以短时心率变异性反映充血性心力衰竭患者自主神经活动的改变

doi:10.3969/j.issn.0258-8021.2016. 02.005

摘要
图/表
参考文献
相关文章 (4)

全文: PDF (872 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要基于短时心率变异性（HRV）分析，探讨充血性心力衰竭（CHF）患者自主神经活动的变化和影响。选用THEW数据库中正常人子数据库作为正常对照组（n=189），对于PhysioNet中两个CHF子数据库的样本（n=44），按照NYHA等级，将NYHA I-II级划分轻度CHF 组（n=12），NYHA III-IV级为重度CHF组（n=32）。对每一个Holter记录选取日间和夜间安静态各5 min的RR间期（RRI）序列，分别进行时域、基于AR模型的频域和去趋势波动（DFA）分析。在正常组、轻度CHF组和重度CHF组等三组中，CHF患者日间的短时分形尺度指数（(α₁)_d）两两比较均有显著性差异，并存在下降趋势（依次分别为1.35±0.21、1.03±0.29和0.81±0.29），反映心率动力学从分形特性转向更随机化的结构。同时，日间HFn((HFn)_d)在三组间的两两比较中均存在显著性差异，并存在上升趋势（依次分别为23.89%±12.78%、37.22%±11.24%和56.30%±15.28%）, 表明CHF导致交感神经和迷走神经交互作用趋于消失。利用夜间RRI（RRI_n）,(HFn)_d和 (α₁)_d等3个指标进行Fisher线性判别，区分正常人和CHF患者的灵敏性和特异性分别为90.91%和92.06%，而区分轻度和重度CHF患者的灵敏性和特异性分别为84.38%和100%。所进行的研究将HRV非线性方法与传统方法相结合评估自主神经状态, 为监测CHF病情或观察治疗效果等潜在的临床应用提供了依据。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	刘红夺儿湛萍王志刚彭屹^#*

关键词 ：心率变异性, 充血性心力衰竭, 去趋势波动分析（DFA）, 自主神经系统

Abstract：This study is aimed to investigate the autonomic modulation and influence in patients with congestive heart failure (CHF) based on shortterm 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 shortterm 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)_dand (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.

Key words： heart rate variability (HRV) congestive heart failure (CHF) detrended fluctuation analysis (DFA) autonomic nervous system (ANS)

基金资助:国家自然科学基金（81071225，81471746）

引用本文:

刘红夺儿湛萍王志刚彭屹^#*. 以短时心率变异性反映充血性心力衰竭患者自主神经活动的改变[J]. 中国生物医学工程学报, 2016, 35(2): 155-161.
Liu Hongduoer Zhan Ping Wang Zhigang Peng Yi^#*. Short-Term Heart Rate Variability for Assessment of Autonomic Nervous Activity in Patients with Congestive Heart Failure. journal1, 2016, 35(2): 155-161.

链接本文:

http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2016. 02.005 或 http://cjbme.csbme.org/CN/Y2016/V35/I2/155

［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 (UKheart)［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 frequencydomain 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 shortterm heart rate variability measures ［J］. PLoS ONE, 2014, 9: e93399.
［17］Pecchia L, Melillo P, Sansone M, et al. Discrimination power of shortterm 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. Agerelated 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 sympathovagal 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 headup 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, Mkikallio TH, YliMyry 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］Perkimki 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］Perkimki JS, Mkikallio TH, Airaksinen KE, et al. Determinants and interindividual variation of RR interval dynamics in healthy middleaged 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.