Abstract：This study is aimed to investigate the autonomic modulation and influence in patients with congestive heart failure (CHF) based on shortterm heart rate variability (HRV) analysis. One dataset from THEW as normal controls (n=189) and two datasets of CHF patients from PhysioNet were selected in this study. According to NYHA class, 44 CHF patients were divided into mild CHF group (NYHA I-II, n=12) and severe CHF group (NYHA III-IV, n=32). Two 5 minute episodes of RR interval (RRI), representing day and night in resting state, were selected in each Holter record. Then, time domain analysis, AR model based frequency domain analysis and detrended fluctuation analysis (DFA) were calculated in each series. The results demonstrated that there were significant differences for shortterm fractal scaling exponent in the day ((α1)d) in any two groups among normal controls, mild CHF group and severe CHF group. Moreover, the declining trend of (α1)d (1.35±0.21, 1.03±0.29 and 0.81±0.29, respectively) showed the change of heart rate dynamics from fractal properties towards random structure. In the meanwhile, significant differences existed for HFn in the day ((HFn)d) in any two groups among the abovementioned three groups. And the sustained increase of (HFn)d (23.89%±12.78%, 37.22%±11.24% and 56.30%±15.28%, respectively) suggested the loss of reciporcal function between sympathetic and vagal branches. Using RRIn(night RRI), (HFn)d and (α1)d , the sensitivity and specificity for discriminating normal people and CHF patients reached 90.91% and 92.06%; Moreover, the sensitivity and specificity for discriminating mild CHF patients and severe CHF patients were 84.38% and 100%. Combining nonlinear analysis and traditional methods, our results provided the basis for clinical use of HRV in monitoring the progress of CHF and testing therapeutic effects.
［1］Mozaffarian D, Benjamin EJ, Go AS, et al. Heart disease and stroke statistics-2016 update a report from the American Heart Association ［EB/OL］．http://circ.ahajournals.org/content/early/2015/12/16/CIR.0000000000000350.full.pdf, 2015-12-16/2015-12-24.
［2］Millar PJ, Murai H, Floras JS. Paradoxical muscle sympathetic reflex activation in human heart failure ［J］. Circulation, 2015, 131:459-468.
［3］Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use［J］. Circulation, 1996, 93: 1043-1065.
［4］Guzzetti S, Magatelli E, Mezzetti S. Heart rate variability in chronic heart failure ［J］. Autonomic Neuroscience: Basic and Clinical, 2001, 90:102-105.
［5］Xhyheri B, Manfrini O, Mazzolini M, et al. Heart rate variability today ［J］. Progress in Cardiovascular Diseases, 2012, 55: 321-331.
［6］Richard G, Sandercock H, Brodie DA. The role of heart rate variability in prognosis for different modes of death in chronic heart failure ［J］. Pace, 2006, 29: 892-904.
［7］Nolan J, Batin PD, Andrews R, et al. Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom heart failure evaluation and assessment of risk trial (UKheart)［J］.Circulation, 1998, 98: 1510-1516.
［8］Moore PKG, Groves D, Kearney MT, et al. HRV spectral power and mortality in chronic heart failure (CHF): 5 years results of the UK heart study［J］. Heart, 2004, 90: A6.
［9］La Rovere MT, Pinna GD, Maestri R, et al. Short term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients［J］. Circulation, 2003, 107(4): 565-570.
［10］Makikallio TH, Huikuri H, Hintze U, et al. Fractal analysis and time and frequencydomain measures of heart rate variability as predictors of mortality in patients with heart failure ［J］. Am J Cardiol, 2001, 87: 178-182.
［11］Dorgin M. Nomenclature and criteria for diagnosis for diseases of the heart and great vessels ［M］. Boston: Little Brown and Company, 1994.
［12］Jessup M, Abraham WT, Casey DE, et al. 2009 focused update: ACCF/AHA guidelines for the diagnosis and management of heart failure in adults a report of the American College of Cardiology Foundation/ American Heart Association Task Force on Practice Guidelines［J］. Circulation, 2009, 119: 1977-2016.
［13］Peng CK, Havlin S, Stanley HE, et al. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series ［J］. Chaos, 1995, 5(1): 82-87.
［14］Liu Chengyu, Li Ke, Zhao Lina, et al. Analysis of heart rate variability using fuzzy measure entropy ［J］. Computers in Biology and Medicine, 2013, 43: 100-108.
［15］Thuraisingham RA. Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals ［J］.Computer Methods and Program in Biomedicine, 2006, 83: 78-82.
［16］Liu Guanzheng, Wang Lei, Wang Qian, et al. A new approach to detect congestive heart failure using shortterm heart rate variability measures ［J］. PLoS ONE, 2014, 9: e93399.
［17］Pecchia L, Melillo P, Sansone M, et al. Discrimination power of shortterm heart rate variability measures for CHF assessment ［J］. IEEE Trans Inf Technol Biomed, 2011, 15: 40-46.
［18］刘红夺儿, 朱逸, 湛萍, 等. 短时非线性方法用于心率变异性分析 ［J］.中国生物医学工程学报，2015, 34（2）:229-236.
［19］Couderc JP. A unique digital electrocardiographic repository for the development of quantitative electrocardiography and cardiac safety: The Telemetric and Holter ECG Warehouse (THEW) ［J］. J Electrocardiol, 2010, 43: 595-600.
［20］Goldberger AL, Amaral LAN, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals ［J］. Circulation, 2000, 101: 215-220.
［21］Iyengar N, Peng CK, Morin R, et al. Agerelated alterations in the fractal scaling of cardiac interbeat interval dynamics ［J］. Am J Physiol, 1996: 271: 1078-1084.
［22］Boardman A, Schlindwein FS, Rocha AP, et al. A study on the optimum order of autoregressive models for heart rate variability ［J］. Physiol Meas,2002, 23: 325-336.
［23］Pagani M, Lombardi F, Guzzetti S, et al. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympathovagal interaction in man and conscious dog ［J］. Circ Res, 1986, 59: 178\|193.
［24］Guzzetti S, Borroni E, Garbelli PE, et al. Symbolic dynamics of heart rate variability: a probe to investigate cardiac autonomic modulation ［J］. Circulation, 2005, 112: 465-470.［25］Porta A, Tobaldini E, Guzzetti S, et al. Assessment of cardiac autonomic modulation during graded headup tilt by symbolic analysis of heart rate variability ［J］. American Journal of Physiology Heart and Circulatory Physiology, 2007, 293: 702-708.
［26］Tulppo MP, Kiviniemi AM, Hautala AJ, et al. Physiological background of the loss of fractal heart rate ［J］. Circulation, 2005, 112: 314-319.
［27］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［J］. Circulation, 1997, 96:842-848.
［28］Vikman S, Mkikallio TH, YliMyry S, et al. Altered complexity and correlation properties of R-R interval dynamics before the spontaneous onset of paroxysmal atrial fibrillation［J］. Circulation, 1999, 100:2079-2084.
［29］Huikuri HV, Poutiainen AM, Makikallio TH, et al. Dynamic behaviour and autonomic regulation of ectopic atrial pacemakers ［J］. Circulation, 1999, 100:1416-1422.
［30］Perkimki JS, Zareba W, Ruta J, et al. Comparability of nonlinear measures of heart rate variability between long and short term electrocardiographic recordings［J］. Am J Cardiol, 2001, 87:905-908.
［31］Perkimki JS, Mkikallio TH, Airaksinen KE, et al. Determinants and interindividual variation of RR interval dynamics in healthy middleaged subjects ［J］. Am J Physiol Heart Circ Physiol, 2001, 280: H1400-H1406.
［32］Wellens HJJ, Schwartz PJ, Lindemans FW, et al. Risk stratification for sudden cardiac death: current status and challenges for the future ［J］. European Heart Journal, 2014, 35: 1642-1651.