應用希爾伯特黃轉換於探究非線性生醫訊號特徵

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林澂(Chen Lin) 查詢紙本館藏

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論文名稱

應用希爾伯特黃轉換於探究非線性生醫訊號特徵
(Application of Hilbert-Huang Transform in Exploring the nonlinear characteristics of Biomedical Signals)

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摘要(中)

隨著臨床醫學的快速進步，醫學的最終目標是希望可以針對不同病人施行個體化的醫療，以取代目前忽略個體異質性的標準化疾病療法。雖然分子層次的生化指標，尤其是經由高通量技術所產生的，已取得一定程度的成功，不但可以提供疾病當前所處的嚴重度、甚至可分類單一疾病的亞群體，但因其需龐大之資源或是技術，多數的生化指標還是難以獲得臨床廣泛的使用，且以此類參數來監測或追蹤疾病的進程也不切實際，因此發展一些方法能探究病人在不同疾病病程時的特徵，補足目前方法之不足，便可能使個體化的醫學能加速達成。
生理訊號是由生物體特定的控制系統在維持內在系統恆定時所產生的輸出，分析此種訊號應為一個可用於探究個體在不同疾病及疾病進程中的特徵，並可提供生理或病理機轉上重要的訊息。此外多數的生理訊號，如心電圖、腦波或呼吸訊號，都可以由簡單且非侵入的方式取得，並藉由數位化長久的保存於便宜的儲存裝置中。也因此對於分析此類訊號的需求也就日益增大，但對此類充滿非線性及非穩態特質的訊號(因為要持續與改變的環境互動所造成)，對目前現存的分析方法是一大挑戰。
希爾伯特黃轉換為一個創新的方法，其演算法由經驗模態分析及希爾伯特轉換所組成。經驗模態分析為當中最關鍵的步驟，可將訊號解構為多個不同尺度、隨時間變化的內在模態函式，相較於用先驗的基底組成現有的訊號，此方法可以對非線性與非穩態系統產生的訊號在對時間上的頻率及振幅能有更好的表示，進一步，從希爾伯特黃轉換所產生的瞬時頻率，可以提供對分析訊號所內存訊息更佳的解釋，尤其是對多個系統交互相連、且需在不斷改變的環境下調控，並具非線性及非穩態的生理訊號。但當前只有非常少數的研究應用非線性與具適應性的訊號分析的概念，因此本研究的目的在於應用希爾伯特黃轉換於多種不同的生物醫學訊號相關主題，包括：(1)用心率變化於探索老化及疾病變化的動態特徵、(2)發展由訊號本身特質所萃取的去趨勢演算法，用於協助易受趨勢影響之非線性分析法、(3)由自動體外電擊器中具高度非穩態心電圖提取主要的特質。
此研究發現，由希爾伯特黃轉換可以在分析生理訊號中的相對應機轉能有更好的結果，且訊號中動態的訊息可能提供一個替代性評估老化或是一些特定疾病的指標，同時，希爾伯特黃轉換也可應用為一個可適性的濾波法，不但可留取訊號中重要的特徵或是去除訊號中的趨勢，且取出的訊號，除可提供一些臨床重要議題更寶貴的資訊外，也可以加強一些既存非線性方法的敏感度。

摘要(英)

As rapid development in clinical medicine, the ultimate goal of clinicians turns to tailor an individualized treatment for each patient rather than using standardized therapy which neglects the heterogeneity of subjects. Although molecular biomarkers, especially generated from high-throughput screening technologies, have achieved a certain degree of success in providing useful information about disease status or finding the subgroups of patients with same diseases, most of those biomarkers are still difficult to gain widespread acceptance because of the resource-consuming analysis and procedure. Furthermore, it is impractical for monitoring or following up the changes of disease processes. Complementary ways to explore the characteristics of patients in different statuses may expedite the progress in achieving the goal.
Analysis of physiological signals, the outputs of specific control systems of biological entity in order to maintain internal homeostasis under the environments, should be a promising way to probe the characteristics of the subjects during normal and diseased conditions and may provide crucial information of the underlying physiological and pathological mechanisms. Moreover, most of the physiological signals, such as electrocardiogram (ECG), electro-encephalogram (EEG), and respiratory signal, can be non-invasively and easily acquired and the digitalized signals can be stored for a long time by in-expensive data storage devices. The demands of approaches to analyze those signals inevitably become stronger by clinical practice. However, the major challenge of contemporary methods is the daunting nonlinearity and nonstationarity of those physiological signals which continuously interact with the varying environment.
An innovated method, Hilbert-Huang Transform (HHT), consists of Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis. EMD, the key step of HHT, can adaptively decompose the signal into many different intrinsic mode functions (IMFs) operated in different time-scales. Instead of integral of a priori basis to reconstruct the signal, it gives better time-frequency-energy representation for the nonlinear and nonstationary systems. Moreover, the instantaneous frequency derived from HHT can provide a better route to analyze the underlying information of the signal, especially for physiological systems that are nonlinear by nature and interconnect with other systems under the perturbation of the ever-changing environments. However, only limited studies adopt the concept of nonlinear and adaptive data analysis. The aims of the study are, therefore, trying to apply HHT in several biomedical topics including (1) probing the dynamical characteristics of aging processes or pathological changes of heart rate dynamics; (2) developing a data-driven detrending method to assist the existed nonlinear analysis that is vulnerable to nonstationary trends; (3) extracting the main features of the highly nonstationary ECG signals from automated extracorporeal defibrillator.
It is suggested that the underlying mechanisms of physiological signals are better described by HHT and dynamical information of the signals may serve as alternative biomarkers for aging or some diseases. Moreover, HHT can also be applied as adaptive filter to extract the important features or eliminate the unwanted trends of the signals. The elicited components of the signals can provide more crucial information of clinical relevant issues and the sensitivity of the existed nonlinear analysis for the reconstructed signals can be improved.

關鍵字(中)

★ 非線性
★ 希爾伯特黃分析
★ 非穩態
★ 可適性訊號分析
★ 生醫訊號

關鍵字(英)

★ Nonlinear
★ Hilbert-Huang Transform
★ Nonstationary
★ Biomedical Signals
★ Adaptive Data Analysis

論文目次

中文摘要 ......... i
Abstract ......... ii
List of Tables ......................... vi
List of Figures ....................... vii
Chapter 1 Introduction ......... 1
Background ................... 1
Empirical Mode Decomposition .............. 2
Ensemble Empirical Mode Decomposition ....................... 4
Probing the Dynamical Information of Physiological Processes ................ 5
Using EMD/EEMD as an Adaptive Filter for Nonstationary Biomedical signals ................ 5
Chapter 2 ...... 7
Multimodal Coupling Assessment (MMCA) of Dynamic Cardiac Vagal Activities under Respiratory Modulation ... 7
Loss of Frequency Modulation in Heart Beat Oscillation of Patients with Brain Death..... 22
Abnormal Oscillations of Heart rate Dynamics during Sleep in Patients with Obstructive Sleep Apnea ................. 28
Chapter 3 .... 38
Evolution of Multiscale Entropy in Patients with Titrated β-blocker Treatment in Patients with Congestive Heart Failure ............... 43
Assessment of the Prognostic Significance of Parameters Derived from MSE for CHF Patients ....................... 46
Chapter 4 .... 55
A New Way to Analyze Resuscitation Quality by Reviewing Automated External Defibrillation Data ....... 55
Using Nonlinear analysis Predicts Successful Defibrillation for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest ..................... 65
Chapter 5 Conclusion and Future Works ... 75
Instantaneous Characteristics of Physiological Signals ... 75
Data-driven Multiscale Entropy as Biomarkers for CHF Patients ............ 76
Reconstruction of Specific Waveform Morphology Embedded in ECG Signals during VF and CPR ....................... 76
Future Works ............... 76

參考文獻

Reference List
(1) Buchman TG. The community of the self. Nature 2002 Nov 14;420(6912):246-51.
(2) Goldberger AL, Amaral LA, Hausdorff JM, Ivanov PC, Peng CK, Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci U S A 2002 Feb 19;99 Suppl 1:2466-72.
(3) 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.
(4) Peng CK, Havlin S, Hausdorff JM, Mietus JE, Stanley HE, Goldberger AL. Fractal mechanisms and heart rate dynamics. Long-range correlations and their breakdown with disease. J Electrocardiol 1995;28 Suppl:59-65.
(5) Seely AJ, Macklem PT. Complex systems and the technology of variability analysis. Crit Care 2004 Dec;8(6):R367-R384.
(6) 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 Mar 1;93(5):1043-65.
(7) Lo MT, Novak V, Peng CK, Liu Y, Hu K. Nonlinear phase interaction between nonstationary signals: a comparison study of methods based on Hilbert-Huang and Fourier transforms. Phys Rev E Stat Nonlin Soft Matter Phys 2009 Jun;79(6 Pt 1):061924.
(8) Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys 2005 Feb;71(2 Pt 1):021906.
(9) Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995;5(1):82-7.
(10) Yuan HK, Lin C, Tsai PH, et al. Acute increase of complexity in the neurocardiovascular dynamics following carotid stenting. Acta Neurol Scand 2011 Mar;123(3):187-92.
(11) Huang NE, Shen Z, Long SR, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences 1998 Mar 8;454(1971):903-95.
(12) Lo MT, Tsai PH, Lin PF, Lin C, Hsin YL. The nonlinear and nonstationary properties in EEG signals: probing the complex fluctuations by Hilbert Huang Transform. Adv Adapt Data Anal 2009;1(3):461-82.
(13) Lo MT, Novak V, Peng CK, Liu Y, Hu K. Nonlinear phase interaction between nonstationary signals: a comparison study of methods based on Hilbert-Huang and Fourier transforms. Phys Rev E Stat Nonlin Soft Matter Phys 2009 Jun;79(6 Pt 1):061924.
(14) Huang W, Shen Z, Huang NE, Fung YC. Engineering analysis of biological variables: an example of blood pressure over 1 day. Proc Natl Acad Sci U S A 1998;95:4816-21.
(15) Hu K, Peng CK, Czosnyka M, Zhao P, Novak V. Nonlinear assessment of cerebral autoregulation from spontaneous blood pressure and cerebral blood flow fluctuations. Cardiovasc Eng 2008 Mar;8(1):60-71.
(16) Lo MT, Hu K, Liu Y, Peng CK, Novak V. Multimodal Pressure Flow Analysis: Application of Hilbert Huang Transform in Cerebral Blood Flow Regulation. EURASIP J Appl Signal Processing 2008;2008:785243.
(17) Wu Z, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 2009;1(1):1-41.
(18) Buchman TG, Stein PK, Goldstein B. Heart rate variability in critical illness and critical care. Curr Opin Crit Care 2002 Aug;8(4):311-5.
(19) Smith SA. Reduced sinus arrhythmia in diabetic autonomic neuropathy: diagnostic value of an age-related normal range. Br Med J (Clin Res Ed) 1982 Dec 4;285(6355):1599-601.
(20) La Rovere MT, Bigger JT, Jr., Marcus FI, Mortara A, Schwartz PJ. Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. ATRAMI (Autonomic Tone and Reflexes After Myocardial Infarction) Investigators. Lancet 1998 Feb 14;351(9101):478-84.
(21) Eckberg DL. Parasympathetic cardiovascular control in human disease: a critical review of methods and results. Am J Physiol 1980 Nov;239(5):H581-H593.
(22) Bristow JD, Honour AJ, Pickering GW, Sleight P, Smyth HS. Diminished baroreflex sensitivity in high blood pressure. Circulation 1969 Jan;39(1):48-54.
(23) Mackay JD. Respiratory sinus arrhythmia in diabetic neuropathy. Diabetologia 1983 Apr;24(4):253-6.
(24) Wieling W, van Brederode JF, de Rijk LG, Borst C, Dunning AJ. Reflex control of heart rate in normal subjects in relation to age: a data base for cardiac vagal neuropathy. Diabetologia 1982 Mar;22(3):163-6.
(25) Low PA, Denq JC, Opfer-Gehrking TL, Dyck PJ, O’’Brien PC, Slezak JM. Effect of age and gender on sudomotor and cardiovagal function and blood pressure response to tilt in normal subjects. Muscle Nerve 1997 Dec;20(12):1561-8.
(26) Hrushesky WJ, Fader D, Schmitt O, Gilbertsen V. The respiratory sinus arrhythmia: a measure of cardiac age. Science 1984 Jun 1;224(4652):1001-4.
(27) Levin AB. A simple test of cardiac function based upon the heart rate changes induced by the Valsalva maneuver. Am J Cardiol 1966 Jul;18(1):90-9.
(28) Ewing DJ, Neilson JM, Travis P. New method for assessing cardiac parasympathetic activity using 24 hour electrocardiograms. Br Heart J 1984 Oct;52(4):396-402.
(29) Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 1981 Jul 10;213(4504):220-2.
(30) Goldberger AL, Goldwater D, Bhargava V. Atropine unmasks bed-rest effect: a spectral analysis of cardiac interbeat intervals. J Appl Physiol 1986 Nov;61(5):1843-8.
(31) Shannon DC, Carley DW, Benson H. Aging of modulation of heart rate. Am J Physiol 1987 Oct;253(4 Pt 2):H874-H877.
(32) Vybiral T, Bryg RJ, Maddens ME, et al. Effects of transdermal scopolamine on heart rate variability in normal subjects. Am J Cardiol 1990 Mar 1;65(9):604-8.
(33) Lipsitz LA, Mietus J, Moody GB, Goldberger AL. Spectral characteristics of heart rate variability before and during postural tilt. Relations to aging and risk of syncope. Circulation 1990 Jun;81(6):1803-10.
(34) Malliani A, Lombardi F, Pagani M. Power spectrum analysis of heart rate variability: a tool to explore neural regulatory mechanisms. Br Heart J 1994 Jan;71(1):1-2.
(35) Sibony O, Fouillot JP, Luton D, Oury JF, Blot P. Effects of neuromuscular blockade on fetal heart rate variability: a power spectrum analysis. J Appl Physiol 1995 Jul;79(1):63-5.
(36) Warren JH, Jaffe RS, Wraa CE, Stebbins CL. Effect of autonomic blockade on power spectrum of heart rate variability during exercise. Am J Physiol 1997 Aug;273(2 Pt 2):R495-R502.
(37) Jasson S, Medigue C, Maison-Blanche P, et al. Instant power spectrum analysis of heart rate variability during orthostatic tilt using a time-/frequency-domain method. Circulation 1997 Nov 18;96(10):3521-6.
(38) Pagani M, Lombardi F, Guzzetti S, et al. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res 1986 Aug;59(2):178-93.
(39) Umetani K, Singer DH, McCraty R, Atkinson M. Twenty-four hour time domain heart rate variability and heart rate: relations to age and gender over nine decades. J Am Coll Cardiol 1998 Mar 1;31(3):593-601.
(40) Kuo TB, Lin T, Yang CC, Li CL, Chen CF, Chou P. Effect of aging on gender differences in neural control of heart rate. Am J Physiol 1999 Dec;277(6 Pt 2):H2233-H2239.
(41) Fauchier L, Babuty D, Cosnay P, Fauchier JP. Prognostic value of heart rate variability for sudden death and major arrhythmic events in patients with idiopathic dilated cardiomyopathy. J Am Coll Cardiol 1999 Apr;33(5):1203-7.
(42) Bernardi L, Wdowczyk-Szulc J, Valenti C, et al. Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability. J Am Coll Cardiol 2000 May;35(6):1462-9.
(43) Ho KK, 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 1997 Aug 5;96(3):842-8.
(44) Wang YY, Fish PJ. Correction for nonstationarity and window broadening in Doppler spectrum estimation. Ieee Signal Processing Letters 1997 Jan;4(1):18-20.
(45) 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 1995 Feb;78(2):638-45.
(46) Eckberg DL. The human respiratory gate. J Physiol 2003 Apr 15;548(Pt 2):339-52.
(47) Piepoli M, Sleight P, Leuzzi S, et al. Origin of respiratory sinus arrhythmia in conscious humans. An important role for arterial carotid baroreceptors. Circulation 1997 Apr 1;95(7):1813-21.
(48) Hoyer D, Hader O, Zwiener U. Relative and intermittent cardiorespiratory coordination. IEEE Eng Med Biol Mag 1997 Nov;16(6):97-104.
(49) Hoyer D, Pompe B, Chon KH, Hardraht H, Wicher C, Zwiener U. Mutual information function assesses autonomic information flow of heart rate dynamics at different time scales. IEEE Trans Biomed Eng 2005 Apr;52(4):584-92.
(50) Peng CK, Henry IC, Mietus JE, et al. Heart rate dynamics during three forms of meditation. Int J Cardiol 2004 May;95(1):19-27.
(51) Houtveen JH, Rietveld S, de Geus EJ. Contribution of tonic vagal modulation of heart rate, central respiratory drive, respiratory depth, and respiratory frequency to respiratory sinus arrhythmia during mental stress and physical exercise. Psychophysiology 2002 Jul;39(4):427-36.
(52) Godin PJ, Buchman TG. Uncoupling of biological oscillators: a complementary hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome. Crit Care Med 1996 Jul;24(7):1107-16.
(53) Goldstein B, Fiser DH, Kelly MM, Mickelsen D, Ruttimann U, Pollack MM. Decomplexification in critical illness and injury: relationship between heart rate variability, severity of illness, and outcome. Crit Care Med 1998 Feb;26(2):352-7.
(54) Hoyer D, Leder U, Hoyer H, Pompe B, Sommer M, Zwiener U. Mutual information and phase dependencies: measures of reduced nonlinear cardiorespiratory interactions after myocardial infarction. Med Eng Phys 2002 Jan;24(1):33-43.
(55) Hoyer D, Friedrich H, Frank B, et al. Autonomic information flow improves prognostic impact of task force HRV monitoring. Comput Methods Programs Biomed 2006 Mar;81(3):246-55.
(56) Mowery NT, Norris PR, Riordan W, Jenkins JM, Williams AE, Morris JA, Jr. Cardiac uncoupling and heart rate variability are associated with intracranial hypertension and mortality: a study of 145 trauma patients with continuous monitoring. J Trauma 2008 Sep;65(3):621-7.
(57) Eckberg DL. Human sinus arrhythmia as an index of vagal cardiac outflow. J Appl Physiol 1983 Apr;54(4):961-6.
(58) Hirsch JA, Bishop B. Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate. Am J Physiol 1981 Oct;241(4):H620-H629.
(59) Grossman P, Wilhelm FH, Spoerle M. Respiratory sinus arrhythmia, cardiac vagal control, and daily activity. Am J Physiol Heart Circ Physiol 2004 Aug;287(2):H728-H734.
(60) Moody GB, Mark RG, Bump MA, et al. Clinical validation of the ECG-derived respiration (EDR) technique. Comput Cardiol 1986;13:507-10.
(61) Moody GB, Mark RG, Zoccola A, Vergani L. Derivation of respiratory signals from multilead ECGs. Comput Cardiol 1985;12:113-6.
(62) Thomas RJ, Mietus JE, Peng CK, Goldberger AL. An electrocardiogram-based technique to assess cardiopulmonary coupling during sleep. Sleep 2005 Sep 1;28(9):1151-61.
(63) Wijdicks EF. The diagnosis of brain death. N Engl J Med 2001 Apr 19;344(16):1215-21.
(64) Baillard C, Vivien B, Mansier P, et al. Brain death assessment using instant spectral analysis of heart rate variability. Crit Care Med 2002 Feb;30(2):306-10.
(65) Biswas AK, Scott WA, Sommerauer JF, Luckett PM. Heart rate variability after acute traumatic brain injury in children. Crit Care Med 2000 Dec;28(12):3907-12.
(66) Rapenne T, Moreau D, Lenfant F, Boggio V, Cottin Y, Freysz M. Could heart rate variability analysis become an early predictor of imminent brain death? A pilot study. Anesth Analg 2000 Aug;91(2):329-36.
(67) Chan J, Sanderson J, Chan W, et al. Prevalence of sleep-disordered breathing in diastolic heart failure. Chest 1997 Jun;111(6):1488-93.
(68) Guilleminault C, Connolly SJ, Winkle RA. Cardiac arrhythmia and conduction disturbances during sleep in 400 patients with sleep apnea syndrome. Am J Cardiol 1983 Sep 1;52(5):490-4.
(69) Hla KM, Young TB, Bidwell T, Palta M, Skatrud JB, Dempsey J. Sleep apnea and hypertension. A population-based study. Ann Intern Med 1994 Mar 1;120(5):382-8.
(70) Koehler U, Schafer H. Is obstructive sleep apnea (OSA) a risk factor for myocardial infarction and cardiac arrhythmias in patients with coronary heart disease (CHD)? Sleep 1996 May;19(4):283-6.
(71) Schafer H, Koehler U, Ewig S, Hasper E, Tasci S, Luderitz B. Obstructive sleep apnea as a risk marker in coronary artery disease. Cardiology 1999;92(2):79-84.
(72) Yaggi HK, Concato J, Kernan WN, Lichtman JH, Brass LM, Mohsenin V. Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med 2005 Nov 10;353(19):2034-41.
(73) Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 2002 May 1;165(9):1217-39.
(74) Haas DC, Foster GL, Nieto FJ, et al. Age-dependent associations between sleep-disordered breathing and hypertension: importance of discriminating between systolic/diastolic hypertension and isolated systolic hypertension in the Sleep Heart Health Study. Circulation 2005 Feb 8;111(5):614-21.
(75) Somers VK, Dyken ME, Mark AL, Abboud FM. Sympathetic-nerve activity during sleep in normal subjects. N Engl J Med 1993 Feb 4;328(5):303-7.
(76) Somers VK, Dyken ME, Clary MP, Abboud FM. Sympathetic neural mechanisms in obstructive sleep apnea. J Clin Invest 1995 Oct;96(4):1897-904.
(77) Hedner JA, Wilcox I, Laks L, Grunstein RR, Sullivan CE. A specific and potent pressor effect of hypoxia in patients with sleep apnea. Am Rev Respir Dis 1992 Nov;146(5 Pt 1):1240-5.
(78) Narkiewicz K, van de Borne PJ, Pesek CA, Dyken ME, Montano N, Somers VK. Selective potentiation of peripheral chemoreflex sensitivity in obstructive sleep apnea. Circulation 1999 Mar 9;99(9):1183-9.
(79) Somers VK, Dyken ME, Mark AL, Abboud FM. Parasympathetic hyperresponsiveness and bradyarrhythmias during apnoea in hypertension. Clin Auton Res 1992 Jun;2(3):171-6.
(80) El Solh AA, Akinnusi ME, Baddoura FH, Mankowski CR. Endothelial cell apoptosis in obstructive sleep apnea: a link to endothelial dysfunction. Am J Respir Crit Care Med 2007 Jun 1;175(11):1186-91.
(81) Kraiczi H, Hedner J, Peker Y, Carlson J. Increased vasoconstrictor sensitivity in obstructive sleep apnea. J Appl Physiol 2000 Aug;89(2):493-8.
(82) Shiomi T, Guilleminault C, Sasanabe R, Hirota I, Maekawa M, Kobayashi T. Augmented very low frequency component of heart rate variability during obstructive sleep apnea. Sleep 1996 Jun;19(5):370-7.
(83) Roche F, Gaspoz JM, Court-Fortune, et al. Alteration of QT rate dependence reflects cardiac autonomic imbalance in patients with obstructive sleep apnea syndrome. Pacing Clin Electrophysiol 2003 Jul;26(7 Pt 1):1446-53.
(84) Guo D, Peng CK, Wu HL, et al. ECG-derived cardiopulmonary analysis of pediatric sleep-disordered breathing. Sleep Med 2011 Apr;12(4):384-9.
(85) Wang YY, Fish PJ. Correction for nonstationarity and window broadening in Doppler spectrum estimation. Ieee Signal Processing Letters 1997 Jan;4(1):18-20.
(86) Wessel N, Voss HMH, Ziehmann CVU. Nonlinear analysis of complex phenomena in cardiological data. Herzschr Elektrophys 2000;11:159-73.
(87) Khoo MC, Kim TS, Berry RB. Spectral indices of cardiac autonomic function in obstructive sleep apnea. Sleep 1999 Jun 15;22(4):443-51.
(88) Narkiewicz K, Montano N, Cogliati C, van de Borne PJ, Dyken ME, Somers VK. Altered cardiovascular variability in obstructive sleep apnea. Circulation 1998 Sep 15;98(11):1071-7.
(89) Penzel T, Kantelhardt JW, Grote L, Peter JH, Bunde A. Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Trans Biomed Eng 2003 Oct;50(10):1143-51.
(90) Aljadeff G, Gozal D, Schechtman VL, Burrell B, Harper RM, Ward SL. Heart rate variability in children with obstructive sleep apnea. Sleep 1997 Feb;20(2):151-7.
(91) Otzenberger H, Gronfier C, Simon C, et al. Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men. Am J Physiol 1998 Sep;275(3 Pt 2):H946-H950.
(92) Bonsignore MR, Parati G, Insalaco G, et al. Continuous positive airway pressure treatment improves baroreflex control of heart rate during sleep in severe obstructive sleep apnea syndrome. Am J Respir Crit Care Med 2002 Aug 1;166(3):279-86.
(93) Belozeroff V, Berry RB, Sassoon CS, Khoo MC. Effects of CPAP therapy on cardiovascular variability in obstructive sleep apnea: a closed-loop analysis. Am J Physiol Heart Circ Physiol 2002 Jan;282(1):H110-H121.
(94) Khoo MC, Belozeroff V, Berry RB, Sassoon CS. Cardiac autonomic control in obstructive sleep apnea: effects of long-term CPAP therapy. Am J Respir Crit Care Med 2001 Sep 1;164(5):807-12.
(95) Butler GC, Naughton MT, Rahman MA, Bradley TD, Floras JS. Continuous positive airway pressure increases heart rate variability in congestive heart failure. J Am Coll Cardiol 1995 Mar 1;25(3):672-9.
(96) Wang TJ, Evans JC, Benjamin EJ, Levy D, LeRoy EC, Vasan RS. Natural history of asymptomatic left ventricular systolic dysfunction in the community. Circulation 2003 Aug 26;108(8):977-82.
(97) Stewart S, Jenkins A, Buchan S, McGuire A, Capewell S, McMurray JJ. The current cost of heart failure to the National Health Service in the UK. Eur J Heart Fail 2002 Jun;4(3):361-71.
(98) van Jaarsveld CH, Ranchor AV, Kempen GI, Coyne JC, van Veldhuisen DJ, Sanderman R. Epidemiology of heart failure in a community-based study of subjects aged > or = 57 years: incidence and long-term survival. Eur J Heart Fail 2006 Jan;8(1):23-30.
(99) Flather MD, Yusuf S, Kober L, et al. Long-term ACE-inhibitor therapy in patients with heart failure or left-ventricular dysfunction: a systematic overview of data from individual patients. ACE-Inhibitor Myocardial Infarction Collaborative Group. Lancet 2000 May 6;355(9215):1575-81.
(100) Dahlof B, Devereux RB, Kjeldsen SE, et al. Cardiovascular morbidity and mortality in the Losartan Intervention For Endpoint reduction in hypertension study (LIFE): a randomised trial against atenolol. Lancet 2002 Mar 23;359(9311):995-1003.
(101) Cohn JN, Tognoni G. A randomized trial of the angiotensin-receptor blocker valsartan in chronic heart failure. N Engl J Med 2001 Dec 6;345(23):1667-75.
(102) La Rovere MT, Pinna GD, Maestri R, et al. Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients. Circulation 2003 Feb 4;107(4):565-70.
(103) Lombardi F. Chaos theory, heart rate variability, and arrhythmic mortality. Circulation 2000 Jan 4;101(1):8-10.
(104) Jagric T, Marhl M, Stajer D, et al. Irregularity test for very short electrocardiogram (ECG) signals as a method for predicting a successful defibrillation in patients with ventricular fibrillation. Transl Res 2007 Mar;149(3):145-51.
(105) Perc M. Nonlinear time series analysis of the human electrocardiogram. European Journal of Physics 2005 Sep;26(5):757-68.
(106) Perc M. The dynamics of human gait. European Journal of Physics 2005 May;26(3):525-34.
(107) Yuan HK, Lin C, Tsai PH, et al. Acute increase of complexity in the neurocardiovascular dynamics following carotid stenting. Acta Neurol Scand 2011 Mar;123(3):187-92.
(108) Costa M, Cygankiewicz I, Zareba W, Bayes de Luna A, Goldberger AL, Lobodzinski S. Multiscale Complexity Analysis of Heart Rate Dynamics in Heart Failure: Preliminary Findings from the MUSIC Study. Comput Cardiol 2006;33:101-3.
(109) Costa M, Priplata AA, Lipsitz LA, et al. Noise and poise: Enhancement of postural complexity in the elderly with a stochastic-resonance-based therapy. Europhys Lett 2007 Mar;77:68008.
(110) Kantz H, Schreiber T. Nonlinear Time Series Analysis. 2 ed. Cambridge: Cambridge University Press, 2004.
(111) Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 2002 Aug 5;89(6):068102.
(112) 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 Sep 18;104(38):14889-94.
(113) 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.
(114) Pikkujamsa SM, Makikallio TH, Sourander LB, et al. Cardiac interbeat interval dynamics from childhood to senescence : comparison of conventional and new measures based on fractals and chaos theory. Circulation 1999 Jul 27;100(4):393-9.
(115) Vanoli E, Adamson PB, Ba L, Pinna GD, Lazzara R, Orr WC. Heart rate variability during specific sleep stages. A comparison of healthy subjects with patients after myocardial infarction. Circulation 1995 Apr 1;91(7):1918-22.
(116) Sanderson JE, Chan SK, Yip G, et al. Beta-blockade in heart failure: a comparison of carvedilol with metoprolol. J Am Coll Cardiol 1999 Nov 1;34(5):1522-8.
(117) Mortara A, La Rovere MT, Pinna GD, Maestri R, Capomolla S, Cobelli F. Nonselective beta-adrenergic blocking agent, carvedilol, improves arterial baroflex gain and heart rate variability in patients with stable chronic heart failure. J Am Coll Cardiol 2000 Nov 1;36(5):1612-8.
(118) Effect of metoprolol CR/XL in chronic heart failure: Metoprolol CR/XL Randomised Intervention Trial in Congestive Heart Failure (MERIT-HF). Lancet 1999 Jun 12;353(9169):2001-7.
(119) Cohn JN, Levine TB, Olivari MT, et al. Plasma norepinephrine as a guide to prognosis in patients with chronic congestive heart failure. N Engl J Med 1984 Sep 27;311(13):819-23.
(120) Packer M. Pathophysiology of chronic heart failure. Lancet 1992 Jul 11;340(8811):88-92.
(121) Krum H, van Veldhuisen DJ, Funck-Brentano C, 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 2010 Jun 1.
(122) Makikallio TH, Hoiber S, Kober L, et al. 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. Am J Cardiol 1999 Mar 15;83(6):836-9.
(123) Sanderson JE, Yeung LY, Chan S, et al. Effect of beta-blockade on baroreceptor and autonomic function in heart failure. Clin Sci (Lond) 1999 Feb;96(2):137-46.
(124) Lin LY, Lin JL, Du CC, Lai LP, Tseng YZ, Huang SK. Reversal of deteriorated fractal behavior of heart rate variability by beta-blocker therapy in patients with advanced congestive heart failure. J Cardiovasc Electrophysiol 2001 Jan;12(1):26-32.
(125) Hunt SA, Abraham WT, Chin MH, et al. ACC/AHA 2005 Guideline Update for the Diagnosis and Management of Chronic Heart Failure in the Adult: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Update the 2001 Guidelines for the Evaluation and Management of Heart Failure): developed in collaboration with the American College of Chest Physicians and the International Society for Heart and Lung Transplantation: endorsed by the Heart Rhythm Society. Circulation 2005 Sep 20;112(12):e154-e235.
(126) Tomaselli GF, Zipes DP. What causes sudden death in heart failure? Circ Res 2004 Oct 15;95(8):754-63.
(127) Zipes DP, Wellens HJ. Sudden cardiac death. Circulation 1998 Nov 24;98(21):2334-51.
(128) Myerburg RJ. Sudden cardiac death: exploring the limits of our knowledge. J Cardiovasc Electrophysiol 2001 Mar;12(3):369-81.
(129) Sans S, Kesteloot H, Kromhout D. The burden of cardiovascular diseases mortality in Europe. Task Force of the European Society of Cardiology on Cardiovascular Mortality and Morbidity Statistics in Europe. Eur Heart J 1997 Aug;18(8):1231-48.
(130) Christenson J, Andrusiek D, Everson-Stewart S, et al. Chest compression fraction determines survival in patients with out-of-hospital ventricular fibrillation. Circulation 2009 Sep 29;120(13):1241-7.
(131) Ko PC, Chen WJ, Lin CH, Ma MH, Lin FY. Evaluating the quality of prehospital cardiopulmonary resuscitation by reviewing automated external defibrillator records and survival for out-of-hospital witnessed arrests. Resuscitation 2005 Feb;64(2):163-9.
(132) Wik L, Steen PA, Bircher NG. Quality of bystander cardiopulmonary resuscitation influences outcome after prehospital cardiac arrest. Resuscitation 1994 Dec;28(3):195-203.
(133) Abella BS, Sandbo N, Vassilatos P, et al. Chest compression rates during cardiopulmonary resuscitation are suboptimal: a prospective study during in-hospital cardiac arrest. Circulation 2005 Feb 1;111(4):428-34.
(134) Valenzuela TD, Kern KB, Clark LL, et al. Interruptions of chest compressions during emergency medical systems resuscitation. Circulation 2005 Aug 30;112(9):1259-65.
(135) Wik L, Kramer-Johansen J, Myklebust H, et al. Quality of cardiopulmonary resuscitation during out-of-hospital cardiac arrest. JAMA 2005 Jan 19;293(3):299-304.
(136) Chiang WC, Ko PC, Wang HC, et al. EMS in Taiwan: past, present, and future. Resuscitation 2009 Jan;80(1):9-13.
(137) Ma MH, Chiang WC, Ko PC, et al. Outcomes from out-of-hospital cardiac arrest in Metropolitan Taipei: does an advanced life support service make a difference? Resuscitation 2007 Sep;74(3):461-9.
(138) Kramer-Johansen J, Edelson DP, Losert H, Kohler K, Abella BS. Uniform reporting of measured quality of cardiopulmonary resuscitation (CPR). Resuscitation 2007 Sep;74(3):406-17.
(139) Fischer H, Gruber J, Neuhold S, et al. Effects and limitations of an AED with audiovisual feedback for cardiopulmonary resuscitation: a randomized manikin study. Resuscitation 2011 Jul;82(7):902-7.
(140) Koster RW, Baubin MA, Bossaert LL, et al. European Resuscitation Council Guidelines for Resuscitation 2010 Section 2. Adult basic life support and use of automated external defibrillators. Resuscitation 2010 Oct;81(10):1277-92.
(141) Travers AH, Rea TD, Bobrow BJ, et al. Part 4: CPR overview: 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation 2010 Nov 2;122(18 Suppl 3):S676-S684.
(142) Berg MD, Clark LL, Valenzuela TD, Kern KB, Berg RA. Post-shock chest compression delays with automated external defibrillator use. Resuscitation 2005 Mar;64(3):287-91.
(143) Cobb LA, Fahrenbruch CE, Walsh TR, et al. Influence of cardiopulmonary resuscitation prior to defibrillation in patients with out-of-hospital ventricular fibrillation. JAMA 1999 Apr 7;281(13):1182-8.
(144) Wik L, Hansen TB, Fylling F, et al. Delaying defibrillation to give basic cardiopulmonary resuscitation to patients with out-of-hospital ventricular fibrillation: a randomized trial. JAMA 2003 Mar 19;289(11):1389-95.
(145) Yakaitis RW, Ewy GA, Otto CW, Taren DL, Moon TE. Influence of time and therapy on ventricular defibrillation in dogs. Crit Care Med 1980 Mar;8(3):157-63.
(146) Callaway CW, Menegazzi JJ. Waveform analysis of ventricular fibrillation to predict defibrillation. Curr Opin Crit Care 2005 Jun;11(3):192-9.
(147) Gray RA, Pertsov AM, Jalife J. Spatial and temporal organization during cardiac fibrillation. Nature 1998 Mar 5;392(6671):75-8.
(148) Hastings HM, Evans SJ, Quan W, Chong ML, Nwasokwa O. Nonlinear dynamics in ventricular fibrillation. Proc Natl Acad Sci U S A 1996 Sep 17;93(19):10495-9.
(149) Hu K, Ivanov PC, Chen Z, Carpena P, Stanley HE. Effect of trends on detrended fluctuation analysis. Phys Rev E Stat Nonlin Soft Matter Phys 2001 Jul;64(1 Pt 1):011114.
(150) Chen PS, Wu TJ, Ting CT, et al. A tale of two fibrillations. Circulation 2003 Nov 11;108(19):2298-303.
(151) Goldberger AL, Amaral LA, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000 Jun 13;101(23):E215-E220.
(152) Povoas HP, Bisera J. Electrocardiographic waveform analysis for predicting the success of defibrillation. Crit Care Med 2000 Nov;28(11 Suppl):N210-N211.
(153) Callaway CW, Sherman LD, Mosesso VN, Jr., Dietrich TJ, Holt E, Clarkson MC. Scaling exponent predicts defibrillation success for out-of-hospital ventricular fibrillation cardiac arrest. Circulation 2001 Mar 27;103(12):1656-61.
(154) Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Physica D: Nonlinear Phenomena 1988 Jun;31(2):277-83.
(155) Brown CG, Dzwonczyk R. Signal analysis of the human electrocardiogram during ventricular fibrillation: frequency and amplitude parameters as predictors of successful countershock. Ann Emerg Med 1996 Feb;27(2):184-8.
(156) Weaver WD, Cobb LA, Dennis D, Ray R, Hallstrom AP, Copass MK. Amplitude of ventricular fibrillation waveform and outcome after cardiac arrest. Ann Intern Med 1985 Jan;102(1):53-5.
(157) Brown CG, Dzwonczyk R, Werman HA, Hamlin RL. Estimating the duration of ventricular fibrillation. Ann Emerg Med 1989 Nov;18(11):1181-5.
(158) Wiggers CJ, Bell JR, Paine M. Studies of ventricular fibrillation caused by electric shock: II. Cinematographic and electrocardiographic observations of the natural process in the dog’’s heart. Its inhibition by potassium and the revival of coordinated beats by calcium. Ann Noninvasive Electrocardiol 2003 Jul;8(3):252-61.

指導教授

黃鍔(Norden E. Huang)

審核日期

2012-8-13

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