即時心電圖分析系統研製及消趨勢分析於心衰診斷之應用

簡易檢索 / 詳目顯示

回結果列表

研究生：	張彬 Chang, Ben
論文名稱：	即時心電圖分析系統研製及消趨勢分析於心衰診斷之應用 Realization of Real-Time Electrocardiogram Systems and Their Applications to Diagnosis of Patients with Congestive Heart Failure
指導教授：	鐘太郎 Jong, Tai-Lang
口試委員:
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2010
畢業學年度：	99
語文別：	英文
論文頁數：	122
中文關鍵詞：	心率變異、心臟衰竭、消趨勢分析
外文關鍵詞：	heart rate variability, congestive heart failure, detrended fluctuation analysis
相關次數：	點閱：3 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

摘要
充血性心衰竭(congestive heart failure, CHF)是各種心血管疾病的終末階段，主要特徵為心肌收縮力減弱，心排血量減少，因而不能滿足機體組織細胞代謝的需要，同時靜脈回流受阻，靜脈系統淤血，從而出現以組織血液灌注不足，以及肺循環和體循環淤血等一種複雜而嚴重的臨床綜合症狀，它具有高發病率和高病死率的二高特點，尤其是在狀況「輕」的情況下，心衰竭往往由於沒有共同承認的定義，以及難以診斷出來。即便是使用最好的治療，心衰竭的年死亡率依舊高達10%。而心衰竭亦是導致65歲以上的老年人入院的主要原因。近年心衰的發病率預料仍將繼續上升，有可能成為本世紀最嚴重的心血管病症，因此心臟系統疾病的正確診斷是當今醫學界面臨的最為迫切需解決的問題。本論文的研究重點包括心率變異(heart rate variability, HRV)的各式即時分析器(real-time analyzer, RTA)及其於CHF診斷之應用。主要研製無線式心電圖(electrocardiogram, ECG)量測系統、基於個人電腦(Personal computer, PC)的虛擬RTA，並將系統實際應用於CHF的診斷。本文首先說明ECG的研製，核心採用自製的低成本電路，來取代昂貴之儀器放大和通信晶片，大大改善傳統有線不便問題，並克服雜訊影響。其次，說明RTA的研製，通過虛擬架構，並修改PC之電路功能，研製出分別具有濾波、間隔期檢測、小波分析、和消趨勢分析(detrended fluctuation analysis, DFA)等功能的各式RTA。接著，採用DFA、近似熵及複雜度分析等三個特徵，給可適應性神經模糊推理系統診斷CHF。最後使用支持向量機(support vector machine, SVM)，探討DFA在接近一日觀察HRV中之應用，並總結出7PM ~9PM是診斷CHF的最佳時機。

Abstract

In this dissertation, the realization of real-time analyzer (RTA) for heart rate variability (HRV) analysis and their applications to congestive heart failure (CHF) are investigated. Implementations include wireless electrocardiogram (ECG) and personal computer (PC) based virtual RTAs, and their applications to CHF are studied. Firstly, we present the realization of ECG. Using low-cost components to replace expensive instrumentation and communication ICs in the circuits, we can not only reduce the system cost, but also resolve the cable inconvenience problems of the data acquisition. In addition, our result is good in noise immune. Next, through the virtual architecture, and modify the circuits of PC, we realized various RTAs capable of filtering, interval detecting, wavelet analyzing, and detrended fluctuation analysis (DFA), respectively. Then, use of DFA, approximate entropy and complexity measure as features of the adaptive neuro-fuzzy inference system (ANFIS) to CHF diagnosis is described. Finally, based on the support vector machine (SVM) we present the application of DFA during circadian observation, and conclude that 7PM~9AM is the best timing to diagnose CHF patient via the α1 scaling exponent of DFA.

List of Contents

Chinese Abstract …………………………………………………………………………....…I
Abstract……………………………………………………………………………..……...….II
Acknowledgements………………………..………………………………………………….III
List of Contents……………………………………………………………………………….IV
List of Figures…………………………………………………………………..………….…VI
List of Tables……………………………………………………………………………….…IX
Chapter 1 Introduction…………………………………………………………..……….…….1
1.1. Background……………………………………………………………………..……1
1.2. Contributions of the Dissertation……………………………….……………………5
1.3. Organization of the Dissertation……………………………………………..………5
Chapter 2 A novel wireless real time system suitable for measuring electrocardiogram………7
2.1. General idea………………………………………………………………………….7
2.2 Design ……………………………………………………………………………..…7
2.3 Testing...……………………………………...………………………….…..………12
2.4 Summary…………….………………………………………………………………15
Chapter 3 A real-time 3050MHz Celeron PC based digital filter for electrocardiogram data acquisition…………………………………………………………….……………16
3.1. Overture……………………………………………………………………….....…16
3.2. Filter specifications…………………………………………………………………18
3.3. Filter design…………………………………………………………………………18
3.5. Implementation……………………………………………………………………..25
3.6. Summary……………………………………………………………………………28
Chapter 4 Accurate R-R interval detection circuit incorporating multiple decay rates with adaptive threshold refractory and error count penalty………………….………….29
4.1. Outline………………………………………………………………………………29
4.2. Technique………………………………………...…………………………………29
4.3. Implementation and validation……………………………...………………………36
4.4. Summary………………………………………………………...………………….37
Chapter 5 A Real-time 3050MHz Celeron PC based heart beat analyzer using sound card……………………………………………………………...…………………38
5.1. Foreword……………………………………………………………………………38
5.2 Project assumptions…………………………………………………………………40
5.3. Realization………………………………………………………………………….41
5.4. Discussion…………………………………………………………………………..46
5.5 Summary…………………………………………………………………………….49
Chapter 6 Implementation of real-time instrument for wavelets analysis of heart rate variability signal………………………………………………………………..…51
6.1. Preface………………………………………………………………..…………51
6.2 Methodology………………………………………………………….…………52
6.3. Result and discussion………………………………………………….……..…57
6.4. Summary……………………………………………………………….…….…59
Chapter 7 Fractal scaling property measure of human heartbeat intervals in patients with congestive heart failure and for healthy Subjects………………………..…..…61
7.1. Nomenclature………………………………………………………….………..61
7.2. Preface………………………………………………………………….……….61
7.3. Theory, methods and subjects………………………………………….………..63
7.4. Results and discussion………………………………………………….…….…71
7.5. Summary………………………………………………………………………..75
Chapter 8 Fuzzy classification of patients with congestive heart failure………...…….…77
8.1. Overview……………………………………………………………………..…77
8.2. Theory, Methods and Subjects…………………………………….……………78
8.3. Results……………………………………………………………………..……86
8.4. Summary…………………………………………………………………..……89
Chapter 9 Optimal timing in screening patients with congestive heart failure and healthy subjects during circadian observation……………………………………….….90
9.1. Synopsis…………………………………………………………………………90
9.2. Material and methods……………………………………………………………91
9.3. Results…………………………………………………………………………...96
9.4. Discussion………………………………………………………………………111
9.4. Summary…………………………………………………………………..……113
Chapter 10 Conclusion……………………………………….…………………………...114
Reference………………………………………………………………………………....117

                                

Reference

1Allegra, J. R., D. G. Cochrane, and R. Biglow. Monthly, weekly, and daily patterns in the incidence of congestive heart failure. Acad. Emerg. Med. 8(6):682-685, 2001.
2Aschoff, J., S. Daan, and G. A. Groos. Vertebrate circadian systems structure and physiology. New York: Springer-Verlag, 1982.
3Bär, K. J., M. Koschke, S. Berger, S. Schulz, M. Tancer, A. Voss, and V. K. Yeragani. Influence of olanzapine on QT variability and complexity measures of heart rate in patients with schizophrenia. J. Clin. Psychopharmacol. 28(6):694-698, 2008.
4Barash, D., R. A. Silverman, P. Gennis, et al. Circadian variation in the frequency of myocardial infarction and death associated with acute pulmonary edema. J. Emerg. Med. 7:119-121, 1989.
5Beckers, F., B. Verheyden, K. Couckuyt, and A. E. Aubert. Fractal dimension in health and heart failure. Biomed. Tech. 51(4):194-197, 2006.
6Bernardi, L., L. Ricordi, P. Lazzari, et al. Impaired circadian modulation of sympathovagal activity in diabetes - a possible explanation for altered temporal onset of cardiovascular disease. Circulation 86:1443-1452, 1992.
7Bigger, J. T., L. F. Fleiss, R. C. Steinman, et al. RR variability in healthy, middle-age persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. Circulation 91:1936-1943. 1995.
8Blaber, A. P., R. L. Bondar, and R. Freeman. Coarse graining spectral analysis of HR and BP variability in patients with autonomic failure. Am. J. Physiol. 271(4):H1555-1564, 1996.
9Blesić, S., D. Stratimirović, S. Milosević, and M. Ljubisavljević. Detecting long-range correlations in time series of dorsal horn neuron discharges. Ann. N. Y. Acad. Sci. 1048:385-391, 2005.
10Butler, G. C., S. Ando, and J. S. Floras. Fractal component of variability of heart rate and systolic blood pressure in congestive heart failure. Clin. Sci. 92(6):543-550, 1997.
11Chang, S., M. C. Hsyu, H. Y. Cheng, S. H. Hsieh, and C. C. Lin. Synergic co-activation in forearm pronation. Ann. Biomed. Eng. 36(12):2002-2018, 2008.
12Chang, S., S. J. Li, M. J. Chiang, S. J. Hu, and M. C. Hsyu. Fractal dimension estimation via spectral distribution function and its application to physiological signals. IEEE Trans. Biomed. Eng. 54(10):1895-1898, 2007.
13Cohen, M. C., K. M. Rohtla, C. E. Lavery, et al. Meta-analysis of the morning excess of acute myocardial infarction and sudden cardiac death. Am. J. Cardiol. 79(11):1512, 1997.
14Echeverría, J. C., S. D. Aguilar, M. R. Ortiz, J. Alvarez-Ramirez, and R. González-Camarena. Comparison of RR-interval scaling exponents derived from long and short segments at different wake periods. Physiol. Meas. 27(4):N19-25, 2006.
15Elliott, W.J. Circadian variation in the timing of stroke onset: A meta-analysis. Stroke 29: 992-996, 1998.
16Fano, U. Ionization Yield of Radiations. II. The Fluctuations of the Number of Ions. Physic- al Review 72:26-26, 1947.
17Fava S., and J. Azzopardi. Circadian variation in the onset of acute pulmonary edema and associated acute myocardial infarction in diabetic and nondiabetic patients. Am. J. Cardiol. 80:336–338, 1997.
18Fava S., J. Azzopardi, H. A. Muscat, et al. Absence of circadian variation in the onset of acute myocardial infarction in diabetic subjects. Br. Heart J. 74:370-372, 1995.
19Feirwals, B. J., and L. E. Toothake. Empirical comparison of ANOVA F-test, normal scores test and Kruskal-Wallis test under violation of assumptions. Edu. Psycho. Meas. 34(4):789-799, 1974.
20Freitas, J., and F. Rocha-Goncalves. Circadian heart rate variability and blood pressure pattern in severe autonomic failure. J. Hyperten. 22:S22-S22, 2004.
21Hu, K., F. A. Scheer, R. M. Buijs, and S. A. Shea. The endogenous circadian pacemaker imparts a scale-invariant pattern of heart rate fluctuations across time scales spanning minutes to 24 hours. J. Biol. Rhythms. 23(3):265-273, 2008.
22Hurst HE. Long term storage capacity of reservoirs, Transactions of the American Society of Civil Engineers. 116:770-799, 1951.
23Ivanov, P. C., L. A. Amaral, A. L. Goldberger, S. Havlin, M. G. Rosenblum, Z. R. Struzik, and H. E. Stanley. Multifractality in human heartbeat dynamics. Nature 399(6735):461-465, 1999.
24Krumholz, H. M., Y. T. Chen, Y. Wang, V. Vaccarino, M. J. Radford, and R. I. Horwitz. Predictors of readmission among elderly survivors of admission with heart failure. Am. Heart J. 139:72-77, 2000.
25Levy R. D., D. Cunningham, L. M. Shapiro, et al. Diurnal variation in left-ventricular function - a study of patients with myocardial- ischemia, syndrome X, and of normal controls. Br. Heart J. 57(2):148-153, 1987.
26Moody, G. B., R. G. Mark, and A. L. Goldberger. PhysioNet: a Web-based resource for the study of physiologic signals. IEEE Eng. Med. Biol. Mag. 20(3):70-75, 2001.
27Mudd, J. O., and D. A. Kass. Tackling heart failure in the twenty-first century. Nature 451(7181):919-928, 2008.
28Nakamura, Y., Y. Yamamoto, and I. Muraoka. Autonomic control of heart rate during physical exercise and fractal dimension of heart rate variability. J. Appl. Physiol. 74(2):875-881, 1993.
29Neubauer, S. The failing heart: an engine out of fuel. N. Engl. J. Med. 356(11):1140-1151, 2007.
30Scanaill, C. N., S. Carew, P. Barralon, N. Noury, D. Lyons, and G. M. Lyons. A review of approaches to mobility telemonitoring of the elderly in their living environment. Ann. Biomed. Eng. 34(4):547-563, 2006.
31Otsuka, K., G. Cornélissen, and F. Halberg. Circadian rhythmic fractal scaling of heart rate variability in health and coronary artery disease. Clin. Cardiol. 20(7):631-638, 1997.
32Panina, G., U. N. Khot, E. Nunziata, R. J. Cody, and P. F. Binkley. Assessment of autonomic tone over a 24-hour period in patients with congestive heart failure: relation between mean heart rate and measures of heart rate variability. Am. Heart J. 129(4):748-753, 1995.
33Peng, C. K., J. Mietus, J. M. Hausdorff, S. Havlin, H. E. Stanley, and A. L. Goldberger. Long-range anticorrelations and non-Gaussian behavior of the heartbeat. Phys. Rev. Lett. 70(9):1343-1346, 1993.
34Peng, C. K., S. Havlin, H. E. Stanley, and A. L. Goldberger. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5(1):82-87, 1995.
35Peng, C. K., S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, and A. L. Goldberger. Mosaic organization of DNA nucleotides. Phys. Rev. E. 49(2):1685-1689, 1994.
36Perkiömäki, J. S., T. H. Mäkikallio, and H. V. Huikuri. Fractal and complexity measures of heart rate variability. Clin. Exp. Hypertens. 27(2):149-158, 2005.
37Poon, C. S., C. K. Merrill. Decrease of cardiac chaos in congestive heart failure. Nature 389(6650):492-495, 1997.
38Pursiainen, V., T. H. Haapaniemi, J. T. Korpelainen, H. V. Huikuri, K. A. Sotaniemi, and V. V. Myllylä. Circadian heart rate variability in Parkinson's disease. J. Neurol. 249(11):1535-1540, 2002.
39Ridha, M., T. H. Mäkikallio, G. Lopera, J. Pastor, E. de Marchena, S. Chakko, H. V. Huikuri, A. Castellanos, and R. J. Myerburg. Effects of carvedilol on heart rate dynamics in patients with congestive heart failure. Ann. Noninvasive Electrocardiol. 7(2):133-138, 2002.
40Rosamond, W., K. Flegal, G. Friday, K. Furie, A. Go, K. Greenlund, N. Haase, M. Ho, V. Howard, B. Kissela, S. Kittner, D. Lloyd-Jones, M. McDermott, J. Meigs, C. Moy, G. Nichol, C. J. O'Donnell, V. Roger, J. Rumsfeld, P. Sorlie, J. Steinberger, T. Thom, S. Wasserthiel-Smoller, and Y. Hong. American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 115(5):e69-e171, 2007.
41Telesca, L., G. Colangelo, V. Lapenna, and M. Macchiato. Fluctuation dynamics in geoelectrical data: an investigation by using multifractal detrended fluctuation analysis, Phys. Lett. A. 332:398-404, 2004.
42Turcott, R. G., M. C. Teich. Fractal character of the electrocardiogram: Distinguishing heart-failure and normal patients. Ann. Biomed. Eng. 24(2):269-293, 1996.
43Van Leeuwen, P., H. Bettermann, U. An der Heiden, and H. C. Kümmell. Circadian aspects of apparent correlation dimension in human heart rate dynamics. Am. J. Physiol. 269(1):H130-134, 1995.
44Vapnik, V., and A. Lerner, Pattern recognition using generalized portrait method. Automat. Remote Contr, 24:774-780, 1963.
45Vapnik. V., A. Chervonenkis, Theory of pattern recognition: Statistical problems of learning, Moscow: Nauka, 1974.
46Yamamoto, Y., J. O. Fortrat, and R. L. Hughson. On the fractal nature of heart rate variability in humans: effects of respiratory sinus arrhythmia. Am. J. Physiol. 269(2):480-486, 1995.
47Yamamoto, Y., Y. Nakamura, H. Sato, M. Yamamoto, K. Kato, and R. L. Hughson. On the fractal nature of heart rate variability in humans: effects of vagal blockade. Am. J. Physiol. 269(4):R830-837, 1995.
48Yeragani, V. K., K. Srinivasan, S. Vempati, R. Pohl, and R. Balon. Fractal dimension of heart rate time series: an effective measure of autonomic function. J. Appl. Physiol. 75(6):2429-2438, 1993.
49Packer M. The neurohormonal hypothesis: A theory to explain the mechanism of the disease progression in heart failure. J Am Coll Cardiol 20:248-254, 1992.
50Binkley PF, Nunziata E, Haas GJ, Nelson SD, Cody RJ. Parasympathetic withdrawal is an integral component of autonomic imbalance in congestive heart failure: Demonstration in human subjects and verification in a paced canine model of ventricular failure. J Am Coll Cardiol 18:464-472, 1991.
51Juha-Pekka Niskanen, Mika P. Tarvainen, Perttu O. Ranta-aho, Pasi A. Karjalainen. Software for advanced HRV analysis. Computer Methods and Programs in Biomedicine 76:73-81, 2004.
52Huikuri, H. V., Linnaluoto, M. K., Seppa¨nen, T., Airaksinen, K. E. J., Kessler, K. M., Takkunen, J. T., et al. Circadian rhythm of heart rate variability in survivors of cardiac arrest. American Journal of Cardiology. 70:610-615, 1992.
53Myers, G. A., Martin, G. J., Magid, N. M., Barnett, P. S., Schaad, J. W., Weiss, J. S., et al. Power spectral analysis of heart rate variability in sudden cardiac death: comparison to other methods. IEEE Transactions on Biomedical Engineering. 33: 1149-1156, 1986.
54Dougherty, C. M., & Burr, R. L. Comparison of heart rate variability in survivors and nonsurvivors of cardiac arrest. American Journal of Cardiology, 70, 441–448, 1992
55Pagani M, Lombardi F, Guzzetti S, Rimoldi 0, Furlan R, Pizzinelli P, Sandrone G, Malfatto G, Dell'Orto S, Piccaluga E, Turiel M, Baselli G, Cerutti S, Malliani A: Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res. 59:178-193, 1986
56Maestri R, Pinna GD. POLYAN: a computer program for polyparametric analysis of cardio-respiratory variability signals. Comput Methods Programs Biomed. 56:37-48,1998
57Bottini, P., Tantucci, C., Scionti, L., Dottorini, M. L., Puxeddu, E., Reboldi, G., Bolli, G. B., Casucci, G., Santeusanio, F., & Sorbini, C. A. Cardiovascular response to exercise in diabetic patients: Influence of autonomic neuropathy of different severity. Diabetologia, 38(2), 244-250. 1995
58Ho KKL, Moody GB, Peng CK, et al. Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 96: 842-848, 1997
59U. C. Niranjan and I. S. N. Murthy. ECG component delineation by Prony’s method. Signal Processing. 31:191-202, 1993.
60Duane Hanselman, Bruce Littlefield, Mastering MATLAB 6: a comprehensive tutorial and reference. NJ:Prentice Hall, 2001.
61T.H. Makikallio, T. Seppanen, K.E.J. Airaksinen, J. Koistinen, M.P. Tulppo, C.-K. Peng, A.L. Goldberger, H.V. Huikuri. Dynamic analysis of heart rate may predict subsequent ventricular tachycardia after myocardial infarction. Am. J. Cardiol. 80:779-783, 1997
62Cysarz D, Bettermann H, van leeuwen P. Entropies of short binary sequences in heart period dynamics. Am J Physiol Heart Circ Physiol. 278:H2163–72,2000
63Mohamed I, Owis, Ahmed H, Abou-Zied, Abou-Bakr M, Youssef, Yasser MK. Study of features on nonlinear dynamical modeling in ECG arrhythmia detection and classification. IEEE Trans Biomed Eng. 49(7):733–6, 2002.
64Rajendra Acharya U, Kumar A, Bhat PS, Lim Choo Min, Iyengar SS, Natarajan K, Krishnan SM. Classification of cardiac abnormalities using heart rate signals. Med Biol Eng Comput. 42(3):288–93, 2004.
65Jang Roger JS. ANFIS—Adaptive-network-based neuro-fuzzy inference systems. IEEE Trans Syst Man Cybernetics. 20(03):665–85, 1993.
66Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA. 88:2297–301, 1991
67A. Lempel, J. Ziv, On the complexity of finite sequences, IEEE Trans. Inform. Theory. 22(1):75-81, 1976.
68Theiler J, Eubank S, Longtin A, Galdrikian B, Farmer JD. Testing for nonlinearity in time series: the method of surrogate data. Physica D. 58:77–94,1992

全文公開日期本全文未授權公開 (校內網路)
全文公開日期本全文未授權公開 (校外網路)

簡易檢索 / 詳目顯示

相關論文