跳到主要內容

臺灣博碩士論文加值系統

(216.73.216.136) 您好!臺灣時間:2025/09/21 07:05
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:李旭恩
研究生(外文):Lee, Hsu-En
論文名稱:居家心肺復健監測之行動式照護平台
論文名稱(外文):Development of Home-based Mobile Cardio-Pulmonary Rehabilitation Consultant System
指導教授:黃國源黃國源引用關係
指導教授(外文):Huang, Kou-Yuan
學位類別:碩士
校院名稱:國立交通大學
系所名稱:多媒體工程研究所
學門:電算機學門
學類:軟體發展學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:59
中文關鍵詞:居家復健心電圖心率推導的呼吸呼吸訊號專家系統動態心電圖
外文關鍵詞:Home-based rehabilitationECGHeart rateDerived respirationRespiratory signalExpert systemECG Holter
相關次數:
  • 被引用被引用:0
  • 點閱點閱:447
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
根據統計,心血管疾病是全球近年來造成死亡的主要原因,而對於已罹患心臟病的病人,包括心血管疾病病人、接受心臟手術的術後病人、甚至心臟移植的病人,接受心臟復健運動訓練後對於疾病症狀、生活品質、心肺功能都能有正面的效果。然而,心臟病患實際參與心臟復健運動訓練的比率卻低於三分之一,主要原因是提供復健服務的醫療機構不足、或距離醫院太遠、或時間無法配合。居家復健被視為是一個可行的替代方案,而居家做心肺復健運動訓練,最大的問題在於無法正確評估運動訓練強度、訓練量的控制和維持、運動訓練時安全的控制以及病人遵從醫囑性和配合度的維持。
在本篇論文中,提出一套可移動居家心肺&;#63846;健監測系統,利用心電訊號擷取裝置透過藍芽傳輸到手機,做即時的分析及監看,除了心跳頻率以外,加入了復健訓練中重要的呼吸頻率的分析,這些資訊也透過實際在醫院進行的實驗,與醫院儀器做相關係數的比對,結果心跳頻率高達98%,呼吸頻率82%。而考量到居家訓練時的安全控制,此系統更加入了心律不整、心跳過快、心跳過慢等的心臟疾病偵測,並整合後端遠距醫療保健平台,達到完整並有效的居家心肺復健監測系統。

Cardiovascular diseases are the most popular causes of death in the world recently. It is well know that doing the cardiac rehabilitation program treatment has positive effects on disease symptoms, quality of life, cardiopulmonary capability and reduction of mortality for the heart disease patients, such as cardiovascular patients, postoperative patients of cardiac surgery, heart failure patients and even the patients who received heart transplant. However, only a small part of heart diseases patients attends and does the exercise regularly. The main reasons of low participation rate are insufficient of medical institutions with rehabilitation treatment service, time constraints and distance from rehabilitation treatment centers. Therefore, a novel cardiac rehabilitation program is needed, especially for the home-based applications.
In this study, home-based cardiac rehabilitation programs are designed as a feasible alternative to avoid various barriers related to care centre based programs. Indeed, there are still have drawbacks of current home-based cardiac rehabilitation program models such as, patients are unable to estimate exercise strength accurately, control and maintain the volume of training, safety control of exercise training, and preserve patients’ cooperation and make them follow doctor’s advice.
The proposed system is built on mobile phone and receiving electrocardiograph (ECG) signal via a wireless ECG holter. Apart from heart rate (HR) monitor, an ECG derived Respiration (EDR) technique is also included to provide respiration rate (RR) which are the most important parameters during exercise. Clinical test of 16 subjects affording Bruce Task (treadmill), correlation between this system and commercial product (Custo-Med) is up to 98% in HR and 82% in RR. The prevention of sudden heart attack, an arrhythmia detection expert system and healthcare server at the backend are also integrated to this system for comprehensive cardio-pulmonary monitoring whenever and wherever doing the exercise. In word, the proposed system is reliable for the home-based cardiac rehabilitation.

摘 要 i
ABSTRACT ii
誌 謝 iv
Table of Contents v
List of Tables vii
List of Figures viii
Chpater 1 Introduction 1
1.1 Cardiovascular diseases 1
1.2 Cardiac Rehabilitation program 2
1.3 Motivation and Goals 3
Chpater 2 Background overview 6
2.1 Electrocardiogram 6
2.2 Common ECG holter monitor 11
Chpater 3 System architecture 13
3.1 System overview 13
3.2 Personal mobility system for monitoring ECG, EDR and heart disease symptoms 15
3.2.1 Wireless ECG holter 16
3.2.2 QRS wave detection algorithm 18
3.2.3 ECG derived respiration algorithm 20
3.2.4 Heart diseases expert algorithm 24
3.2.5 Design of the software for ECG monitoring on mobile phone 26
3.3 Healthcare server 36
Chpater 4 System evaluation from benchmark database and software 39
4.1 Testing the QRS wave detection algorithm and heart diseases expert algorithm by MIT Arrhythmia Database 39
4.2 Testing the noise tolerance by MIT Noise Stress Test Database 43
4.3 Testing the EDR algorithm by MGH/MF Database 44
4.4 Testing mobile power consumption by PowerTutor 45
4.4.1 The Power consumption of monitoring stress ECG, HR and RR 46
Chpater 5 Experimental evaluations 48
5.1 Evaluation of the developed QRS wave and EDR algorithms 48
5.1.1 Experiment progress 48
5.1.2 Testing results 50
5.2 Evaluation of the developed heart diseases expert algorithms 52
5.2.1 Data acquisition 52
5.2.2 Testing results 52
Chpater 6 Discussions 54
Chpater 7 Conclusions and Future works 56
Reference 57

1. Cardiovascular diseases (CVDs). 2011; Available from: http://www.who.int/mediacentre/factsheets/fs317/en/index.html.
2. Department of Health, E.Y., R.O.C. (Taiwan). Health Statistics in Taiwan. 2009; Available from: http://www.doh.gov.tw/.
3. Yusuf, S., et al., Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. The Lancet, 2004. 364(9438): p. 937-952.
4. Balady, G.J., et al., Core components of cardiac rehabilitation/secondary prevention programs: 2007 update. A scientific statement from the American Heart Association Exercise, Cardiac Rehabilitation, and Prevention Committee, the Council on Clinical Cardiology; the Councils on Cardiovascular Nursing, Epidemiology and Prevention, and Nutrition, Physical Activity, and Metabolism; and the American Association of Cardiovascular and Pulmonary Rehabilitation. Circulation, 2007: p. CIRCULATIONAHA. 106.180945.
5. Sethi, P.S., et al., A comprehensive cardiac rehabilitation program in post-CABG patients: a rationale and critical pathway. Critical Pathways in Cardiology, 2003. 2(1): p. 20.
6. Hansen, D., et al., Continuous low-to moderate-intensity exercise training is as effective as moderate-to high-intensity exercise training at lowering blood HbA 1c in obese type 2 diabetes patients. Diabetologia, 2009. 52(9): p. 1789-1797.
7. Taylor, R.S., et al., Exercise-based rehabilitation for patients with coronary heart disease: systematic review and meta-analysis of randomized controlled trials* 1. The American journal of medicine, 2004. 116(10): p. 682-692.
8. Scott, I.A., K.A. Lindsay, and H.E. Harden, Utilisation of outpatient cardiac rehabilitation in Queensland. Medical journal of Australia, 2003. 179(7): p. 341-345.
9. Cooper, A., et al., Factors associated with cardiac rehabilitation attendance: a systematic review of the literature. Clinical Rehabilitation, 2002. 16(5): p. 541.
10. Taylor, R., et al., Home-based cardiac rehabilitation versus hospital-based rehabilitation: a cost effectiveness analysis. International journal of cardiology, 2007. 119(2): p. 196-201.
11. Mattila, J., et al. Mobile tools for home-based cardiac rehabilitation based on heart rate and movement activity analysis. 2009: IEEE.
12. Walters, D.L., et al., A mobile phone-based care model for outpatient cardiac rehabilitation: the care assessment platform(CAP). BMC Cardiovascular Disorders, 2010. 10(1): p. 5.
13. Bidargaddi, N. and A. Sarela, Activity and heart rate-based measures for outpatient cardiac rehabilitation. Methods of Information in Medicine, 2008. 47(3): p. 208-216.
14. Cardiology teaching Package, Chest Leads.; Available from: http://www.virtualmedicalcentre.com.
15. Introduction of ECG a Basic Idea about ECG with general description of 12 Leads. Available from: http://health.medicscientist.com/2011/02/ecg-general-description-with-basic-idea-of-12-leads-with-positions-on-body.html.
16. Lin, C.T., et al., An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation. Information Technology in Biomedicine, IEEE Transactions on, 2010. 14(3): p. 726-733.
17. Afonso, V.X., et al., ECG beat detection using filter banks. Biomedical Engineering, IEEE Transactions on, 1999. 46(2): p. 192-202.
18. Chen, S.W., H.C. Chen, and H.L. Chan, A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising. Computer methods and programs in biomedicine, 2006. 82(3): p. 187-195.
19. Hamilton, P.S. and W.J. Tompkins, Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. Biomedical Engineering, IEEE Transactions on, 1986(12): p. 1157-1165.
20. Pan, J. and W.J. Tompkins, A real-time QRS detection algorithm. Biomedical Engineering, IEEE Transactions on, 1985(3): p. 230-236.
21. Poli, R., S. Cagnoni, and G. Valli, Genetic design of optimum linear and nonlinear QRS detectors. Biomedical Engineering, IEEE Transactions on, 1995. 42(11): p. 1137-1141.
22. Ruha, A., S. Sallinen, and S. Nissila, A real-time microprocessor QRS detector system with a 1-ms timing accuracy for the measurement of ambulatory HRV. Biomedical Engineering, IEEE Transactions on, 1997. 44(3): p. 159-167.
23. Xue, Q., Y.H. Hu, and W.J. Tompkins, Neural-network-based adaptive matched filtering for QRS detection. Biomedical Engineering, IEEE Transactions on, 1992. 39(4): p. 317-329.
24. Friesen, G.M., et al., A comparison of the noise sensitivity of nine QRS detection algorithms. Biomedical Engineering, IEEE Transactions on, 1990. 37(1): p. 85-98.
25. Behbehani, K., et al. An investigation of the mean electrical axis angle and respiration during sleep. 2002: IEEE.
26. Lipsitz, L.A., et al., Heart rate and respiratory rhythm dynamics on ascent to high altitude. British heart journal, 1995. 74(4): p. 390.
27. Moody, G.B., et al., Clinical validation of the ECG-derived respiration (EDR) technique. Group. 1: p. 3.
28. Moody, G.B., et al., Derivation of respiratory signals from multi-lead ECGs. Computers in Cardiology, 1985. 12: p. 113-116.
29. Nazeran, H., et al. Reconstruction of respiratory patterns from electrocardiographic signals. 1998: IEEE.
30. Dingab, S., et al., Derivation of respiratory signal from single-channel ECGs based on Source Statistics.
31. Cichocki, A. and S. Amari, Blind Signal and Image Processing. 2002: Wiley Online Library.
32. Cardoso, J.F. Multidimensional independent component analysis. 1998: IEEE.
33. Important state paths of an Activity. Available from: http://developer.android.com/.
34. Goldberger, A.L., et al., PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 2000. 101(23): p. e215.
35. Moody, G.B. and R.G. Mark. The MIT-BIH arrhythmia database on CD-ROM and software for use with it. 1990: IEEE.
36. Moody, G.B., W. Muldrow, and R.G. Mark, A noise stress test for arrhythmia detectors. Computers in Cardiology, 1984. 11(3): p. 381-384.
37. Zhang, L., et al. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. 2010: ACM.

連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top