Assessment of the autonomic control of heart rate variability in healthy and spinal-cord injured subjects: contribution of different complexity-based estimators

We investigated how complexity-based estimators of heart rate variability can detect changes in cardiovascular autonomic drive with respect to traditional measures of variability. This was done by analyzing healthy subjects and paraplegic patients with different autonomic impairment due to low (vascular impairment only) or high (cardiac and vascular impairment) spinal cord injury, during progressive autonomic activations. While traditional techniques only quantified the effects of the autonomic activation, not distinguishing the effects of the lesion level, some recently proposed complexity estimators could also reveal the pathologic alterations in the autonomic control of heart rate. These estimators included the detrended fluctuation analysis coefficient (sensitive to both low and high autonomic lesions), sample entropy (sensitive to low-level lesions) and the largest Lyapunov exponent (sensitive to high-level lesions). Thus complexity-based methods provide information on the autonomic function from the heart rate dynamics that cannot be obtained by traditional techniques. This finding supports the combined use of both complexity-based and traditional methods to investigate the autonomic cardiovascular control from a more comprehensive perspective.

[1]  S. Miyake,et al.  Assessment of autonomic function in traumatic quadriplegic and paraplegic patients by spectral analysis of heart rate variability. , 1995, Journal of the autonomic nervous system.

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

[3]  J. Bigger,et al.  Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction , 1998, The Lancet.

[4]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[5]  Keith Willson,et al.  A direct analytical demonstration of the essential equivalence of detrended fluctuation analysis and spectral analysis of RR interval variability. , 2003, Physiological measurement.

[6]  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.

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

[8]  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.

[9]  C. Mathias,et al.  Cardiovascular control in spinal man. , 1988, Annual review of physiology.

[10]  Peter J. Schwartz,et al.  Baroreflex Sensitivity and Heart Rate Variability in the Identification of Patients at Risk for Life-Threatening Arrhythmias Implications for Clinical Trials , 2001 .

[11]  H E Stanley,et al.  Scaling and universality in heart rate variability distributions. , 1998, Physica A.

[12]  J. Fleiss,et al.  Components of heart rate variability measured during healing of acute myocardial infarction. , 1988, The American journal of cardiology.

[13]  M. Emdin,et al.  Hyperinsulinemia and Autonomic Nervous System Dysfunction in Obesity: Effects of Weight Loss , 2001, Circulation.

[14]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[15]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[16]  A. Malliani,et al.  Spectral analysis of heart rate variability in the assessment of autonomic diabetic neuropathy. , 1988, Journal of the autonomic nervous system.

[17]  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 .

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

[19]  Holger Kantz,et al.  Practical implementation of nonlinear time series methods: The TISEAN package. , 1998, Chaos.

[20]  N P Chau,et al.  Relationship between diabetic autonomic dysfunction and heart rate variability assessed by recurrence plot. , 1997, The American journal of physiology.

[21]  J. Bassingthwaighte,et al.  Evaluation of the dispersional analysis method for fractal time series , 1995, Annals of Biomedical Engineering.

[22]  A L Goldberger,et al.  On a mechanism of cardiac electrical stability. The fractal hypothesis. , 1985, Biophysical journal.

[23]  M. Kumashiro,et al.  Assessment of autonomic function in myotonic dystrophy by spectral analysis of heart-rate variability. , 1995, Journal of the autonomic nervous system.

[24]  A. Goldberger Fractal mechanisms in the electrophysiology of the heart , 1992, IEEE Engineering in Medicine and Biology Magazine.

[25]  J. V. van Beek,et al.  Four methods to estimate the fractal dimension from self-affine signals (medical application) , 1992, IEEE Engineering in Medicine and Biology Magazine.

[26]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[27]  P. Almenoff,et al.  Sympathovagal balance of the heart in subjects with spinal cord injury. , 1997, The American journal of physiology.

[28]  A J Camm,et al.  Baroreflex Sensitivity and Heart Rate Variability in the Identification of Patients at Risk for Life-Threatening Arrhythmias: Implications for Clinical Trials , 2001, Circulation.

[29]  J. Nolan,et al.  Heart rate variability and cardiac failure , 1999, Heart.

[30]  S Cerutti,et al.  Short and long term non-linear analysis of RR variability series. , 2002, Medical engineering & physics.

[31]  G. Diamond,et al.  Fascinating rhythm: a primer on chaos theory and its application to cardiology. , 1990, American heart journal.