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研究生：	林俊廷 Lin, Chun-Ting
論文名稱：	應用機器學習對阻塞型呼吸中止症之手術反應的多重生理信號進行特徵量化 Characterization of Polysomnogram Signals for Surgical Response in Obstructive Sleep Apnea Using Machine Learning
指導教授：	鄭國順 Cheng, Kuo-Sheng
學位類別：	碩士 Master
系所名稱：	工學院 - 生物醫學工程學系 Department of BioMedical Engineering
論文出版年：	2024
畢業學年度：	112
語文別：	英文
論文頁數：	73
中文關鍵詞：	睡眠呼吸中止症、機器學習、手術結果
外文關鍵詞：	Obstructive Sleep Apnea, Machine learning, Surgical outcomes
相關次數：	點閱：53 下載：2
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阻塞性睡眠呼吸暫停症 (OSA) 是一種常見的睡眠障礙，其主要病理機制是上呼吸道狹窄。持續正壓呼吸 (CPAP) 是治療OSA的非手術療法，它通過維持氣道通暢來防止呼吸暫停。然而，一些患者無法耐受CPAP，這使得替代治療方案變得必要。為了解決這一問題，各種手術干預措施如懸雍垂腭咽成形術 (UPPP)、頦舌肌前移術和上下頜骨前移術已被開發出來，旨在增加上呼吸道肌肉張力和擴大咽腔空間。這些手術的成功率差異顯著，範圍從45%到78%不等，這凸顯了準確識別合適手術候選人的重要性，以避免不必要的手術並達到最佳治療效果。目前，手術成功的預測主要依賴於睡眠外科醫生的主觀判斷，而基於多個多導睡眠圖 (PSG) 參數或上呼吸道評估的客觀預測方法在臨床實踐中未得到充分應用。本研究旨在通過機器學習技術，評估使用PSG信號參數作為特徵來預測OSA患者手術效果的有效性。研究中引入了四個新的參數——事件恢復時間、迴路增益、樣本熵和呼吸速率變異性 (RRV)，並對四種模型——支持向量機 (SVM)、邏輯回歸、隨機森林分類器和梯度提升分類器——的性能進行了評估和比較。

Obstructive Sleep Apnea (OSA) is a common sleep disorder primarily caused by upper airway narrowing, which is a key pathophysiological mechanism. Continuous Positive Airway Pressure (CPAP) is a widely used non-surgical treatment for OSA that prevents apnea events by maintaining airway patency through a constant stream of air. However, some patients are unable to tolerate CPAP, necessitating alternative treatment options. Various surgical interventions, such as uvulopalatopharyngoplasty (UPPP), genioglossus advancement, and maxillomandibular advancement, have been developed to address this issue by increasing upper airway muscle tone and expanding the pharyngeal space. The success rates of these surgeries vary significantly, ranging from 45% to 78%, highlighting the importance of accurately identifying suitable candidates to avoid unnecessary procedures and achieve optimal outcomes. Currently, the prediction of surgical success largely depends on the subjective judgment of sleep surgeons, with objective prediction methods based on multiple polysomnography (PSG) parameters or upper airway assessments being underutilized in clinical practice. This study aims to evaluate the effectiveness of using PSG signal parameters as features in predicting surgical outcomes for OSA patients through machine learning techniques. Four novel parameters—Event-recovery time, Loop gain, Sample entropy, and Respiratory Rate Variability (RRV)—were introduced into the predictive models. The performance of four models—Support Vector Machine (SVM), Logistic Regression, Random Forest Classifier, and Gradient Boosting Classifier—was assessed and compared.

中文摘要 I
ABSTRACT II
LIST OF FIGURES VII
LIST OF TABLES VIII
CHAPTER 1	INTRODUCTION 1
1 Background 1
2 Distribution 4
3 Treatment Options 5
4 Issues 6
CHAPTER 2	LITERATURE REVIEW 8
1 Analysis of Treatments in OSA Research 8
2 Phenotype of OSA 9
2.1 Pcrit (Critical Pressure) 10
2.2 Genioglossus Muscle Responsiveness 11
2.3 Arousal Threshold to Respiratory Stimuli 12
2.4 High Loop Gain 13
3 OSA Phenotypes and Surgical Treatment 15
4 Machine Learning in Predicting Surgical Outcomes 16
5 Success criteria in obstructive sleep apnea therapy 19
CHAPTER 3MATERIAL AND METHODS 21
1 Research Framework 21
2 Data Acquisition 22
3 Experiment Flowchart 24
4 Event Detection 25
5 Novel parameter 30
5.1 Event-recovery time 30
5.2 Modify Loop gain 31
5.3 Sample entropy 34
5.4 RRV 35
6 Statistical analysis methods 37
7 Machine Learning 38
7.1 Machine Learning Workflow 39
7.2 Grouping and Selection 39
7.3 Recursive Feature Elimination (RFE) 41
7.4 Mean Absolute Error (MAE) 41
7.5 Intraclass Correlation Coefficient (ICC) 42
CHAPTER 4	RESULT AND DISCUSSION 45
1 Parameters and Severity 45
2 Parameters and Surgical Outcomes 47
3 Machine Learning Predictions 49
3.1 Classification Tasks 49
3.2 Regression Tasks 52
CHAPTER 5 CONCLUSION 55
REFERENCES 58
APPENDIX 62
                                    

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