for Synaptic Currents ..................... 36 2.2 Environmental Interfacing ....................... 39 2.2.1 Action and State............................ 39 2.2.2 Episode Termination Condition.................... 44 2.2.3 Reward ................................ 45 2.3 RL Model Construction ................................ 47 2.4 Related Works ................................ 50 Chapter 3 Results and Discussion 52 3.1 Environmental Validation ................................ 52 3.1.1 Single-compartment Ablation..................... 52 3.1.2 Integrated BGT Network Outcomes ................. 57 3.1.3 Biomarker Signal for Model Training ........................... 59 3.2 Rewards Ablation............................ 62 3.3 RL Control Strategies ......................... 63 3.3.1 PD Initial States............................ 64 3.3.2 Normal Initial States ......................... 66 3.4 Comprehensive Assessments ......................... 68 Chapter 4 Conclusions and Prospects 73 References 77 Appendix A — BGT Model Parameters 88 Appendix B — RL Model Parameters 93 | |
| dc.language.iso | en | |
| dc.subject | 獎勵塑造 | zh_TW |
| dc.subject | 基底核-丘腦網路 | zh_TW |
| dc.subject | 閉迴路深腦電刺激 | zh_TW |
| dc.subject | 帕金森氏症 | zh_TW |
| dc.subject | 強化學習 | zh_TW |
| dc.subject | Parkinson’s disease (PD) | en |
| dc.subject | reward shaping | en |
| dc.subject | reinforcement learning (RL) | en |
| dc.subject | closed-loop deep brain stimulation (cl-DBS) | en |
| dc.subject | basal ganglia-thalamic (BGT) network | en |
| dc.title | 基於強化學習和基底核-丘腦動態網路之帕金森氏症閉迴路深腦電刺激演算法 | zh_TW |
| dc.title | Closed-loop Deep Brain Stimulation Algorithm for Parkinson's Disease based on Reinforcement Learning and Basal Ganglia-Thalamus Network Dynamics | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.author-orcid | 0000-0001-5301-587X | |
| dc.contributor.oralexamcommittee | 劉宏輝(Horng-Huei Liou),鄭士康(Shyh-Kang Jeng),黃博浩(Po-Hao Huang) | |
| dc.subject.keyword | 基底核-丘腦網路,閉迴路深腦電刺激,帕金森氏症,強化學習,獎勵塑造, | zh_TW |
| dc.subject.keyword | basal ganglia-thalamic (BGT) network,closed-loop deep brain stimulation (cl-DBS),Parkinson’s disease (PD),reinforcement learning (RL),reward shaping, | en |
| dc.relation.page | 94 | |
| dc.identifier.doi | 10.6342/NTU202200118 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2022-08-02 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| 顯示於系所單位： | 醫學工程學研究所 | |
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