臺灣博碩士論文加值系統

中文摘要
本研究目的在於設計偵測睡眠呼吸異常檢測技術，結合生理量測智慧衣發展出一套可長時間監控使用者睡眠時呼吸是否異常之系統。在擷取呼吸訊號中
，為了讓使用者能在舒適的狀態下進行，我們使用生理量測智慧衣量測，並且運用多位學者長期研究探討並具有一定準確度與長時間發展的心源性呼吸訊號來做睡眠呼吸異常分析。本系統主要以 LabVIEW® 程式為分析端，擷取端則使用取樣頻率250 Hz心電圖訊號的生理量測智慧衣來做訊號擷取，並且經由電腦做連結達到同步擷取訊號與即時分析檢測功能。在檢測部分，我們為了確認演算法的準確度，則採用短時間的呼吸控制，諸如：吸二吐二、吸三吐二、長時間閉氣、短時間閉氣、長時間弱呼吸、短時間弱呼吸等特定呼吸型態取樣，做為即時檢測分析以測試準確度。經由研究測試以及數據分析後，本系統判斷呼吸正常狀態上具有90%以上的準確度，而判斷呼吸異常狀態亦有90%以上的準確度，該分析數據證實本研究系統對於呼吸狀態判斷上具有很好的準確度，並且若呼吸異常現象超過十秒時，該系統則會啟動警示系統以便通知照護者前來觀看。而未來上可運用其資訊與呼吸器通氣功能結合，以達到更符合人體需求且高效能的即時動態感知呼吸器運作。

關鍵字: 心源性呼吸訊號、心電圖、呼吸異常、智慧衣

Abstract
The main purpose of this research is to develop a real-time abnormal breathing anomaly system, integrated with the physiological measurements smart clothing for monitoring abnormal breathing during sleep. In order to reduce the abnormal phenomena, physiological measurements will instead be done by smart-shirt measurements, and uses electrocardiograph derived respiratory signal from previous studies in order to achieve the same functionality with breathing bands. This system will be developed using LabVIEW® for analysis, while the signals will be measured and acquired using physiological cardiac signals smart-shirt with a sampling frequency 250 Hz, and linked by Bluetooth for real-time analysis and detection. In order to prove the accuracy of the system for the analysis of abnormal breathing detection, short-term controlled breathing data will be used, such as inhaling 2 seconds (s) exhaling 2s, inhaling 3s exhaling 2s, long breath holding, short breath holding, long weak breathing, short weak breathing, and other specific breathing pattern sampling, for instantaneous detection and analysis in order to achieve the accuracy measurement. After testing, the system has an accuracy of 90% when detecting normal breathing state and 90% when detecting abnormal breathing state. The results confirm the breathing state determining system has a good accuracy, and if more than ten seconds of abnormal breathing is detected, the system will alert caregivers. In the future, the data can also be combined with a respirator to achieve an ergonomic and efficient respirator.

Keywords: ECG-derived respiratory signal, ECG, abnormal breathing,
smart clothing.
 

目錄
致謝 i
中文摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 緒論 1
1.1 前言 1
1.2 研究目的 1
1.3 文獻回顧 2
第二章 研究背景 4
2.1 睡眠呼吸中止症 4
2.1.1 何謂睡眠呼吸中止症 4
2.1.2 檢測方法 4
2.1.3 睡眠呼吸中止症類別 5
2.2 心電圖原理與量測 7
2.3 心電圖波形 8
2.4 心源性呼吸訊號 (ECG-derived respiratory, EDR) 10
2.4.1 心源性呼吸訊號原理及介紹 10
2.4.2 心源性呼吸訊號擷取方式 10
2.5 生理量測智慧衣 11
第三章 研究方法與材料 13
3.1 系統架構 13
3.2 ECG演算法 14
3.2.1 前置處理 14
3.2.2 檢測R點 16
3.3 擷取心源性呼吸訊號 17
3.3.1 R點正規化 18
3.3.2 分析心源性呼吸訊號 18
3.4 呼吸異常狀態分析 22
3.5 即時分析之呼吸停止警示系統 28
3.6 離線分析之呼吸停止分析 29
3.7 實驗流程 30
第四章 實驗結果與討論 32
4.1 改進EDR結果與顯示 32
4.2 呼吸狀態結果與顯示 33
4.3 系統介面 37
4.3.1 主程式系統介面架構 37
4.3.2 即時分析系統介面架構 37
4.3.3 離線分析系統介面架構 39
4.4 數據比對 41
第五章 結論與未來展望 47
5.1 結論 47
5.2 未來展望 48
參考文獻 49

參考文獻
[1]楊美貞, 馬順德“談阻塞型睡眠呼吸中止症-好眠不止息”人醫心傳, 第六十八期, pp.56-67, 2009年8月。
[2] S.M. Caples, T. Kara, V.K. Somers “Cardiopulmonary consequences of obstructive sleep apnea,” Seminars in Respiratory and Critical Care Medicine ,vol.26, pp.25-32 ; February 2005.
[3] T. Young, P. Peppard, D. Gottlieb “Epidemiology of obstructive sleep apnea.: a population health perspective,” American Journal of Respiratory and Critical Care Medicine, vol.165, pp.1217-1239, May 2002.
[4] A. Rechtschaffen, A. Kales “A manual of standardized terminology,techniques and scoring system of sleep stages in human subjects,” Washington DC: US Government Printing Office, US Public Health Service, 1968.
[5] D. Moser, P. Anderer, G.Gruber, S.Parapatics, E.Loretz, M.Boeck, G.Kloesch,
E. Heller, A.Schmidt, H.Danker-Hopfe , B.Saletu, J.Zeitlhofer, G.Dorffner “Sleep Classification According to AASM and Rechtschaffen &; Kales: Effects on Sleep Scoring Parameters,” Sleep, vol. 32, pp.139-149, February 2009.
[6] Polysomnography -Wikipedia, the free encyclopedia：http://en.wikipedia.org/wiki/Polysomnography
[7] S. B. Park, Y. S. Noh, S. J. Park, H. R. Yoon “An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation,” Medical and Biological Engineering and Computing, vol.46, pp.147–15. February 2008.
[8] K.S.Tan, R.Saatchi, H,Elphick, D.Burke “Real-time vision based respiration monitoring system,” 2010 7th International Symposium Communication Systems Networks and Digital Signal Processing (CSNDSP), pp.770-774, July 2010.
[9] S.D. Min, J.K. Kim, H.S. Shin, Y.H.Yun, C.K. Lee, J.H. Lee “Noncontact Respiration Rate Measurement System Using an Ultrasonic Proximity Sensor,” IEEE SENSORS JOURNAL, vol.11, pp.1732-1739, November 2010.
[10] J. Lazaro, A. Alcaine, E. Gil, P. Laguna, R. Bailon “Electrocardiogram Derived Respiration from QRS Slopes,” 35th Annual International Conference of the IEEE EMBS Osaka in Japan, pp.3913-3916, July 2013.
[11] D.Widjaja, J.Taelman, S. Vandeput, M.A.K.A. Braeken, R.A. Otte, B.R.H.Van den Bergh, S.Van Huffel “ECG-derived respiration : Comparison and new measures for respiratory variability,” Computing in Cardiology, pp. 149-152. September 2010.
[12] J. Boyle , N.Bidargaddi , A.Sarela , M.Karunanithi “Automatic Detection of Respiration Rate From Ambulatory Single-Lead ECG,” IEEE Transactions on Information Technology in Biomedicine ,vol.13 , pp. 890 - 896, November 2009.
[13] A.E Santo, C.Carbajal “Respiration rate extraction from ECG signal viadiscrete wavelet transform,” Circuits and Systems for Medical and Environmental Applications Workshop (CASME) 2010 2nd, pp.1- 4, December 2010.
[14] A.Yadollahi, Z.Moussavi “Acoustic Obstructive sleep apnea detection,” 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, pp.7110-7113, September 2009.
[15] F. Liao, F. Wang, P. Zhou “Deriving respiratory signals from ECG by filtering method,” 2011 3rd International Conference in Computer Research and Development (ICCRD), Shanghai, vol.4, pp.456-459, March 2011.
[16] P. de Chazal, R. Reilly, C. Heneghan “Automatic sleep apnoea detection using measures of amplitude and heart rate variability from the electrocardiogram,” 16th International Conference on Pattern Recognition, vol.1, pp,775-778,
August 2002.
[17] G.D. Furman, Z. Shinar, A. Baharav, S. Akselrod “Electrocardiogram derived respiration during sleep,” Computers in Cardiology, pp.351-354 , September 2005.
[18] L. S. Correa, E. Laciar, V. Mut, A. Torres, R. Jane “Sleep Apnea Detection based on Spectral Analysis of Three ECG - Derived Respiratory Signals” 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA,
pp. 4723-4726, September 2009.
[19] C. Avci, I. Delibasoglu, A. Akbas “Sleep Apnea Detection Using Wavelet Analysis of ECG Derived Respiratory Signal” 2012 International Conference on Biomedical Engineering (ICoBE), pp.272-275, February 2012.
[20] B. Mazzanti, C. Lamberti, J. de Bie “Validation of an ECG-Derived Respiration Monitoring Method” Computers in Cardiology, pp.613-616, September 2003.
[21] A. Qureshi, R.D. Ballard, H.S. Nelson “Obstructive sleep apnea,” Journal of Allergy and Clinical Immunology, vol.112, pp.643–651 , October 2003.
[22]楊家祥, 陳俊傑 “阻塞型睡眠呼吸中止症候群的診斷與治療”, 基層醫學, 第21卷, 第11期, pp.306-311, 2006年11月。
[23]嘉義長庚睡眠醫學中心-成人睡眠呼吸中止症：http://www1.cgmh.org.tw/sleepcenterjia/index.files/Page697.htm
[24] K.Virend, P. David, A. Raouf, T. William, F. Costa, A. Culebras, S. Daniels, S. John, E.Carl, J. Lyle, G.Thomas, R. Russell, M.Woo, T. Young “Sleep Apnea and Cardiovascular Disease,” Journal of the American College of Cardiology, No.8, vol.52, pp.686-717, August 2008.
[25]邱創乾, 藍振晏, 石天威, 謝靜玟 “未來服飾¬-智慧衣” 科學發展 470期 pp10-14, 2012年2月。
[26] H. H. So, K. L. Chan “Development of QRS detection method for real-time ambulatory cardiac monitor,“ Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, vol.1, pp.289-292, October.30-November 1997.