Heart rate time series: decreased chaos after intravenous lactate and increased non-linearity after isoproterenol in normal subjects

In this study, we reanalyzed our previous heart rate time series data on the effects of intravenous sodium lactate (n=9) and intravenous isoproterenol (n=11) using non-linear techniques. Our prior findings of significantly higher baseline non-linear scores (NL: S(netGS)) and significantly lower largest Lyapunov exponents in supine posture in patients with panic disorder compared to control subjects prompted this study. We obtained the largest Lyapunov exponent (LLE), and a measure of non-linearity (NL: S(netGS)) of heart rate time series. LLE quantifies predictability and NL quantifies the deviation from linear processes. There was a significant increase in NL score, (S(netGS)) after isoproterenol infusions and a significant decrease in LLE (an increase in predictability indicating decreased chaos), after intravenous lactate in supine posture in normal control subjects. Increased NL scores in supine posture after intravenous isoproterenol may be due to a relative increase in cardiac sympathetic activity or a decrease in vagal activity at least in certain circumstances, and an overall decrease in LLE may indicate an impaired cardiac autonomic flexibility after intravenous sodium lactate, as LLE is diminished by autonomic blockade by atropine. Band analysis of LLE (LF/HF) (LF: 0.04-0.15 Hz and HF: 0.15-0.5 Hz) showed an increase of these ratios during either condition with a higher sympathovagal interaction after the drug administration. These findings may throw new light on the association of anxiety and significant cardiovascular events.

[1]  J. Miller,et al.  Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. , 1987, The American journal of cardiology.

[2]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[3]  R. Balon,et al.  Isoproterenol-induced panic attacks , 1988, Biological Psychiatry.

[4]  R Balon,et al.  Fractal dimension of heart rate time series: an effective measure of autonomic function. , 1993, Journal of applied physiology.

[5]  M. Weissman,et al.  Panic disorder and cardiovascular/cerebrovascular problems: results from a community survey. , 1990, The American journal of psychiatry.

[6]  A. N. Sharkovskiĭ Dynamic systems and turbulence , 1989 .

[7]  V. Yeragani,et al.  Decreased chaos and increased nonlinearity of heart rate time series in patients with panic disorder , 2001, Autonomic Neuroscience.

[8]  D J Nutt,et al.  Lactate and hyperventilation substantially attenuate vagal tone in normal volunteers. A possible mechanism of panic provocation? , 1989, Archives of general psychiatry.

[9]  V. Yeragani Heart rate and blood pressure variability: implications for psychiatric research. , 1995, Neuropsychobiology.

[10]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[11]  G. Deco,et al.  A characterization of HRV's nonlinear hidden dynamics by means of Markov models , 1999, IEEE Transactions on Biomedical Engineering.

[12]  M. Fujishima,et al.  Chaos and spectral analyses of heart rate variability during head-up tilting in essential hypertension. , 1999, Journal of the autonomic nervous system.

[13]  R. Glenny,et al.  Applications of fractal analysis to physiology. , 1991, Journal of applied physiology.

[14]  V. Yeragani,et al.  Nonlinear measures of heart period variability: Decreased measures of symbolic dynamics in patients with panic disorder , 2000, Depression and anxiety.

[15]  I Hagerman,et al.  Chaos-related deterministic regulation of heart rate variability in time- and frequency domains: effects of autonomic blockade and exercise. , 1996, Cardiovascular research.

[16]  J. Matías‐Guiu,et al.  Heart rate variability in multiple sclerosis during a stable phase , 1998, Acta neurologica Scandinavica.

[17]  P. Vokonas,et al.  Symptoms of anxiety and risk of coronary heart disease. The Normative Aging Study. , 1994, Circulation.

[18]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[19]  Niels-Henrik Holstein-Rathlou,et al.  Approximate entropy and point correlation dimension of heart rate variability in healthy subjects , 1998, Integrative physiological and behavioral science : the official journal of the Pavlovian Society.

[20]  V. Yeragani,et al.  Effect of age on long-term heart rate variability. , 1997, Cardiovascular research.

[21]  S Cerutti,et al.  Linear and nonlinear dynamics of heart rate variability after acute myocardial infarction with normal and reduced left ventricular ejection fraction. , 1996, The American journal of cardiology.

[22]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[23]  D. Dutt,et al.  Nonlinear measures of heart rate time series: influence of posture and controlled breathing , 2000, Autonomic Neuroscience.

[24]  K. Sakata,et al.  Nine-year follow-up study of heart rate variability in patients with Duchenne-type progressive muscular dystrophy. , 1998, American heart journal.

[25]  V. Yeragani,et al.  Decreased heart-period variability in patients with panic disorder: a study of Holter ECG records , 1998, Psychiatry Research.

[26]  R. Balon,et al.  Lactate sensitivity and cardiac cholinergic function in panic disorder. , 1994, The American journal of psychiatry.

[27]  Yasuji Sawada,et al.  Practical Methods of Measuring the Generalized Dimension and the Largest Lyapunov Exponent in High Dimensional Chaotic Systems , 1987 .

[28]  R. Eykholt,et al.  Estimating the Lyapunov-exponent spectrum from short time series of low precision. , 1991, Physical review letters.

[29]  W. Coryell,et al.  Mortality among outpatients with anxiety disorders. , 1986, The American journal of psychiatry.

[30]  R. Cohen,et al.  An Efficient Algorithm for Spectral Analysis of Heart Rate Variability , 1986, IEEE Transactions on Biomedical Engineering.

[31]  C. Polman,et al.  Cardiovascular autonomic function in multiple sclerosis , 1991, Journal of the Neurological Sciences.

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

[33]  Theiler,et al.  Efficient algorithm for estimating the correlation dimension from a set of discrete points. , 1987, Physical review. A, General physics.

[34]  Bruce J. West,et al.  Fractals in physiology and medicine. , 1987, The Yale journal of biology and medicine.

[35]  J. Kurths,et al.  The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death. , 1996, Cardiovascular research.

[36]  R. Balon,et al.  Effects of lactate on cross-spectral analysis of heart rate, blood pressure, and lung volume in normal volunteers , 1996, Psychiatry Research.

[37]  T. T. Soong,et al.  Random differential equations in science and engineering , 1974 .

[38]  S Cerutti,et al.  Non-linear dynamics and chaotic indices in heart rate variability of normal subjects and heart-transplanted patients. , 1996, Cardiovascular research.

[39]  D. Adam,et al.  Assessment of autonomic function in humans by heart rate spectral analysis. , 1985, The American journal of physiology.

[40]  M. Malik Heart Rate Variability , 1996, Clinical cardiology.

[41]  R. Berger,et al.  Effects of Yohimbine on Heart Rate Variability in Panic Disorder Patients and Normal Controls: A Study of Power Spectral Analysis of Heart Rate , 1992, Journal of Cardiovascular Pharmacology.

[42]  G. Re,et al.  Central Autonomic Dysorganization in the Early Stages of Experimental Meningitis in Rabbits Induced by Complement-C5A-Fragment: A Pathophysiological Validation of the Largest Lyapunov Exponent of Heart Rate Dynamics , 1994 .

[43]  M. Turiel,et al.  Power Spectral Analysis of Heart Rate and Arterial Pressure Variabilities as a Marker of Sympatho‐Vagal Interaction in Man and Conscious Dog , 1986, Circulation research.

[44]  U Zwiener,et al.  Deterministic--chaotic and periodic properties of heart rate and arterial pressure fluctuations and their mediation in piglets. , 1996, Cardiovascular research.

[45]  F. Pitts,et al.  Lactate metabolism in anxiety neurosis. , 1967, The New England journal of medicine.

[46]  R. E. Ganz,et al.  Central cardio-autonomic disorganization in interictal states of epilepsy detected by phase space analysis. , 1994, The International journal of neuroscience.

[47]  L. Cao Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .

[48]  R. Balon,et al.  Effects of isoproterenol infusions on heart rate variability in patients with panic disorder , 1995, Psychiatry Research.

[49]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[50]  R. E. Ganz,et al.  The Lyapunov exponent of heart rate dynamics as a sensitive marker of central autonomic organization: an exemplary study of early multiple sclerosis. , 1993, The International journal of neuroscience.

[51]  M. Ja,et al.  Brainstem lesions decrease heart rate variability. , 2000 .

[52]  Santi Chillemi,et al.  Nonlinearity Tests Using the Extrema of a Time Series , 1998 .

[53]  Mees,et al.  Singular-value decomposition and embedding dimension. , 1987, Physical review. A, General physics.

[54]  D. Levy,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. , 1997, Circulation.

[55]  Richard Balon,et al.  Diminished chaos of heart rate time series in patients with major depression , 2002, Biological Psychiatry.

[56]  M. J. Katz,et al.  Fractals and the analysis of waveforms. , 1988, Computers in biology and medicine.

[57]  R. Cohen,et al.  Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. , 1981, Science.

[58]  Richard Balon,et al.  Decreased heart rate variability in panic disorder patients: A study of power-spectral analysis of heart rate , 1993, Psychiatry Research.

[59]  J. Fleiss,et al.  Frequency Domain Measures of Heart Period Variability and Mortality After Myocardial Infarction , 1992, Circulation.

[60]  Sawada,et al.  Measurement of the Lyapunov spectrum from a chaotic time series. , 1985, Physical review letters.

[61]  A. Malliani,et al.  Cardiovascular Neural Regulation Explored in the Frequency Domain , 1991, Circulation.

[62]  M. Rosenstein,et al.  A practical method for calculating largest Lyapunov exponents from small data sets , 1993 .

[63]  Alfred Ramani,et al.  The Painlevé property and singularity analysis of integrable and non-integrable systems , 1989 .

[64]  F. Takens Detecting strange attractors in turbulence , 1981 .