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Normalized correlation dimension for heart rate variability analysis

  • Corinna Raab , Jürgen Kurths , Alexander Schirdewan and Niels Wessel
Published/Copyright: October 25, 2006
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Biomedical Engineering / Biomedizinische Technik
From the journal Volume 51 Issue 4

Abstract

In this paper we use the concept of large-scale dimension densities to analyze heart rate variability data. This method uses a normalized Grassberger-Procaccia algorithm and estimates the dimension in the rather large scales of the system. This enables us to analyze very short data. First we reanalyze data from the CIC 2002 challenge and can completely distinguish between real data and computer-generated data using only one parameter. We then analyze unfiltered data for 15 patients with atrial fibrillation (AF), 15 patients with congestive heart failure (CHF), 15 elderly healthy subjects, and 18 young healthy subjects. This method can completely separate the AF group from the other groups and the CHF patients show significant differences compared to the young and elderly healthy volunteers. Furthermore, differences are evident in the dimensionality between day and night for healthy persons, but not for the CHF patients. Finally, the results are compared to standard heart rate variability parameters.


Corresponding author: Dr. Niels Wessel, Universität Potsdam, Institut für Physik, AG NLD–Kardiovaskuläre Physik, Postfach 601553, 14415 Potsdam, Germany Phone: +49-331-9771639 or +49-30-94172450 Fax: +49-331-977-1142

References

1 Barron HV, Lesh MD. Autonomic nervous system and sudden cardiac death. J Am Coll Cardiol1996; 27: 1053–1060.10.1016/0735-1097(95)00615-XSearch in Google Scholar

2 Engbert R, Schiekand M, Scheffczyk C, et al. Symbolic dynamics of physiological synchronisation: examples from bimanual movements and cardiorespiratory interaction. Nonlin Anal Theory Methods Appl1997; 30: 973–984.10.1016/S0362-546X(96)00137-XSearch in Google Scholar

3 Glass L. Synchronization and rhythmic processes in physiology. Nature2001; 410: 277–284.10.1038/35065745Search in Google Scholar PubMed

4 Glass L, Kaplan D. Time series analysis of complex dynamics in physiology and medicine. Med Prog Technol1993; 19: 115–128.Search in Google Scholar

5 Goldberger AL, Rigney DR, Mietus J, Antman EM, Greenwald S. Nonlinear dynamics in sudden cardiac death syndrome: heart rate oscillations and bifurcations. Experientia1988; 44: 983–987.10.1007/BF01939894Search in Google Scholar PubMed

6 Goldberger AL, Amaral L, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation2000; 101: E215.10.1161/01.CIR.101.23.e215Search in Google Scholar

7 Grassberger P, Procaccia I. Characterization of strange attractors. Phys Rev Lett1983; 50: 346–349.10.1103/PhysRevLett.50.346Search in Google Scholar

8 http://www.physionet.org/challenge/2002/. RR interval time series modeling: a challenge from PhysioNet and Computers in Cardiology 2002.Search in Google Scholar

9 Kantz H, Schreiber T. Nonlinear time series analysis. Cambridge: Cambridge University Press 1997.Search in Google Scholar

10 Kurths J, Voss A, Witt A, Saparin P, Kleiner HJ, Wessel N. Quantitative analysis of heart rate variability. Chaos1995; 5: 88–94.10.1063/1.166090Search in Google Scholar PubMed

11 Raab C, Kurths J. Estimation of large-scale dimension densities. Phys Rev E2001; 64: 016216.10.1103/PhysRevE.64.016216Search in Google Scholar PubMed

12 Raab C, Wessel N, Schirdewan A, Kurths J. Large-scale dimension densities for heart rate variability analysis. Comput Cardiol2005; 32: 985–988.10.1109/CIC.2005.1588274Search in Google Scholar

13 Raab C, Wessel N, Schirdewan A, Kurths J. Large-scale dimension densities for heart rate variability analysis. Phys Rev E2006; 73: 041907.10.1103/PhysRevE.73.041907Search in Google Scholar PubMed

14 Rapp PE, Cellucci CJ, Korslund KE, Watanabe TA, Jimenez-Montano MA. Effective normalization of complexity measurements for epoch length and sampling frequency. Phys Rev E2001; 64: 16209.10.1103/PhysRevE.64.016209Search in Google Scholar

15 Schäfer C, Rosenblum MG, Kurths J, Abel HH. Heartbeat synchronized with ventilation. Nature1998; 392: 239–240.10.1038/32567Search in Google Scholar

16 Schwarz U, Benz AO, Kurths J, Witt A. Analysis of solar spike events by means of symbolic dynamics methods. Astron Astrophys1993; 277: 215–224.Search in Google Scholar

17 Voss A, Kurths J, Kleiner HJ, et al. The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death. Cardiovasc Res1996; 31: 419–433.10.1016/S0008-6363(96)00008-9Search in Google Scholar

18 Wackerbauer R, Witt A, Atmanspacher H, Kurths J, Scheingraber H. A comparative classification of complexity measures. Chaos Solitons Fractals1994; 4: 133–173.10.1016/0960-0779(94)90023-XSearch in Google Scholar

19 Wessel N, Malberg H, Mayerfeldt U, Schirdewan A, Kurths J. Classifying simulated and physiological heart rate variability signals. Comput Cardiol2002; 29: 133–135.Search in Google Scholar

20 Wessel N, Schirdewan A, Kurths J. Intermittently decreased beat-to-beat variability in congestive heart failure. Phys Rev Lett2003; 91: 119801.10.1103/PhysRevLett.91.119801Search in Google Scholar PubMed

21 Wessel N, Voss A, Malberg H, et al. Nonlinear analysis of complex phenomena in cardiological data. Herzschr Elektrophys2000; 11: 159–173.10.1007/s003990070035Search in Google Scholar

Published Online: 2006-10-25
Published in Print: 2006-10-01

©2006 by Walter de Gruyter Berlin New York

Articles in the same Issue

  1. ESGCO 2006 Conference and Meeting of the European Study Group on Cardiovascular Oscillations, Jena, Germany, May 15–17, 2006
  2. Cardiovascular Oscillations: from methods and models to clinical applications
  3. Circadian and ultradian rhythms in heart rate variability
  4. Influence of age, body mass index, and blood pressure on the carotid intima-media thickness in normotensive and hypertensive patients
  5. Multivariate and multidimensional analysis of cardiovascular oscillations in patients with heart failure
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  7. Role of the autonomic nervous system in generating non-linear dynamics in short-term heart period variability
  8. Non-linear dynamic analysis of the cardiac rhythm during transient myocardial ischemia
  9. Complex autonomic dysfunction in cardiovascular, intensive care, and schizophrenic patients assessed by autonomic information flow
  10. Low HRV entropy is strongly associated with myocardial infarction
  11. Revisiting the potential of time-domain indexes in short-term HRV analysis
  12. Fractal dimension in health and heart failure
  13. Spatiotemporal correlation analyses: a new procedure for standardisation of DC magnetocardiograms
  14. Changes in heart rate variability of athletes during a training camp
  15. The missing link between cardiovascular rhythm control and myocardial cell modeling
  16. Model of the sino-atrial and atrio-ventricular nodes of the conduction system of the human heart
  17. Modelling long-term heart rate variability: an ARFIMA approach
  18. Clinical correlates of non-linear indices of heart rate variability in chronic heart failure patients
  19. Recurrence analysis of nocturnal heart rate in sleep apnea patients
  20. Normalized correlation dimension for heart rate variability analysis
  21. Complexity of heart rate fluctuations in near-term sheep and human fetuses during sleep
  22. Differences between heart rate and blood pressure variability in schizophrenia
  23. Influence of sympathetic vascular regulation on heart-rate scaling structure: spinal cord lesion as a model of progressively impaired autonomic control
  24. Increase in regularity of fetal heart rate variability with age
  25. Fetal heart rate variability in growth restricted fetuses
  26. Frequency modulation between low- and high-frequency components of the heart rate variability spectrum
  27. Mixed predictability and cross-validation to assess non-linear Granger causality in short cardiovascular variability series
  28. Assessment of spatial organization in the atria during paroxysmal atrial fibrillation and adrenergic stimulation
  29. Attenuated autonomic function in multiple organ dysfunction syndrome across three age groups
  30. Central vasopressin V1a and V1b receptors modulate the cardiovascular response to air-jet stress in conscious rats
  31. Heart rate asymmetry by Poincaré plots of RR intervals
  32. Analyses of cardiovascular oscillations for enhanced diagnosis and risk stratification in cardiac diseases and disorders
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