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系統識別號 U0026-1102202005563100
論文名稱(中文) 穿戴式裝置於阻塞型睡眠呼吸中止症之應用
論文名稱(英文) Application of Wearable Devices in Patients with Obstructive Sleep Apnea
校院名稱 成功大學
系所名稱(中) 工程科學系
系所名稱(英) Department of Engineering Science
學年度 108
學期 1
出版年 109
研究生(中文) 劉文德
研究生(英文) Wen-Te Liu
電子信箱 lion5835@gmail.com
學號 N98004016
學位類別 博士
語文別 英文
論文頁數 145頁
口試委員 指導教授-莊哲男
口試委員-廖德祿
口試委員-林政佑
口試委員-嚴成文
口試委員-羅友倫
中文關鍵字 阻塞型睡眠呼吸暫停  多導睡眠圖  穿戴式裝置  呼吸暫停低通氣指數  心率的週期性變化 
英文關鍵字 Obstructive sleep apnea  Polysomnography  Wearable device  Apnea-hypopnea index  Cyclic variation of heart rate 
學科別分類
中文摘要 阻塞性睡眠呼吸暫停(OSA)是現代社會中最重要的公共衛生問題之一,直到現在,多導睡眠監測(PSG)仍是評估OSA嚴重程度的黃金標準。但是,由於環境因素的影響,OSA患者在家中的睡眠和實驗室PSG期間睡眠的現實情況可能會大不相同,並且偏倚的程度和原因尚不清楚。
本研究共納入125例患者,分別來自為新光吳火獅紀念醫院和衛生福利部雙和醫院的睡眠中心。研究使用包括心電圖和加速度計信號在內的穿戴式裝置RootiRx,步驟為收集四天的睡眠生理數據,第一天由PSG與RootiRx同步進行檢查,之後的三天夜間則由RootiRx檢測睡眠生理訊號。從RootiRx中檢測到的生物信號定義為心率變異指數(CVHRI),胸腔呼吸做功指數(CEI)以及合併CVHRI和CEI的Rx指數(Rx index)。根據兩個標準將患者分為三組,一類是根據家庭睡眠時的仰臥位百分比(低,中和高仰臥位百分比)分組,另一組是通過實驗室內PSG進行檢查的OSA嚴重程度(AHI <15、15至< 30,≥30)。該研究分為兩個步驟。首先,我們驗證了穿戴式裝置RootiRx檢測OSA嚴重度的準確性。其次,我們比較了實驗室內PSG和家用穿戴式裝置對OSA患者的測試結果以及可能造成的影響。
結果:在這項研究的第一步中,穿戴式裝置收集的生理訊號驗證表明,與實驗室內PSG在OSA嚴重性方面存在顯著相關性,包括所有三種生物信號(CVHRI,r = 0.7433; CEI,r = 0.7017 ; Rx指數,r = 0.7826;所有P <0.0001)。在這項研究的第二步中,先將患者按照居家低,中和高仰臥位睡眠的方式分組。在實驗室內PSG期間觀察到他們的仰臥位百分比顯著增加(增加的百分比分別為28.4±22.1%,24.2±22.2%,11.9±18.1%)。此外,在醫院評估時,這三組的OSA嚴重程度也顯著升高(升高程度分別為10.4±14.3、5.6±12.4、6.2±14.3和p <0.0001,P = 0.0054,P = 0.0084)。在臨床方面,通過實驗室內PSG檢測到患有嚴重OSA(AHI≥30)的患者在家中仰臥位百分比較低(59.65±20.64%,56.54±20.09%,47.53±23.85%,AHI <15,15至<30,分別≥30)。通過實驗室內PSG評估時,無論OSA嚴重程度如何,仰臥位百分比均顯著增加(與家庭測試相比,分別為14.6±22.8%,21.8±20.6%,25.3±21.5%,p <0.001)。然而,只有重度OSA患者的Rx指數嚴重程度顯著增加(13.8±15.5,P <0.0001)。
結論:基於ECG和三軸加速度計的Patch穿戴式裝置可作為OSA患者臨床評估的工具。此外,當我們將實驗室中PSG的結果與穿戴式裝置進行的家庭睡眠測試進行比較時,我們發現仰臥位百分比升高與OSA嚴重程度升高之間存在關聯性。這項研究表明,位置變化的可能性可能與環境因素(包括身體上大量附著的導線)引起的OSA嚴重程度升高相關,尤其是在患有嚴重OSA的患者中特別顯著。
英文摘要 Purpose: Obstructive sleep apnea (OSA) is one of the most critical public health issues in modern society until now the polysomnography (PSG) is the golden standard for assessing the severity of OSA. However, the real-world situations of OSA patients’ sleep at home and the sleep during in-lab PSG could be very different due to the environmental factors, and the direction and magnitude of bias are unclear.
Materials and Methods: One hundred and twenty-five patients, referring to the sleep center of Shin-Kong Wu Ho-Su Memorial Hospital and Shuang Ho Hospital were included in this study. Wearable device RootiRx, which includes ECG and accelerometer signals, was applied simultaneously in the PSG conducted night and the following two to four days. The bio-signals detected from the RootiRx were defined as cyclic variation of heart rate index(CVHRI), chest effort index (CEI), and the combination of CVHRI and CEI into Rx index. Patients were divided into three groups by two criteria, one is grouped according to supine percentage during home sleep (low, medium, and high supine percentage), and the second is the severity which accessed by in-lab PSG (AHI<15, 15 to <30, ≥30). The study had been divided into two steps. First, we validated the accuracy of a wearable device for detecting the severity of OSA. Second, we compared the testing results of OSA patients from in-lab PSG and home-based wearable devices.
Results: In the first step of this study, the validation of wearable device revealed a significant correlation with the in-lab PSG on the severity of OSA, including all the three bio-signals (CVHRI, r = 0.7433; CEI, r = 0.7017; Rx index, r = 0.7826; all P < 0.0001). In the second step of this study, patients were grouped by sleeping in low, medium, and high supine percentage at home. They were observed to have a significantly increased percentage of supine position during the in-lab PSG (28.4±22.1%, 24.2±22.2%, 11.9±18.1%, respectively). Besides, the OSA severities were also significantly increased in these three groups when they were assessed in hospital (10.4±14.3, 5.6±12.4, 6.2±14.3, and p<0.0001, P = 0.0054, P = 0.0084 respectively). In a clinical aspect, patients with severe OSA (AHI≥30) detected by in-lab PSG showed lower supine percentage at home (59.65±20.64%, 56.54±20.09%, 47.53±23.85%, AHI<15, 15 to <30, ≥30, respectively). There were significant increases of supine percentage no matter the OSA severity when they were assessed by in-lab PSG compared with the home test (14.6±22.8%, 21.8±20.6%, 25.3±21.5%, p<0.001). However, only the patients with severe OSA had a significant increase of severity by the Rx index (13.8±15.5, P<0.0001).
Conclusion: The Patch-type wearable device with an ECG-based and tri-axial accelerometer could be an appropriate tool for clinical evaluation of patients with OSA. Besides, we identified a novel association between the elevated supine percentage and the increased severity of OSA, when we compared the results from in-lab PSG with home sleep test by the wearable device. It is the first study suggesting that the possibilities of positional change could relate to the increased OSA severity by environmental factors, including plenty of attached wires on the body, especially in the patients with severe OSA. The results showed the importance of considering the alternative, reduced interfering options for evaluating the severity of OSA.
論文目次 Table of Contents
Application of Wearable Devices in Patients with Obstructive Sleep Apnea i
摘要 iv
ABSTRACT vi
誌謝 viii
Table of Contents x
List of Tables xiii
List of Figures xiv
Chapter 1 Introduction 1
I. Research Background: Evaluating Sleep Apnea Syndrome 1
A. High prevalence of sleep disorders: 1
B. Sleep apnea is an important issue of sleep disorders: 2
C. Obstructive sleep apnea leading to severe health problems 2
D. Difficulties to reach an appropriate evaluation of obstructive sleep apnea 3
II. Research Motivation and Purpose of Wearable Devices Applied in Obstructive Sleep Apnea 4
A. Clinical application of wearable devices in obstructive sleep apnea 6
B. Patch-type wearable device for evaluation of obstructive sleep apnea 7
C. Preliminary results of study from the patch-type wearable device for evaluation of sleep related breathing events 8
D. Research Aim and Purpose of Wearable Devices Applied in Obstructive Sleep Apneas 14
Chapter 2 Literature Review 16
I. Problems about Obstructive Sleep Apnea: 16
A. High prevalence of sleep disorders: 16
B. Sleep apnea is an important issue of sleep disorders: 17
C. Obstructive sleep apnea leading to severe health problems: 18
1. Pathophysiology of obstructive sleep apnea in adults 18
2. Clinical features of obstructive sleep apnea: 20
3. Complications of obstructive sleep apnea: 22
II. Diagnostic Approaches to Obstructive Sleep Apnea: 25
A. Diagnostic evaluation: 25
1. Patient selection: 25
2. Evaluation tool parameters (Appendix I: Screening questionnaires for obstructive sleep apnea): 26
B. In-lab polysomnography (PSG): 28
1. Measured variables by polysomnography: 29
2. Identification and scoring of events by polysomnography: 32
3. Measures of sleep quality: 34
4. Measures severity of sleep-related breathing disorder: 34
5. Presentation of summary data and interpretation: 36
C. Home sleep apnea testing: 39
1. Type 3 devices: 41
2. Type 4 devices: 43
3. Alternative classification: SCOPER system 43
D. Selecting home or in-laboratory testing: 44
III. Application of Wearable Devices in Patients with Obstructive Sleep Apnea: 45
A. Tri-axial accelerometer (actigraphy) for motion detection: 45
B. Photoplethysmography (PPG) in wearable device: 46
C. Cyclic variation of heart rate in obstructive sleep apnea: 47
D. Clinical application of wearable device for sleep monitoring: 48
Chapter 3 Analytical Framework and Methods 49
I. Hypotheses, Framework and Design of this study 49
A. Hypotheses of this study: 49
B. Framework and design of this study (Figure 3.1, 3.2): 50
II. Subjects and study protocol: 53
E. Polysomnography (PSG): 54
F. RootiCare System: 55
G. Cyclic variation of heart rate, CVHR (Figure 3.6): 56
H. Chest Effort and Sleep Apnea: 59
I. Combination of CVHRI and CEI (Figure 3.11): 62
J. Analysis Report of RootiCare System (Figure 3.12): 63
III. Statistical analysis: 64
Chapter 4 Results 65
I. Predictive Performance of Patch-type Wearable Device for Patients with OSA 65
A. Characteristics of the Study Population 65
B. Associations Between CVHR, CEI, Rx Index and Severity of OSA 67
C. Detection Performance of CVHR, CEI and Rx Index 69
D. Clinical Applications of CVHR, CEI and Rx Index 73
II. Overestimation of OSA severity by in-lab polysomnography compared with home-based wearable device 75
A. Grouping by the Percentage of Supine Position during Sleep at home 78
B. Grouping by the OSA Severity Measured by in-Lab Polysomnography 81
Chapter 5 Discussion 84
I. Bio-signals as the Surrogates of Physiological Biomarkers for Sleep Monitoring from Wearable Devices: 84
A. Electrocardiography (ECG) derived Bio-signal, cyclic variation of heart rate (CVHR) as the surrogate of respiratory events: 84
B. Tri-axial accelerometer derived Bio-signal, chest effort as the surrogate of respiratory events: 88
II. The Advantages of Combining Different Bio-signals for Assessing the OSA Severity: 90
III. The Clinical Interpretation of Signals and Information from the Wearable Device: 91
IV. The Strengths of Patch-type Wearable Device for Evaluation of OSA Patients: 94
V. Overestimation of OSA Severity by in-Lab Polysomnography Compared with the Home-based Wearable Device Measurement: 96
VI. Limitation of this Study: 102
Chapter 6 Dissertation Conclusion 105
Appendix A 108
I. Screening Questionnaires for Obstructive Sleep Apnea: 108
A. STOP-Bang: 108
B. Sleep apnea clinical score (SACS): 111
C. Berlin questionnaire: 112
D. The NoSAS score: 115
E. Multivariable Apnea Prediction (MVAP) instrument: 115
II. Sleep efficiency ≥80%, 102 subjects for analysis: 116
III. Sleep efficiency ≥90%, 45 subjects for analysis: 119
IV. Correlation between OSA severity and Sleep efficiency 120
References 121
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