增強接受化療的乳癌患者的放鬆：基於 VR 的正念呼吸訓練干預

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姓名	黃雅琪(Ya-Chi Huang) 查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	增強接受化療的乳癌患者的放鬆：基於 VR 的正念呼吸訓練干預 (Enhancing Relaxation in Breast Cancer Patients Undergoing Chemotherapy: A VR-Based Mindfulness Breathing Training Intervention)
檔案	[Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] 至系統瀏覽論文 (2026-7-29以後開放)
摘要(中)	乳癌是女性常見的癌症類型，也是癌症相關死亡的主要原因。化療雖然是主要的治療方式，但常常會引起患者不良的心理反應。這項研究引入了一種創新系統，旨在化療前誘導乳癌患者處於放鬆狀態。該系統將虛擬實境 (VR) 與透過語音指令引導的正念呼吸練習相結合，並利用生物識別數據提供生物回饋，為患者護理提供整體方法。透過調查和生理數據分析，針對傳統方法和無幹預的對照組評估了該系統的有效性。結果表明，與聲音組和對照組相比，參與者在幹預後達到了明顯更高的放鬆狀態。這項研究展示了結合虛擬實境和正念練習來減輕乳癌患者化療心理負擔的潛力。
摘要(英)	Breast cancer is a prevalent type of cancer among women and a leading cause of cancer-related deaths. Chemotherapy, a primary treatment method, often induces adverse psychological reactions in patients. This study introduces a novel system aimed at achieving a state of relaxation in breast cancer patients prior to undergoing chemotherapy. The system integrates virtual reality (VR) with mindfulness breathing training, using voice guidance. It collects physiological data to generate biofeedback, providing a holistic approach to patient care. The effectiveness of this system was evaluated against traditional methods and a control group with no intervention, through surveys and physiological data analysis. Results indicated that participants achieved a significantly higher state of relaxation post-intervention, as compared to the control and voice groups. This study demonstrates the potential of combining VR and mindfulness practices in reducing the psychological burden of chemotherapy in breast cancer patients.
關鍵字(中)	★ 虛擬實境 ★ 機器學習 ★ 心率變異性 ★ 皮膚電反應 ★ 呼吸率變異性 ★ 腦電圖 ★ 靜息狀態	關鍵字(英)	★ Virtual Reality (VR) ★ Machine Learning (ML) ★ Heart Rate Variability (HRV) ★ Galvanic Skin Response (GSR) ★ Respiratory Rate Variability (RRV) ★ Electroencephalogram (EEG) ★ resting-state
論文目次	摘要 i Abstract ii 致謝 iii Table of Contents iv List of Figures vi List of Tables vii 1. Introduction 1 2. Related Works 5 3. Method 8 3-1 System Architecture 8 3-2 Experiments 10 3-2-1 Subject 10 3-2-2 Procedure 11 3-2-3 Measurement 12 3-3 Analysis Method 12 3-3-1 Feature Extraction 12 3-3-2 Statistical Analysis 16 3-3-3 Machine Learning Method 17 4. Results 19 4-1 Subject 19 4-2 Feature Performance and Statistical Analysis 19 4-2-1 EEG 19 4-2-2 HRV 21 4-2-3 RES 23 4-2-4 GSR 24 4-2-5 Effect Size 25 4-3 Relaxation Level 27 4-4 Machine Learning Analysis 30 5. Discussion 33 5-1 Significant Features 33 5-2 Short-term Effect 34 5-3 Fusion 34 6. Conclusion 36 Reference 37
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指導教授	吳曉光葉士青	審核日期	2024-7-31
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