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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴飛羆 | zh_TW |
| dc.contributor.advisor | Feipei Lai | en |
| dc.contributor.author | 蔡昌恆 | zh_TW |
| dc.contributor.author | Chan-hen Tsai | en |
| dc.date.accessioned | 2023-09-07T16:53:52Z | - |
| dc.date.available | 2025-07-25 | - |
| dc.date.copyright | 2023-09-11 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-31 | - |
| dc.identifier.citation | [1] de Jonge P, Roest AM, Lim CC, Florescu SE, Bromet EJ, Stein DJ, et al. Cross-national epidemiology of panic disorder and panic attacks in the world mental health surveys. Depress Anxiety 2016;33:1155-1177.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89411 | - |
| dc.description.abstract | 提供恐慌症患者睡眠和身體活動建議是相當重要的，但過去卻較少被仔細研究。本研究是一項三年期前瞻性世代研究及研發計畫，目標在預測未來七天內的恐慌發作（PA）、狀態焦慮（SA）、特質焦慮（TA）以及恐慌症嚴重程度（PDS）。我們從一家綜合醫院納入了148名恐慌症患者，使用DSM-5、MINI和臨床評估問卷：BDI、BAI、PDSS-SR、STAI以及Garmin智慧手環，從2020年6月16日至2023年6月10日記錄他們的每日睡眠、身體活動和心跳。團隊應用RNN、LSTM、GRU深度學習和SHAP可解釋模型來分析數據。有99人完成了本研究。從LSTM（長期和短期記憶）模型計算的7天預測準確率分別為PA、SA、TA、PDS：92.8％、83.6％、87.2％ 和75.6％。透過SHAP可解釋模型，PA較容易復發於：初始BDI及BAI分數較高、共病憂鬱症、泛焦慮症或懼曠症的病人。然而，PA在以下情況有所下降：每日平均心跳在72-87次/分鐘區間、每日爬升大於9層樓、總睡眠時間介於6小時23分至10小時50分、以及深度睡眠期大於50分鐘。綜合以上，本試驗透過機器學習與深度學習分析了穿戴式手環和問卷資料，預測七天後恐慌發作的正確機率約為75.6-92.8%，而且，研究顯示充足的睡眠以及每日爬升樓層數可降低PA復發機率。此外，運用個案管理合併上述預測及回饋系統，相較於常規治療，顯著改善了恐慌症維持期治療 (p < 0.05) 的結果。 | zh_TW |
| dc.description.abstract | Sleep and physical activity suggestions for panic disorder (PD) patients are critical but less surveyed. This three-year prospective cohort study aims to predict panic attacks (PA), state anxiety (SA), and trait anxiety (TA), panic disorder severity (PDS) in the upcoming seven days. We enrolled 148 PD patients from one general hospital. We used DSM-5, MINI, and clinical app questionnaires: BDI, BAI, PDSS-SR, STAI, and wearables, recording their daily sleep, physical activities, and heart rates from June 16, 2020, to June 10, 2023. Our teams applied RNN, LSTM, GRU deep learning, and SHAP explainable methods to analyze the dataset. Ninety-nine completed this study. The 7-day prediction accuracies for PA, SA, TA, and PDS were 92.8%, 83.6%, 87.2%, and 75.6%, respectively, from LSTM (Long and Short-Term Memory) model. By SHAP explainable model, higher initial BDI.BAI score, comorbidities with depressive disorder, general anxiety disorder, or agoraphobia predict increased chances of PAs. However, PA decreased in the following conditions: daily average heart rates fall between 72-87 bpm, daily floors climbing more than 9 floors, total sleep duration between 6h23m-10h50m, deep sleep over 50m. Deep learning provides a 75.6-92.8% accuracy of 7-day PA prediction through smartwatch and questionnaire data. Recurrent PA chances decrease if participants' daily sleep time falls between 6h23m-10h50m or daily floors climb more than nine floors. Furthermore, case management with this system improves maintenance treatment (p < 0.05) compared with treatment as usual. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-07T16:53:52Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-07T16:53:52Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Panic Attacks 1 1.2 Sleep and Panic Attacks 2 1.3 Physical Activities and Panic Attacks 2 1.4 HRV, Heart Rates, and Panic Attacks 3 1.5 Panic Attack Prediction System 3 1.6 Explainable Artificial Intelligence (XAI) 6 1.7 Treatment as Usual for Panic Disorder 7 1.8 Mobile-aided Case Management 8 1.9 The Aim of this Study 10 Chapter 2 Methods 11 2.1 Participants 12 2.2 Smartwatches and Physiological Data Storage 12 2.3 Questionnaires and Psychological Data Storage 13 2.4 System Architecture and API System 15 2.5.1 Ground Truth (labels) and input features 17 2.5.2 Training process 18 2.6 Explainable AI Model 20 2.8 Feedback System 21 2.9 Mobile-aided Case Management vs. Treatment as Usual 25 2.10 Efficacy Assessment 26 2.11 Statistical Analysis 30 Chapter 3 Results 32 3.1 Participants' Demographic Summary 32 3.2 PA, SA, TA, PDS Prediction Using ML Method 34 3.3 PA Prediction Using Deep Learning Methods 39 3.4 PA Prediction Using Regression Models 43 3.5 PA Explainable AI models 48 3.6 Panic Symptoms and Their Prevalence 73 3.7 The HRV during PA and Resting State 74 3.8 Feedback System and its Efficacy 79 3.8.1 Demography and symptom improvement 79 3.8.2 Qualitative Result 79 Chapter 4 Discussion 87 4.1 Principal Finding 87 4.2 Sub-analysis Finding of HRV and PA Symptoms 88 4.3 Strength 89 4.4 Limitations 90 4.5 System and Platforms 91 4.6 Data Types and Feature Selection 92 4.7 Training Models 100 4.8 Feedback System and PD Case Management 101 4.9 Clinical Suggestion 106 4.10 Future Plan 106 Chapter 5 Conclusion 108 REFERENCE 109 Appendix: Abbreviations 122 | - |
| dc.language.iso | en | - |
| dc.subject | 可解釋模型 | zh_TW |
| dc.subject | 個案管理 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 穿戴式裝置 | zh_TW |
| dc.subject | 恐慌發作 | zh_TW |
| dc.subject | 恐慌症 | zh_TW |
| dc.subject | deep learning (DL) | en |
| dc.subject | Panic disorder (PD) | en |
| dc.subject | case management | en |
| dc.subject | wearable device | en |
| dc.subject | explainable AI (XAI) model | en |
| dc.subject | panic attack (PA) | en |
| dc.title | 恐慌發作的預測與可解釋模型：運用穿戴式裝置及機器學習之研發與世代研究 | zh_TW |
| dc.title | Panic attack prediction and explainable model: development and cohort study using wearable devices and machine learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 薛智文;何弘能;許凱平;張鑾英;趙坤茂;林澤;簡意玲 | zh_TW |
| dc.contributor.oralexamcommittee | Chih-Wen Hsueh;Hong-Nerng Ho;Kai-Ping Hsu;Luan-Yin Chang;Kun-Mao Chao;Che Lin;Yi-Ling Chien | en |
| dc.subject.keyword | 恐慌症,恐慌發作,穿戴式裝置,深度學習,可解釋模型,個案管理, | zh_TW |
| dc.subject.keyword | Panic disorder (PD),panic attack (PA),wearable device,deep learning (DL),explainable AI (XAI) model,case management, | en |
| dc.relation.page | 124 | - |
| dc.identifier.doi | 10.6342/NTU202302079 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-02 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | - |
| dc.date.embargo-lift | 2025-07-25 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 4.34 MB | Adobe PDF |
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