Home Fractal dimension in health and heart failure
Article
Licensed
Unlicensed Requires Authentication

Fractal dimension in health and heart failure

  • Frank Beckers , Bart Verheyden , Kurt Couckuyt and André E. Aubert
Published/Copyright: October 25, 2006
Become an author with De Gruyter Brill
Biomedical Engineering / Biomedizinische Technik
From the journal Volume 51 Issue 4

Abstract

Background: Non-linear analysis of heart rate variability (HRV) can give additional information about autonomic control of the heart rate. This study applied the fractal dimension (FD) in a congestive heart failure (CHF) population.

Methods: FD and HRV were evaluated in a healthy population (n=21) and an end-stage heart failure population (n=21) using 1-h segments during the day and night from Holter recordings.

Results: CHF patients presented a loss of circadian variation in both FD and conventional time- and frequency-domain HRV indices. FD was higher in the CHF population both during the day and night. In the CHF population the correlation between FD and high-frequency power of HRV was lost.

Conclusion: Day-night variations of heart rate fluctuations are lost in heart failure. Changes in FD reflecting physiological and pathophysiological changes were observed.


Corresponding author: Frank Beckers, Laboratory of Experimental Cardiology, Campus Gasthuisberg O/N 1, Herestraat 49, 3000 Leuven, Belgium Phone: +32-16-330022 Fax: +32-16-345844

References

1 Akselrod S. Spectral analysis of fluctuations in cardiovascular parameters: a quantitative tool for the investigation of autonomic control. Trends Pharmacol Sci1988; 9: 6–9.10.1016/0165-6147(88)90230-1Search in Google Scholar

2 Aubert AE, Ramaekers D. Neurocardiology: the benefits of irregularity. The basics of methodology, physiology and current clinical applications. Acta Cardiol1999; 54: 107–120.Search in Google Scholar

3 Beckers F. Linear and non-linear analysis of cardiovascular variability: validation and clinical applications. Leuven: Leuven University Press 2002.Search in Google Scholar

4 Grassberger P, Procaccia I. Measuring the strangeness of strange attractors. Physica D1983; 9: 189–208.10.1016/0167-2789(83)90298-1Search in Google Scholar

5 Katz MJ. Fractals and the analysis of waveforms. Comput Biol Med1988; 18: 145–156.10.1016/0010-4825(88)90041-8Search in Google Scholar

6 Rosenstein MT, Collins JJ, De Luca CJ. A practical method for calculating the largest Lyapunov exponents from small data sets. Physica D1993; 65: 117–134.10.1016/0167-2789(93)90009-PSearch in Google Scholar

7 Persson PB, Wagner CD. General principles of chaotic dynamics. Cardiovasc Res1996; 31: 332–341.10.1016/S0008-6363(96)00006-5Search in Google Scholar

8 Yamamoto Y, Hughson RL. On the fractal nature of heart rate variability in humans: effects of data length and beta-adrenergic blockade. Am J Physiol1994; 266: R40–R49.10.1152/ajpregu.1994.266.1.R40Search in Google Scholar

9 Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA1991; 88: 2297–2301.10.1073/pnas.88.6.2297Search in Google Scholar

10 Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos1995; 5: 82–87.10.1063/1.166141Search in Google Scholar

11 Mandelbrot BB. Fractals: form, chance, dimension. New York: WH Freeman 1977.Search in Google Scholar

12 Mandelbrot BB. The fractal geometry of nature. New York: WH Freeman 1982.Search in Google Scholar

13 Goldberger AL, West BJ. Fractals in physiology and medicine. Yale J Biol Med1987; 60: 421–435.Search in Google Scholar

14 Denton TA, Diamond GA, Helfant RH, Khan S, Karagueuzian H. Fascinating rhythm: a primer on chaos theory and its application to cardiology. Am Heart J1990; 120: 1419–1440.10.1016/0002-8703(90)90258-YSearch in Google Scholar

15 Aubert AE, Ramaekers D, Beckers F, et al. The analysis of heart rate variability in unrestrained rats. Validation of method and results. Comput Methods Programs Biomed1999; 60: 197–213.10.1016/S0169-2607(99)00017-6Search in Google Scholar

16 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. Circulation 1996; 93: 1043–1065.Search in Google Scholar

17 Yeragani VK, Nadella R, Hinze B, Yeragani S, Jampala VC. Nonlinear measures of heart period variability: decreased measures of symbolic dynamics in patients with panic disorder. Depress Anxiety2000; 12: 67–77.10.1002/1520-6394(2000)12:2<67::AID-DA2>3.0.CO;2-CSearch in Google Scholar

18 Peng CK, Havlin S, Hausdorff JM, Mietus JE, Stanley HE, Goldberger AL. Fractal mechanisms and heart rate dynamics. Long-range correlations and their breakdown with disease. J Electrocardiol1995; 28 (Suppl): 59–65.10.1016/S0022-0736(95)80017-4Search in Google Scholar

19 Yeragani VK, Sobolewski E, Kay J, Jampala VC, Igel G. Effect of age on long-term heart rate variability. Cardiovasc Res1997; 35: 35–42.10.1016/S0008-6363(97)00107-7Search in Google Scholar

20 Yeragani VK, Srinivasan K, Vempati S, Pohl R, Balon R. Fractal dimension of heart rate time series: an effective measure of autonomic function. J Appl Physiol1993; 75: 2429–2438.10.1152/jappl.1993.75.6.2429Search in Google Scholar

21 Beckers F, Ramaekers D, van Cleemput J, et al. Association between restoration of autonomic modulation in the native sinus node and hemodynamic improvement after cardiac transplantation. Transplantation2002; 73: 1614–1620.10.1097/00007890-200205270-00016Search in Google Scholar

22 Yeragani VK, Mallavarapu M, Radhakrishna RK, Tancer M, Uhde T. Linear and nonlinear measures of blood pressure variability: increased chaos of blood pressure time series in patients with panic disorder. Depress Anxiety2004; 19: 85–95.10.1002/da.10129Search in Google Scholar

23 Wu ZK, Vikman S, Laurikka J, et al. Nonlinear heart rate variability in CABG patients and the preconditioning effect. Eur J Cardiothorac Surg2005; 28: 109–113.10.1016/j.ejcts.2005.03.011Search in Google Scholar

24 Kim WS, Yoon YZ, Bae JH, Soh KS. Nonlinear characteristics of heart rate time series: influence of three recumbent positions in patients with mild or severe coronary artery disease. Physiol Meas2005; 26: 517–529.10.1088/0967-3334/26/4/016Search 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
  6. Multivariate and multiorgan analysis of cardiorespiratory variability signals: the CAP sleep case
  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
Downloaded on 31.10.2025 from https://www.degruyterbrill.com/document/doi/10.1515/BMT.2006.035/html?lang=en
Scroll to top button