短时非线性方法用于心率变异性分析

doi:10.3969/j.issn.0258-8021. 2015. 02.014

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Abstract Nonlinear analysis brings about new insights into heart rate variability (HRV) changes under various physiological and pathological conditions, providing additional prognostic information and complementing traditional time and frequency domain analyses. Shortterm nonlinear analysis conforms to the nonlinearity and nonstationarity of heart rate dynamics. Several dominant methods, such as symbolic dynamics analysis, shortterm fractal scaling exponent (α1) analyzed by the detrended fluctuation analysis method, approximation entropy and sample entropy, recurrence quantification analysis, are introduced for their application to shortterm RR series. Furthermore, this article reviews some expansion of application, including shortterm nonlinear analysis coupled with longterm RR series, as well as risk stratification based on multidomain indexes including shortterm nonlinear ones. Several issues concerning the wide spread clinical use of HRV analysis were discussed.

Key words： heart rate variability (HRV) nonlinear analysis shortterm series cardiac autonomic nerve

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	Liu Hongduoer Zhu Yi Zhan Ping Wang Zhigang Peng Yi^#*

Cite this article:

Liu Hongduoer Zhu Yi Zhan Ping Wang Zhigang Peng Yi^#*. The Application of ShortTerm Nonlinear Methods to Heart Rate Variability Analysis[J]. journal1, 2015, 34(2): 229-236.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2015. 02.014 OR http://cjbme.csbme.org/EN/Y2015/V34/I2/229

［1］Goldberger JJ, Cain ME, Hohnloser SH, et al. AHA/ACCF/Heart Rhythm Society Scientific Statement on Noninvasive Risk Stratification Techniques for Identifying Patients at Risk for Sudden Cardiac Death: A Scientific Statement from the American Heart Association Council on Clinical on Cardiology Committee on Electrocardiography and Arrhythmias and Council on Epidemiology and Prevention. Circulation, 2008, 118: 1497-1518.
［2］Grassi G, Esler M. How to assess sympathetic activity in humans ［J］. J Hypertens,1999, 17: 719-734.
［3］Shen MJ, Choi EK, Tan AY, et al. Patterns of baseline autonomic nerve activity and the development of pacinginduced sustained atrial fibrillation ［J］. Heart Rhythm, 2011, 8:583-589.
［4］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.
［5］Voss A, Schulz S, Schroeder R, et al. Methods derived from nonlinear dynamics for analyzing heart rate variability ［J］. Philosophical Transactions of the Royal Society A, 2009, 367: 277-296.
［6］Xhyheri B, Manfrini O, Mazzolini M, et al. Heart rate variability today ［J］. Progress in Cardiovascular Diseases, 2012, 55: 321-331.
［7］Huikuri HV, Perkiomaki JS, Maestri R, et al. Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics ［J］. Philos Transact A Math Phys Eng Sci, 2009, 367: 1223-1238.
［8］Voss A, Schroeder R, Vallverdú M, et al. Shortterm vs longterm heart rate variability in ischemic cardiomyopathy risk stratification ［J］. Frontiers in Physiology, 2013, 4: 1-15.
［9］Voss A, Dietz R, Fiehring H, et al. High resolution ECG, heart rate variability and nonlinear dynamics: tools for high risk stratification ［C］// Computers in Cardiology. Los Alamitos: IEEE Computer Society Press, 1993: 261-264.
［10］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.
［11］Moura-Tonello SCG, Takahashi ACM, Francisco CO, et al. Influence of type 2 diabetes on symbolic analysis and complexity of heart rate variability in men ［J］. Diabetology & Metabolic Syndrome, 2014, 6: 13-23.
［12］Guzzetti S, Borroni E, Garbelli PE, et al. Symbolic dynamics of heart rate variability: A probe to investigate cardiac autonomic ［J］. Circulation, 2005, 〖STHZ〗112: 465-470.
［13］Liu Yuchen, Hung Chisheng, Wu Yenwen, et al. Influence of nonalcoholic fatty liver disease on autonomic changes evaluated by the time domain, frequency domain, and symbolic dynamics of heart rate variability ［J］. PLoS ONE, 2013,
8(4): 1-6.
［14］张靖，李心雅，彭屹. 心肌缺血中心率变异性的符号动力学分析［J］. 国际生物医学工程学杂志，2010，33（1）:31-35.
［15］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.［16］Goldberger AL, Amaral LA, Hausdorff JM, et al. Fractal dynamics in physiology: alterations with disease and aging ［J］. Proc Natl Acad Sci USA, 2002, 99:2466-2472.
［17］Tulppo MP, Kiviniemi AM, Hautala AJ, et al. Physiological background of the loss of fractal heart rate ［J］. Circulation, 2005,112: 314-319.
［18］Bhaskar R, Sobhendu G. Nonlinear methods to assess changes in heart rate variability in type 2 diabetic patients ［J］. Arq Bras Cardiol, 2013, 101(4):317-332.
［19］Castiglioni P, Parati G, Di Rienzo M, et al. Scale exponents of blood pressure and heart rate during autonomic blockade as assessed by detrended fluctuation analysis ［J］. J Physiol, 2011, 589(2): 355-369.
［20］Coronada AV, Carpena P. Size effect on correlation measures ［J］. Journal of Biological Physics, 2005, 31: 121-133.
［21］Richman JS, Moorman JR. Physiological timeseries analysis using approximate and sample entropy ［J］. Am J Physiol, 2000, 278: 2039-2049.
［22］Porta A, GnecchiRuscone T, Tobaldini E, et al. Progressive decrease of heart period variability entropy based complexity during graded headup tilt ［J］. J Appl Physiol, 2007, 〖STHZ〗103〖STBZ〗: 1143-1149.
［23］Weissman A, Zimmer EZ, Aranovitch M, et al. Heart rate dynamics during acute pain in newborns ［J］. Pflugers ArchEur J Physiol, 2012, 464:593-599.
［24］Agiovlasitis S, Baynard T, Pitett KH, et al. Heart rate complexity in response to upright tilt in persons with Down syndrome ［J］. Research in Developmental Disabilities, 2011, 32:2102-2107.
［25］Kanaley JA, Goulopoulou S, Franklin RM, et al. Plasticity of heart rate signaling and complexity with exercise training in obese individuals with and without type 2 diabetes ［J］. Int J Obes (Lond), 2009, 33(10):1198-1206.
［26］Weippert M, Behrens K, Rieger A, et al. Heart rate variability and blood pressure during dynamic and static exercise at similar heart rate levels ［J］. PLOS ONE, 2013, 8(12):1-8.
［27］Yentes JM, Hunt N, Schmid KK, et al. The appropriate use of approximate entropy and sample entropy with short data sets ［J］. Annals of Biomedical Engineering, 2013, 4(2): 349-365.
［28］Chon KH, Scully CG, Lu S. Approximate entropy for all signals ［J］. IEEE Eng Med Biol, 2009, 28:18-23.
［29］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.
［30］刘大钊, 李瑜, 李锦. 以基于基本尺度熵的心率变异性指标表征生理和病理原因引起的自主神经活动变化［J］. 中国生物医学工程学学报，2014，33（4）:402-409.
［31］Gonzáleza H, Infanteb O, PérezGrovas H, et al. Nonlinear dynamics of heart rate variability in response to orthostatism and hemodialysis in chronic renal failure patients: Recurrence analysis approach ［J］. Medical Engineering & Physics, 2013(35): 178-187.
［32］Javorka M, Turianikova Z, Tonhajzerova I, et al. The effect of orthostasison recurrence quantification analysis of heart rate and blood pressure dynamics ［J］. Physiological Measurement, 2009, 30:29-41.
［33］Peng Yi, Sun Zhongwei. Characterization of QT and RR interval series during acute myocardial ischemia by means of recurrence quantification analysis ［J］. Medical & Biological Engineering & Computing, 2011, 49(1):25-31.
［34］Navoret N, Jacquir S, Laurent G, et al. Detection of complex fractionated atrial electrograms using recurrence quantification analysis ［J］. IEEE Transactions on Biomedical Engineering, 2013, 60(7): 1975-1982.
［35］Zbilut JP, Thomasson N, Webber CL. Recurrence quantification analysis as a tool for nonlinear exploration of nonstationary cardiac signals ［J］. Medical Engineering & Physics, 2002(24): 53-60.
［36］Kiyono K, Struzik ZR, Aoyagi N, et al. Multiscale probability density function analysis: nonGaussian and scaleinvariant fluctuations of healthy human heart rate ［J］. IEEE Transactions on Biomedical Engineering, 2006, 53(1): 95-102.
［37］Kiyono K, Hayano J, Watanabe, E, et al. NonGaussian heart rate as an independent predictor of mortality in patients with chronic heart failure［J］. Heart Rhythm, 2008, 5:261-268.
［38］Hayano J, Kiyono K, Struzik ZR, et al. Increased nonGaussianity of heart rate variability predicts cardiac mortality after an acute myocardial infarction ［J］. Front Physio, 2011, 2:65.
［39］Ding H, Crozier S, Wilson S. A new heart rate variability analysis method by means of quantifying the variation of nonlinear dynamic patters ［J］. IEEE Transactions on Biomedical Engineering, 2007, 54(9):1590-1597.
［40］Mourot L. Could nonlinear heart rate variability analysis of short RR intervals series give clinically valuable information in heart disease? ［J］. J Clin Exp Res Cardiol, 2014, 1(1): 104.
［41］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(4): e93399.
［42］Ebrahimzadeh E, Pooyan M, Bijar A, et al. A novel approach to predict sudden cardiac death (SCD) using nonlinear and timefrequency analyses from HRV signals［J］. PLoS ONE, 2014, 9(2): e81896.
［43］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.