創新利用模擬呼吸竇性心律不整之多階熵評估乙型腎上腺素阻斷劑在心衰竭病人之治療成效

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：35

、訪客IP：3.135.249.119

姓名

阮仲豪(CHUNG-HAU JUAN) 查詢紙本館藏

畢業系所

跨領域轉譯醫學研究所

論文名稱

創新利用模擬呼吸竇性心律不整之多階熵評估乙型腎上腺素阻斷劑在心衰竭病人之治療成效
(Evolution of Multiscale Entropy with Simulated Respiratory Sinus Arrhythmia Component in Patients with Congestive Heart Failure Treated by β-Blocker)

相關論文

★ 非接觸式生理感測訊號分析研究	★ 以磁振造影探究有病灶及無病灶神經疾病的自動偵測方法之開發
★ 複雜系統跨頻率耦合方法	★ 不同麻醉深度之相位-振幅耦合量測及強度比較
★ 基於小波轉換之單一導程心電圖重構12導程心電圖與分類	★ 發展非侵入式即時交感神經活性指標之量測系統
★ 以靜息態功能性磁振造影探討頸動脈支架手術對於頸動脈狹窄病患大腦功能之影響	★ 運用加速度計實現具多項生理功能量測之即時監控IOT平台
★ 功能性抗生物沾黏單層膜於冠狀動脈心血管疾病標誌物之檢測應用	★ 發展高抗干擾非接觸式生理訊號監測系統
★ 應用特徵分群技術於非侵入式神經活性與行為活動訊號之生物指標萃取	★ 應用模擬電生理及人工智慧技術創造跨臨床心電圖資料庫之心肌缺血成像模型
★ 從同步鼾聲聲學分析和睡眠動態核磁共振成像進行靜態顱面測量和動態上呼吸道塌陷觀察，並探討其與阻塞性睡眠呼吸中止症嚴重程度的關聯。	★ 口內負壓睡眠裝置對於睡眠呼吸中止病人的轉譯研究- 針對解剖結構治療療效及策略探討
★ 體外加強反搏治療裝置開發	★ 自12導程心電圖擷取P波特徵辨識竇性心律下之心房顫動高風險病患

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

生理活動如心跳起伏等被認定為複雜行為並為非線性過程且非穏定性質。因此非線性數學方法被發展以應用在這些混沌系統。多尺度熵(Multiscale entropy-MSE) 和去趨勢波動分析(Detrended fluctuation analysis-DFA)是最常用的方法去量化在不同的時間尺度上的複雜性，而且被認為對分辨健康和病人特別是患有心臟衰竭的病人上較傳統的以熵為基礎的運算法則更為優異。無論如何，此兩種方法在生理上的意義仍未清楚瞭解。乙型交感神經阻斷劑在過去數十年一直被用作治療心衰竭的有效治療藥物，雖然陸續有很多不同的理論和證明，但呼吸竇性心律不整(Respiratory Sinus Arrhythmia-RSA)在其作用機轉之角色很少被提及甚至證明。因此我們假設呼吸竇性心律不整是乙型交感神經阻斷劑在治療心衰竭病人時的其中一個重要的關連，甚至呼吸竇性心律不整的比例和強度大小會影響治療效果。因此我們首先收集心衰竭病人在使用乙型交感神經阻斷劑治療後的MSE和DFA分析結果；然後利用創新的數學方法模擬不同RSA的分數（fraction）和振幅(amplitude)計算出MSE和DFA的結果跟之前實際臨床病人的數據作比較。
在10位以乙型交感神經阻斷劑(atenolol)治療進階心衰竭病人獲得包括基準、治療一個月和三個月後連續24小時心電圖記錄(24-hour Holter)。短段去趨勢波動分析在以乙型交感神經阻斷劑治療1至3個月後增加但中段去趨勢波動分析並沒有增加。在多尺度熵分析中，斜度1-5在治療1個月後上升並在治療3個月後由負值轉為正值。在面積5 在治療1個月後亦明顯由4.03±2.11 增加至 4.69±1.28。在多尺度熵的大尺度參數在治療後沒有明顯增加。研究中創新以模擬不同比例的分數和振幅的呼吸竇性心律不整在多尺度模型于以計算。在模擬的多尺度熵的短時間尺度參數相較於長時間尺度具有相對的結果和變化。總而言之，在我們的研究中多尺度熵和去趨勢波動分析是一種有效的方法可用於監測心衰竭病人的治療效果，而且更重要的是本論文中我們證明了透過首創的數學模型指出呼吸竇性心律不整很可能是導致乙型交感神經阻斷劑治療心衰竭病人的MSE和DFA改變的重要機轉。

摘要(英)

Physiological behaviors such as heart rate fluctuations have been recognized as complex behaviors originated from nonlinear processes and often with nonstationary property. So non-linear mathematic methods was developed to apply on these chaos system. Multiscale entropy (MSE) and detrended fluctuation analysis (DFA) are the most common methods to quantify complexity in multiple time scales and has been demonstrated to be superior to traditional entropy-based algorithms in discriminating healthy and disease conditions, especially in investigation of congestive heart failure. However, their physical interpretation remains unclear. ß-blocker was used for patients with congestive heart failure for decades. Although studies were approved many different mechanisms of it’s treatment, RSA was seldom be showed as a important role. So we assumed RSA is a major role of mechanism in the treatment o f patients with congestsive heart failure, even it’s fraction and amplitude will contribute to different effects and results . So we collected the MSE and DFA results of the patients treated by ß-blokcer in our clinical practice. After that we utilized the novel model of simulated different fraction and amplitude of RSA to calculate the results of MSE and DFAto compare with the clinical results.
Sequential 24-hour Holter ECG recordings were obtained at baseline, and 1 and 3 months after addition of atenolol therapy for advanced congestive heart failure in 10 patients. Short-term DFA increased after 1 to 3 months of atenolol treatment (0.79±0.16 vs. 0.95±0.22 and1.11±0.19 , all P < 0.05 compared with baseline ) while the intermediate-term DFA did not change. The slope 1-5 increased after 1 month of atenolol treatment (-0.08±0.10 vs. -0.03±0.10, P < 0.05) and changed from negative to positive value after 3 months of treatment (-0.03±0.10 vs. 0.02±0.06, P < 0.05). The mean area5 also significantly increased from 4.03±2.11 to 4.69±1.28 after 1 month of atenolol treatment. The large time-scale parameter of MSE (area6-20) did not significantly change after atenolol treatment (14.80±5.85 vs. 19.26±3.49, P = 0.06). The novel model of MSE with simulated different fractioin and amplitude of RSA components was calculated. The simulated short time-scale of parameter MSE was compatible with the actual results rather than the large time-scale parameter of MSE. In summary, MSE and DFA were a useful nonlinear methods to monitor the treatment of CHF and more importantly we proved that the change of RSA is possibly a major mechanism of MSE and DFA change in the treatment of CHF by ß-blocker.

關鍵字(中)

★ 多階熵
★ 呼吸竇性心律不整
★ 心衰竭
★ 乙型腎上腺素阻斷劑

關鍵字(英)

★ multiscale entropy
★ respiratory sinus arrhythmia
★ congestive heart failure
★ beta-blockers

論文目次

中文摘要………………………………………………..…………….………………..i
Abstract……………………………………………………………..………………..iii
Acknowledgment…..…………………………………………………………………v
Tables of Contents………………………..…………………………………………vii
List of tables…………………………………………………………...…..………....ix
List of Figures……………………………………………………..…...…….………xi
List of abbreviations…………………………………………………….…………xiii
Chapter 1 Introduction
1-1 Motivation…………………………………………………………………………1
1-2 Contribution………………………………………………………………………..2
1-3 Overview of Dissertation…………………………………………………………..2
Chapter 2 Overview of Congestive Heart Failure
2-1 Incidence and Epidemics..………………………………………………………...3
2-2 Etiology and Pathophysiology………………………………..…………..………3
2-3 Symptoms and Signs…………………………………………………….………..4
2-4 Evaluation and Diagnosis…………………………………………………………6
2-5 Treatments…………………………………………………………………………9
2-6 Beta-blokcers for CHF……………………………………………………………9
Chapter 3 Respiratory Sinus Arrhythmia
3-1 Introduction of RSA ………………………………...…………………………..11
3-2 Mechanism of RSA………..…………………………………………………….11
3-3 Application of RSA………………………………………………………………13
Chapter 4 Overview of Nonlinear methods
4-1 Multiscale Entropy……………………………………………………………….14
4-2 Detrended Fluctuation Analysis………………………………………………….17
4-3 Nonlinear mathematic methods of MSE and DFA in CHF………………………18
Chapter 5 Materials & Methods
5-1 Protocol of Study of CHF subjects………………………………………………19
5-2 R-R Interval Recording…………………………………………………………..22
5-3 Heart Rate Variability Parameters…………………………………………22
5-4 MSE Analysis…………………………………………………………………….22
5-5 Detrended Fluctuation Analysis………………………………………………….23
5-6 Statistical Analysis……………………………………………………….………23
5-7 Protocol of Simulated Model of RSA ……………………………………24
5-8 Results……………………………………………………………………………25
5-9 Discussion………………………………………………………………………..31
Chapter 6 Conclusion and Future Work
6-1 Conclusion……………………………………………………………...………..40
6-2 New & Noteworthy………………………………………………………………41
6-3 Limitation………………………………………………………………..………41
6-4 Future Work……………………………………………………………………...42
References……………………………………………………..…………………….43

參考文獻

1. Roger VL: Epidemiology of heart failure. Circ Res 2013, 113(6):646-659.
2. Rich MW: Epidemiology, pathophysiology, and etiology of congestive heart failure in older adults. J Am Geriatr Soc 1997, 45(8):968-974.
3. King M, Kingery J, Casey B: Diagnosis and evaluation of heart failure. Am Fam Physician 2012, 85(12):1161-1168.
4. Costa M, Goldberger AL, Peng CK: Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys 2005, 71(2 Pt 1):021906.
5. del Paso GA, Langewitz W, Robles H, Perez N: A between-subjects comparison of respiratory sinus arrhythmia and baroreceptor cardiac reflex sensitivity as non-invasive measures of tonic parasympathetic cardiac control. Int J Psychophysiol 1996, 22(3):163-171.
6. Berntson GG, Cacioppo JT, Quigley KS: Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications. Psychophysiology 1993, 30(2):183-196.
7. Gomberg-Maitland M, Baran DA, Fuster V: Treatment of congestive heart failure: guidelines for the primary care physician and the heart failure specialist. Arch Intern Med 2001, 161(3):342-352.
8. Czuriga I, Edes I: [Role of beta-blockers in the treatment of chronic heart heart failure]. Orv Hetil 2001, 142(37):2005-2012.
9. El-Omar M, Kardos A, Casadei B: Mechanisms of respiratory sinus arrhythmia in patients with mild heart failure. Am J Physiol Heart Circ Physiol 2001, 280(1):H125-131.
10. Teerlink JR: Endothelins: pathophysiology and treatment implications in chronic heart failure. Curr Heart Fail Rep 2005, 2(4):191-197.
11. Cohn JN: Structural basis for heart failure. Ventricular remodeling and its pharmacological inhibition. Circulation 1995, 91(10):2504-2507.
12. Parissis J, Farmakis D, Kadoglou N, Ikonomidis I, Fountoulaki E, Hatziagelaki E, Deftereos S, Follath F, Mebazaa A, Lekakis J et al: Body mass index in acute heart failure: association with clinical profile, therapeutic management and in-hospital outcome. Eur J Heart Fail 2016, 18(3):298-305.
13. el-Ebrashy N, el-Dansoury M, Higazi AM: Effect of digitalisation on urinary excretion of: water, sodium, potassium and chloride in heart failure. J Egypt Med Assoc 1968, 51(8):638-644.
14. Cohn JN, Johnson G, Ziesche S, Cobb F, Francis G, Tristani F, Smith R, Dunkman WB, Loeb H, Wong M et al: A comparison of enalapril with hydralazine-isosorbide dinitrate in the treatment of chronic congestive heart failure. The New England journal of medicine 1991, 325(5):303-310.
15. Gorre F, Vandekerckhove H: Beta-blockers: focus on mechanism of action. Which beta-blocker, when and why? Acta Cardiol 2010, 65(5):565-570.
16. Waagstein F, Hjalmarson A, Varnauskas E, Wallentin I: Effect of chronic beta-adrenergic receptor blockade in congestive cardiomyopathy. Br Heart J 1975, 37(10):1022-1036.
17. Design of the cardiac insufficiency bisoprolol study II (CIBIS II). The CIBIS II Scientific Committee. Fundam Clin Pharmacol 1997, 11(2):138-142.
18. McMurray JJ, Adamopoulos S, Anker SD, Auricchio A, Bohm M, Dickstein K, Falk V, Filippatos G, Fonseca C, Gomez-Sanchez MA et al: ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur Heart J 2012, 33(14):1787-1847.
19. Rehsia NS, Dhalla NS: Mechanisms of the beneficial effects of beta-adrenoceptor antagonists in congestive heart failure. Exp Clin Cardiol 2010, 15(4):e86-95.
20. Yasuma F, Hayano J: Respiratory sinus arrhythmia: why does the heartbeat synchronize with respiratory rhythm? Chest 2004, 125(2):683-690.
21. Ben-Tal A, Shamailov SS, Paton JF: Evaluating the physiological significance of respiratory sinus arrhythmia: looking beyond ventilation-perfusion efficiency. J Physiol 2012, 590(8):1989-2008.
22. Cysarz D, Zerm R, Bettermann H, Fruhwirth M, Moser M, Kroz M: Comparison of respiratory rates derived from heart rate variability, ECG amplitude, and nasal/oral airflow. Ann Biomed Eng 2008, 36(12):2085-2094.
23. Hrushesky WJ, Fader DJ, Berestka JS, Sommer M, Hayes J, Cope FO: Diminishment of respiratory sinus arrhythmia foreshadows doxorubicin-induced cardiomyopathy. Circulation 1991, 84(2):697-707.
24. Taha BH, Simon PM, Dempsey JA, Skatrud JB, Iber C: Respiratory sinus arrhythmia in humans: an obligatory role for vagal feedback from the lungs. J Appl Physiol (1985) 1995, 78(2):638-645.
25. Bernardi L, Keller F, Sanders M, Reddy PS, Griffith B, Meno F, Pinsky MR: Respiratory sinus arrhythmia in the denervated human heart. J Appl Physiol (1985) 1989, 67(4):1447-1455.
26. Zucker TL, Samuelson KW, Muench F, Greenberg MA, Gevirtz RN: The effects of respiratory sinus arrhythmia biofeedback on heart rate variability and posttraumatic stress disorder symptoms: a pilot study. Appl Psychophysiol Biofeedback 2009, 34(2):135-143.
27. Grossman P, Taylor EW: Toward understanding respiratory sinus arrhythmia: relations to cardiac vagal tone, evolution and biobehavioral functions. Biol Psychol 2007, 74(2):263-285.
28. Katona PG, Jih F: Respiratory sinus arrhythmia: noninvasive measure of parasympathetic cardiac control. J Appl Physiol 1975, 39(5):801-805.
29. Grossman P, Karemaker J, Wieling W: Prediction of tonic parasympathetic cardiac control using respiratory sinus arrhythmia: the need for respiratory control. Psychophysiology 1991, 28(2):201-216.
30. Masi CM, Hawkley LC, Rickett EM, Cacioppo JT: Respiratory sinus arrhythmia and diseases of aging: obesity, diabetes mellitus, and hypertension. Biol Psychol 2007, 74(2):212-223.
31. Saul JP, Berger RD, Chen MH, Cohen RJ: Transfer function analysis of autonomic regulation. II. Respiratory sinus arrhythmia. Am J Physiol 1989, 256(1 Pt 2):H153-161.
32. Staton L, El-Sheikh M, Buckhalt JA: Respiratory sinus arrhythmia and cognitive functioning in children. Dev Psychobiol 2009, 51(3):249-258.
33. Blues CM, Pomfrett CJ: Respiratory sinus arrhythmia and clinical signs of anaesthesia in children. Br J Anaesth 1998, 81(3):333-337.
34. Busa MA, van Emmerik REA: Multiscale entropy: A tool for understanding the complexity of postural control. J Sport Health Sci 2016, 5(1):44-51.
35. Baumert M, Javorka M, Seeck A, Faber R, Sanders P, Voss A: Multiscale entropy and detrended fluctuation analysis of QT interval and heart rate variability during normal pregnancy. Comput Biol Med 2012, 42(3):347-352.
36. Hardstone R, Poil SS, Schiavone G, Jansen R, Nikulin VV, Mansvelder HD, Linkenkaer-Hansen K: Detrended fluctuation analysis: a scale-free view on neuronal oscillations. Front Physiol 2012, 3:450.
37. Peng CK, Havlin S, Stanley HE, Goldberger AL: Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995, 5(1):82-87.
38. Costa M, Goldberger AL, Peng CK: Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 2002, 89(6):068102.
39. Bigger JT, Jr., Steinman RC, Rolnitzky LM, Fleiss JL, Albrecht P, Cohen RJ: Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants. Circulation 1996, 93(12):2142-2151.
40. Makikallio TH, Hoiber S, Kober L, Torp-Pedersen C, Peng CK, Goldberger AL, Huikuri HV: Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. TRACE Investigators. TRAndolapril Cardiac Evaluation. The American journal of cardiology 1999, 83(6):836-839.
41. Ho KK, Moody GB, Peng CK, Mietus JE, Larson MG, Levy D, Goldberger 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 1997, 96(3):842-848.
42. Ho YL, Lin C, Lin YH, Lo MT: The prognostic value of non-linear analysis of heart rate variability in patients with congestive heart failure--a pilot study of multiscale entropy. PLoS One 2011, 6(4):e18699.
43. Lin YH, Huang HC, Chang YC, Lin C, Lo MT, Liu LY, Tsai PR, Chen YS, Ko WJ, Ho YL et al: Multi-scale symbolic entropy analysis provides prognostic prediction in patients receiving extracorporeal life support. Crit Care 2014, 18(5):548.
44. Liu H, Zhan P, Shi J, Hu M, Wang G, Wang W: Heart rhythm complexity as predictors for the prognosis of end-stage renal disease patients undergoing hemodialysis. BMC Nephrol 2020, 21(1):536.
45. Huikuri HV, Makikallio TH, Peng CK, Goldberger AL, Hintze U, Moller M: Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. Circulation 2000, 101(1):47-53.
46. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 1996, 93(5):1043-1065.
47. P.D. W: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust 1967, 15:70-73.
48. Ivanov P, Rosenblum MG, Peng CK, Mietus JE, Havlin S, Stanley HE, Goldberger AL: Scaling and universality in heart rate variability distributions. Physica A 1998, 249:587-593.
49. Taylor JA, Carr DL, Myers CW, Eckberg DL: Mechanisms underlying very-low-frequency RR-interval oscillations in humans. Circulation 1998, 98(6):547-555.
50. Tripathi KK: Very low frequency oscillations in the power spectra of heart rate variability during dry supine immersion and exposure to non-hypoxic hypobaria. Physiol Meas 2011, 32(6):717-729.
51. Wu Z, Huang NE, Long SR, Peng CK: On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Natl Acad Sci U S A 2007, 104(38):14889-14894.
52. Peng CK, Costa M, Goldberger AL: Adaptive Data Analysis of Complex Fluctuations in Physiologic Time Series. Adv Adapt Data Anal 2009, 1(1):61-70.
53. Packer M: Pathophysiology of chronic heart failure. Lancet 1992, 340(8811):88-92.
54. Cohn JN, Levine TB, Olivari MT, Garberg V, Lura D, Francis GS, Simon AB, Rector T: Plasma norepinephrine as a guide to prognosis in patients with chronic congestive heart failure. The New England journal of medicine 1984, 311(13):819-823.
55. Krum H, van Veldhuisen DJ, Funck-Brentano C, Vanoli E, Silke B, Erdmann E, Follath F, Ponikowski P, Goulder M, Meyer W et al: Effect on mode of death of heart failure treatment started with bisoprolol followed by Enalapril, compared to the opposite order: results of the randomized CIBIS III trial. Cardiovasc Ther 2011, 29(2):89-98.
56. Guedon-Moreau L, Pinaud A, Logier R, Caron J, Lekieffre J, Dupuis B, Libersa CH: Effect of ramipril on heart rate variability in digitalis-treated patients with chronic heart failure. Cardiovasc Drugs Ther 1997, 11(4):531-536.
57. Jansson K, Hagerman I, Ostlund R, Karlberg KE, Nylander E, Nyquist O, Dahlstrom U: The effects of metoprolol and captopril on heart rate variability in patients with idiopathic dilated cardiomyopathy. Clin Cardiol 1999, 22(6):397-402.
58. Vaile JC, Chowdhary S, Osman F, Ross HF, Fletcher J, Littler WA, Coote JH, Townend JN: Effects of angiotensin II (AT1) receptor blockade on cardiac vagal control in heart failure. Clin Sci (Lond) 2001, 101(6):559-566.
59. Silva LE, Silva CA, Salgado HC, Fazan R, Jr.: The role of sympathetic and vagal cardiac control on complexity of heart rate dynamics. Am J Physiol Heart Circ Physiol 2017, 312(3):H469-H477.
60. Hayano J, Yasuma F: Hypothesis: respiratory sinus arrhythmia is an intrinsic resting function of cardiopulmonary system. Cardiovasc Res 2003, 58(1):1-9.
61. Pitzalis MV, Mastropasqua F, Massari F, Passantino A, Totaro P, Forleo C, Rizzon P: Beta-blocker effects on respiratory sinus arrhythmia and baroreflex gain in normal subjects. Chest 1998, 114(1):185-191.
62. Bartsch RP, Schumann AY, Kantelhardt JW, Penzel T, Ivanov P: Phase transitions in physiologic coupling. Proc Natl Acad Sci U S A 2012, 109(26):10181-10186.
63. Aad G, Abbott B, Abbott DC, Abed Abud A, Abeling K, Abhayasinghe DK, Abidi SH, AbouZeid OS, Abraham NL, Abramowicz H et al: Search for Higgs Boson Decays into a Z Boson and a Light Hadronically Decaying Resonance Using 13 TeV pp Collision Data from the ATLAS Detector. Phys Rev Lett 2020, 125(22):221802.
64. deBoer RW, Karemaker JM, Strackee J: Hemodynamic fluctuations and baroreflex sensitivity in humans: a beat-to-beat model. Am J Physiol 1987, 253(3 Pt 2):H680-689.
65. Tankeu AT, Azabji-Kenfack M, Nganou CN, Ngassam E, Kuate-Mfeukeu L, Mba C, Dehayem MY, Mbanya JC, Sobngwi E: Effect of propranolol on heart rate variability in hyperthyroidism. BMC Res Notes 2018, 11(1):151.
66. Aronson D, Burger AJ: Effect of beta-blockade on heart rate variability in decompensated heart failure. Int J Cardiol 2001, 79(1):31-39.
67. Ching ES, Tsang YK: Multifractality and scale invariance in human heartbeat dynamics. Phys Rev E Stat Nonlin Soft Matter Phys 2007, 76(4 Pt 1):041910.
68. Voss A, Schulz S, Schroeder R, Baumert M, Caminal P: Methods derived from nonlinear dynamics for analysing heart rate variability. Philos Trans A Math Phys Eng Sci 2009, 367(1887):277-296.
69. Rousseau MF, Chapelle F, Van Eyll C, Stoleru L, Hager D, Van Nueten L, Pouleur H: Medium-term effects of beta-blockade on left ventricular mechanics: a double-blind, placebo-controlled comparison of nebivolol and atenolol in patients with ischemic left ventricular dysfunction. J Card Fail 1996, 2(1):15-23.

指導教授

羅孟宗(Men-Tzung Lo)

審核日期

2021-1-26

推文