臺灣博碩士論文加值系統

近年來睡眠問題與壓力問題已成為公共衛生問題，慢性疾病的預防與治療與睡眠及壓力息息相關，睡眠與壓力問題所產生之經濟成本與衝擊，更是不容忽視。睡眠呼吸中止症廣泛的潛藏於大眾之睡眠中而不自覺，進而影響睡眠品質與日常生活。本研究的假設是：因為睡眠呼吸中止症改變了自律神經系統的平衡性，使得交感神經被過度活化，讓身體無法透過睡眠，達到充分休息及修復受損的組織。另外，本研究的另一個假設是：可以透過呼吸調控的方式，來活化副交感神經系統，達到改善睡眠品質的目的。有鑑於此，本研究利用心率變異度（HRV ）分析來分析睡眠呼吸中止症病人於睡眠時的自律神經系統狀態，發現嚴重的睡眠呼吸中止症病人，在去除睡眠呼吸中止事件後，於深層睡眠時其自律神經系統仍處於交感神經主導狀態，使得非快速眼動期與清醒階段時的自律神經平衡性無法產生明顯區分。另若於非快速眼動期時發生睡眠呼吸中止事件，會興奮交感神經並抑制副交感神經，進而改變自律神經系統平衡性影響深層睡眠，可能讓睡眠階段提早轉換至快速眼動期或清醒階段，破壞正常睡眠結構影響睡眠品質。因自律神經活性與睡眠深淺結構相關，本研究進一步使用一具以心律變異為基礎的睡眠深淺偵測裝置Actiheart，與睡眠檢查專用的睡眠多項式生理監測儀與進行臨床測試比較，結果發現此方式，在判斷深層睡眠部分具有準確度 81.52％，但是準確度會隨睡眠呼吸中止事件的頻繁度增加而降低，然後於AHI>=20 events/hr再提昇。由此可確認睡眠呼吸中止症，確實會於睡眠中活化交感神經並抑制副交感神經，改變自律神經系統的平衡性，進而影響睡眠結構，使得身體無法透過睡眠的過程達到充分休息。因此，若可以提早檢測出睡眠呼吸中止症的嚴重性，將可大大提升睡眠品質，於是，本研究開發一睡眠分析軟體並與Kubios軟體驗證比較，兩者的心律變異參數相關性皆高於0.96，後續利用開發之軟體分析睡眠資料，進而找出睡眠呼吸中止事件發生時其相對應的心律變異特徵參數，搭配類神經網路的方法用於偵測睡眠呼吸阻塞事件，其準確性為70.7%。在能監測睡眠狀態之後，更重要的是如何改善睡眠品質。因此，本研究進一步探討呼吸調控的方式對活化副交感神經的可行性，研究結果發現呼吸調控的確能有效地提升副交感神經活性讓身體放鬆，相較於其他呼吸調控模式，在吸氣3秒及吐氣6秒的模式呼吸調控訓練後，副交感神經活性最為有效被提升 (HFnu, 0.32, p < 0.05)。此呼吸調控方式若經長時間不斷練習，應可促進健康，釋放壓力進而改善睡眠品質。未來本研究所開發的軟體可繼續擴充睡眠分析的參數，如睡眠結構、覺醒事件…等，進一步結合呼吸調控與單導程心電圖感測器，整合為穿戴式呼吸調控睡眠回饋系統，做為以呼吸調控來提升睡眠品質的訓練系統。

Sleep apnea causes sleeping disturbance and also influences the quality of sleep obviously. I hypothesized that sleep apnea affects the balance of autonomic nervous system (ANS), which activates sympathetic nervous system (SNS) and suppress parasympathetic nervous system (PNS) during sleep. This causes body cannot get appropriate rest and repair the tissue through sleep. Since the PNS restores and conserves body and in response to stimulations. hence, voluntary control of respiratory rhythm, known as respiratory regulation, is considered a type of external stimulation to the body. Therefore, I also hypothesized that PNS can be activated by respiratory regulation for improving sleep quality. Upon the hypotheses, this study used heart rate variability (HRV) to analyze the data of sleep apnea patients, the results indicated that the severe group (AHI ≥ 30) has higher sympathetic nervous system (SNS) activity and lower parasympathetic nervous system (PNS) activity in nonrapid eye movement (NREM). Furthermore, apnea events occurring in NREM activated SNS and suppressed PNS activity. Owing to sleep apnea is associated with an imbalance of ANS activity. Therefore, twenty-five subjects were examined simultaneously using polysomnography (PSG) and an HRV-based sleep staging method during sleep. The method had an accuracy of 81.52% in identifying NREM when compared with the accuracy of PSG results. Furthermore, the accuracy of identifying NREM sleep in healthy (AHI < 5) and severe sleep apnea (AHI ≥ 30) groups was 92.49% and 86.82%, respectively, which was significantly greater than in the other groups. The findings proved that sleep apnea affects the balance of ANS, which activates SNS and suppress PNS during sleep. Hence, if sleep apnea severity can be early detected, then the sleep quality will be significantly improved. We further developed a software using Back-Propagation Network as the classifier to identify sleep apnea. The results show that its accuracy is 70.7%. In addition to identifying sleep quality, how to improve sleep quality is also important, therefore, we studied the efficacy of respiratory regulation on PNS using HRV. The results highlighted that sympathetic nervous system and PNS are easily activated and inhibited, respectively, during Mode 2 (inhalation 3 s, exhalation 6 s) breathing mode. This allowing PNS to quickly become predominant, causing the body to relax during the following resting session (HFnu, 0.32, p < 0.05). These results suggest that Mode 2 can be used as a breathing model for long-term improvements in PNS function. In the future, those works can be further integrated as a sleep bio-feedback system with the respiratory regulation method for improving sleep quality.

TABLE OF CONTENTS
摘要 I
ABSTRACT II
誌謝 III
TABLE OF CONTENTS IV
LIST OF FIGURES VII
LIST OF TABLES X
Chapter 1 Introduction 1
1.1 Sleep disorder 1
1.2 Hypnogram 1
1.3 Heart rate variability in sleep disorder 2
1.4 Sleep apnea on autonomic nervous system 3
1.5 Respiratory regulation 4
1.6 Study aims 5
Chapter 2 ANS activity in each sleep stage during OSA 6
2.1 Methods 6
2.1.1 Data acquisition from public database 6
2.1.2 Experimental design and study flow 7
2.1.3 Introducing a mutually intelligible expression 8
2.1.4 Preprocessing of the input ECG data 9
2.1.5 Activity parameter calculations for HRV statistical analysis 10
2.2 Results 10
2.2.1 Comparison of the sleep stages in free apnea by carrying out Aim I processes 10
2.2.2 Awake and REM 14
2.2.3 Awake and NREM 15
2.2.4 REM and NREM 16
2.2.5 Apnea occurred in REM and NREM stages by carrying out Aim II processes 17
2.3 Discussions 19
Chapter 3 Evaluation of HRV based sleep staging method on OSA 21
3.1 Materials and Methods 21
3.1.1 Study protocol 21
3.1.2 Sleep staging method 22
3.1.3 Accuracy analysis 22
3.1.4 Statistical analysis 23
3.2 Results 23
3.2.1 Subject characteristics 23
3.2.2 Sleep staging accuracy 25
3.2.3 The correlation between sleep staging accuracy and AHI 26
3.2.4 Sleep staging accuracy in sleep apnea group 28
3.3 Discussions 30
Chapter 4 Using neural network classifier for OSA identification 32
4.1 Methods 32
4.1.1 The process of HRV analysis software 33
4.1.2 The comparison of parameters with and without sleep apnea 34
4.1.3 The sleep apnea detection algorithm 34
4.2 Results 36
4.3 Discussions 41
Chapter 5 Respiratory regulation on PNS 43
5.1 Materials and methods 43
5.1.1 Sample size estimation 44
5.1.2 The study flow and respiratory regulation pattern 44
5.1.3 The respiratory regulation coach 45
5.1.4 The ECG recorder 46
5.1.5 Statistical method 46
5.2 Results 46
5.2.1 ANS response of all study sessions 46
5.2.2 ANS response of breathing modes 48
5.2.3 ANS response immediately after respiratory regulation 50
5.3 Discussions 51
Chapter 6 Conclusions 53
References 56

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