Complexity of the autonomic heart rate control in coronary artery occlusion in patients with and without prior myocardial infarction.

Autonomic nervous system (ANS) is governed by complex interactions arising from feedback loops of nonlinear systems that operate over a wide range of temporal and spatial scales, enabling the organism to adapt to stress, metabolic changes and diseases. This study is aimed to assess multifractal and nonlinear characteristics of the ANS during ischemic events provoked by a prolonged percutaneous coronary intervention (PCI) procedure. Eighty-seven patients from the STAFF III database were used. Patients were classified into 2 groups: (1) with prior myocardial infarction (MI) and (2) without MI (noMI). R-R signals during three 3-min stages of the procedures were analyzed using multifractal and surrogate data techniques. Multifractal indices increased significantly from the pre-inflation stage to the post-deflation stage. These variations were more marked for the noMI group. Multifractal changes significantly correlated with both the decreased parasympathetic and the increased sympathetic modulations accounted by classical linear indices. Multifractal measures resulted to be a more powerful indicator than linear HRV indices in quantifying the ischemia-induced changes. Right coronary artery (RCA) occlusions provoke greater multifractal reactions throughout the PCI procedure. Our findings suggest reduced complex multifractal and nonlinear reactions of ANS activity in patients with prior MI in comparison to the noMI group, possibly due to degradation in the complexity of control mechanism of heart rate generation.

[1]  Zdobysław Goraj,et al.  Basic mathematical relations of fluid dynamics for modified panel methods , 1998 .

[2]  Joanna Wdowczyk-Szulc,et al.  Reading multifractal spectra: Aging by multifractal analysis of heart rate , 2011 .

[3]  Danuta Makowiec,et al.  Long-range dependencies in heart rate signals—revisited , 2005, q-bio/0510044.

[4]  K. Umeno,et al.  Cardiac sympathetic denervationmodulates the sympathoexcitatoryresponse to acute myocardial ischemia , 2002 .

[5]  Ramón González-Camarena,et al.  Applying fractal analysis to short sets of heart rate variability data , 2009, Medical & Biological Engineering & Computing.

[6]  E. Feigl,et al.  Adrenergic vasoconstriction lessens transmural steal during coronary hypoperfusion. , 1986, The American journal of physiology.

[7]  Junichiro Hayano,et al.  1/f scaling in heart rate requires antagonistic autonomic control. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Tapio Seppänen,et al.  Frequency Domain Measures of Heart Rate Variability Before the Onset of Nonsustained and Sustained Ventricular Tachycardia in Patients With Coronary Artery Disease , 1993, Circulation.

[9]  P. Stein,et al.  Assessment of autonomic control of the heart during transient myocardial ischemia. , 2012, Journal of electrocardiology.

[10]  L. Hejjel,et al.  Heart rate variability analysis. , 2001, Acta physiologica Hungarica.

[11]  C. Pizzi,et al.  Changes in autonomic nervous system activity: spontaneous versus balloon-induced myocardial ischaemia. , 2004, European heart journal.

[12]  Luís A. Nunes Amaral,et al.  From 1/f noise to multifractal cascades in heartbeat dynamics. , 2001, Chaos.

[13]  H. Kantz,et al.  Hölder-exponent-based test for long-range correlations in pseudorandom sequences , 2006 .

[14]  J. Alpert,et al.  Universal definition of myocardial infarction. , 2007, European heart journal.

[15]  N. Montano,et al.  Complexity and Nonlinearity in Short-Term Heart Period Variability: Comparison of Methods Based on Local Nonlinear Prediction , 2007, IEEE Transactions on Biomedical Engineering.

[16]  K. Umeno,et al.  Cardiac sympathetic denervation modulates the sympathoexcitatory response to acute myocardial ischemia. , 2002, Journal of the American College of Cardiology.

[17]  Alberto Malliani,et al.  Principles of Cardiovascular Neural Regulation in Health and Disease , 2012, Basic Science for the Cardiologist.

[18]  T. Seppänen,et al.  Physiological Background of the Loss of Fractal Heart Rate Dynamics , 2005, Circulation.

[19]  Charles W. Therrien,et al.  Discrete Random Signals and Statistical Signal Processing , 1992 .

[20]  Piet M. T. Broersen,et al.  Finite sample criteria for autoregressive order selection , 2000, IEEE Trans. Signal Process..

[21]  F Lombardi,et al.  Sudden cardiac death: role of heart rate variability to identify patients at risk. , 2001, Cardiovascular research.

[22]  G. Breithardt,et al.  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. , 1996 .

[23]  K. Torre,et al.  Fractal analyses for 'short' time series: A re-assessment of classical methods , 2006 .

[24]  H. Stanley,et al.  Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.

[25]  H. Stanley,et al.  Multiscale aspects of cardiac control , 2004 .

[26]  P. Caminal,et al.  Multifractal and nonlinear assessment of autonomous nervous system response during transient myocardial ischaemia , 2010, Physiological measurement.

[27]  T. Ehring,et al.  Impact of alpha-adrenergic coronary vasoconstriction on the transmural myocardial blood flow distribution during humoral and neuronal adrenergic activation. , 1993, Circulation research.

[28]  H. Huikuri,et al.  Heart rate variability and occurrence of ventricular arrhythmias during balloon occlusion of a major coronary artery. , 1999, The American journal of cardiology.

[29]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[30]  G S Wagner,et al.  The Selvester QRS scoring system for estimating myocardial infarct size. The development and application of the system. , 1985, Archives of internal medicine.

[31]  H. L. Stone,et al.  Autonomic mechanisms in ventricular fibrillation induced by myocardial ischemia during exercise in dogs with healed myocardial infarction. An experimental preparation for sudden cardiac death. , 1984, Circulation.

[32]  O. Pahlm,et al.  Comparison of ST-segment deviation to scintigraphically quantified myocardial ischemia during acute coronary occlusion induced by percutaneous transluminal coronary angioplasty. , 2006, The American journal of cardiology.

[33]  J. Alpert,et al.  Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction , 2008 .

[34]  Blas Echebarria,et al.  Characterization of the nonlinear content of the heart rate dynamics during myocardial ischemia. , 2009, Medical engineering & physics.

[35]  A. Luna Location of Q-wave myocardial infarction in the era of cardiac magnetic resonance imaging techniques , 2006 .

[36]  A Malliani,et al.  A sympathetic reflex elicited by experimental coronary occlusion. , 1969, The American journal of physiology.

[37]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[38]  Joanna Wdowczyk-Szulc,et al.  Comparison of Wavelet Transform Modulus Maxima and Multifractal Detrended Fluctuation Analysis of Heart Rate in Patients with Systolic Dysfunction of Left Ventricle , 2008, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[39]  A. Malliani,et al.  Heart rate variability signal processing: a quantitative approach as an aid to diagnosis in cardiovascular pathologies. , 1987, International journal of bio-medical computing.

[40]  Jacques Duchêne,et al.  Fractal time series analysis of postural stability in elderly and control subjects , 2007, Journal of NeuroEngineering and Rehabilitation.

[41]  N. Wessel,et al.  Evaluation of renormalised entropy for risk stratification using heart rate variability data , 2000, Medical and Biological Engineering and Computing.

[42]  C. Peng,et al.  What is physiologic complexity and how does it change with aging and disease? , 2002, Neurobiology of Aging.

[43]  A. Bayés de Luna Location of Q-wave myocardial infarction in the era of cardiac magnetic resonance imaging techniques: an update. , 2007, Journal of Electrocardiology.

[44]  Pere Caminal,et al.  Methods derived from nonlinear dynamics for analysing heart rate variability , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[45]  H. Rockman,et al.  Effect of α‐Adrenergic Stimulation on Regional Contractile Function and Myocardial Blood Flow With and Without Ischemia , 1991, Circulation.

[46]  Schreiber,et al.  Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.

[47]  M. R. Ortiz,et al.  Comparison of RR-interval scaling exponents derived from long and short segments at different wake periods , 2006, Physiological measurement.

[48]  S Van Huffel,et al.  Nonlinear heart rate dynamics: circadian profile and influence of age and gender. , 2012, Medical engineering & physics.

[49]  R. Riener,et al.  Journal of Neuroengineering and Rehabilitation Open Access Biofeedback for Robotic Gait Rehabilitation , 2022 .

[50]  F. Schlindwein,et al.  A study on the optimum order of autoregressive models for heart rate variability. , 2002, Physiological measurement.