Multifractal Analysis of Cardiac Series and Predictors of Sudden Cardiac Death

Authors

  • V.I. Kovalchuk Taras Shevchenko National University of Kyiv, Faculty of Physics
  • O.S. Svechnikova Taras Shevchenko National University of Kyiv, Faculty of Physics
  • L.A. Bulavin Taras Shevchenko National University of Kyiv, Faculty of Physics

DOI:

https://doi.org/10.15407/ujpe66.10.879

Keywords:

multifractal analysis, heart rate variability, sudden cardiac death

Abstract

In the framework of the multifractal formalism and using the wavelet-transform modulusmaxima method, the daily Holter monitoring records from the PhysioNet databases for sudden cardiac death and normal sinus rhythm have been analyzed. On the basis of successive window samples of the heart rate variability signals for the VFL range (0.0025–0.04 Hz), the time dependences of the widths of singularity spectra and the positions of their maxima are calculated. The average energy of low-frequency oscillations of the singularity spectrum width for the studied records of sudden cardiac death is found to be by 36% higher than the corresponding value for the records of normal sinus rhythm. This discrepancy can be considered as a predictor of sudden cardiac death.

References

Clinical Arrhythmology. Edited by A.V. Ardashev (Medpraktika, 2009) (in Russian) [ISBN: 978-5-98803-198-7].

M.E. Mortada, M. Akhtar. Sudden cardiac death. Cardiac Intens. Care 25, 293 (2010).

https://doi.org/10.1016/B978-1-4160-3773-6.10025-4

V.E. Oleynikov, M.V. Lukianova, E.V. Dushina. Sudden death predictors in patients after myocardial infarction by Holter ECG monitoring. Russ. J. Cardiol. 119 (3), 108 (2015).

https://doi.org/10.15829/1560-4071-2015-3-108-116

A.V. Ardashev, A.Y. Loskutov. Practical Aspects of Modern Analysis Methods of Heart Rate Variability (Medpraktika, 2011) (in Russian) [ISBN: 978-5-98803-250-2].

R.M. Bayevsky. Analysis of heart rate variability: History and philosophy, theory and practice. J. Clin. Inform. Telemed. 1, 54 (2004) (in Russian).

Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Task force of the european society of cardiology the north american society of pacing electrophysiology. Circulation 93, 1043 (1996).

J. Giera ltowski, J. J. Zebrowski, R. Baranowski. Multiscale multifractal analysis of heart rate variability recordings with a large number of occurrences of arrhythmia. Phys. Rev. E 85, 021915 (2012).

https://doi.org/10.1103/PhysRevE.85.021915

K. Gadhoumi, D. Do, F. Badilini, M.M. Pelter, X. Hu. Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation. J. Electrocard. 51, S83 (2018).

https://doi.org/10.1016/j.jelectrocard.2018.08.030

J. Sen, D. McGill. Fractal analysis of heart rate variability as a predictor of mortality: A systematic review and metaanalysis. Chaos 28, 072101 (2018).

https://doi.org/10.1063/1.5038818

P. Castiglioni, F. Faini. A fast DFA algorithm for multifractal multiscale analysis of physiological time series. Front. Physiol. 10, 115 (2019).

https://doi.org/10.3389/fphys.2019.00115

O.E. Dick, A.D. Nozdrachev. Mechanisms of Changes in Dynamical Complexity of Physiological Signal Patterns (Saint-Petersburg State University, 2019) (in Russian) [ISBN: 978-5-28805-932-2].

A.N. Pavlov, V.S. Anishchenko. Multifractal analysis of complex signals. Physics-Usp. 50, 819 (2007).

https://doi.org/10.1070/PU2007v050n08ABEH006116

V.S. Kublanov, V.I. Borisov, A.Yu. Dolganov. Analysis of Biomedical Signals in MATLAB Environment (Ural University Publishing House, 2016) (in Russian) [ISBN: 978-5-79961-813-1].

P.Ch. Ivanov, L.A.N. Amaral, A.L. Goldberger, S. Halvin, M.G. Rosenblum, Z.R. Struzik, H.E. Stanley. Multifractality in human heartbeat dynamics. Lett. Nature 399, 461 (1999).

https://doi.org/10.1038/20924

A.N. Pavlov, O.V. Sosnovtseva, A.R. Ziganshin. Multifractal analysis of chaotic dynamics in interacting systems. Izv. Vuz. Appl. Nonlinear Dynam. 11, 39 (2003) (in Russian).

J.F. Muzy, E. Bacry, A. Arneodo. Wavelets and multifractal formalism for singular signals: Application to turbulence data. Phys. Rev. Lett. 67, 3515 (1991).

https://doi.org/10.1103/PhysRevLett.67.3515

J.F. Muzy, E. Bacry, A. Arneodo. Multifractal formalism for fractal signals: The structure-function approach versus the wavelet-transform modulus-maxima method. Phys. Rev. E. 47, 875 (1993).

https://doi.org/10.1103/PhysRevE.47.875

J.F. Muzy, E. Bacry, A. Arneodo. The multifractal formalism revisited with wavelets. Int. J. Bifurc. Chaos 4, 245 (1994).

https://doi.org/10.1142/S0218127494000204

I. Daubechies. Ten Lectures on Wavelets (SIAM, 1992) [ISBN: 978-0-89871-274-2].

https://doi.org/10.1137/1.9781611970104

A.G. Maslovskaya, L.S. Afanasov. Algorithms of multifractal wavelet analysis in problems of specifying raster images of self-similar structures. Tomsk State Univ. J. Control Comput. Sci. 53, 61 (2020) (in Russian).

https://doi.org/10.17223/19988605/53/6

Sudden Cardiac Death Holter Database [https:// physionet.org/content/sddb/1.0.0/].

MIT-BIH Normal Sinus Rhythm Database [https:// physionet.org/content/nsrdb/1.0.0/].

D. Makowiec, A. Dudkowska, R. Galaska, A. Rynkiewicz. Multifractal estimates of monofractality in RR-heart series in power spectrum ranges. Physica A 388 3486 (2009).

https://doi.org/10.1016/j.physa.2009.05.005

N.M. Astaf'eva. Wavelet analysis: basic theory and some applications. Physics-Usp. 39, 1085 (1996).

https://doi.org/10.1070/PU1996v039n11ABEH000177

D. Wackerly, W. Mendenhall, R.L. Scheaffer. Mathematical Statistics with Applications (Thomson Brooks/Cole, 2008) [ISBN: 978-0-49511-081-1].

R.A. Fisher, Y. Frank. Statistical Tables for Biological, Agricultural and Medical Research (Oliver and Boyd, 1938).

H.E. Stanley, L.A.N. Amaral, A.L. Goldberger, S. Havlin, P.Ch. Ivanov, C.K. Peng. Statistical physics and physiology: Mono-fractal and multifractal approaches. Physica A 270, 309 (1999).

https://doi.org/10.1016/S0378-4371(99)00230-7

G. Rangarajan, M. Ding. Processes with Long-Range Correlations: Theory and Applications (Springer, 2003) [ISBN: 978-3-540-44832-7]. https://doi.org/10.1007/3-540-44832-2

M.E. Dokukin, N.V. Guz, R.M. Gaikwad, C.D. Woodworth, I. Sokolov. Cell surface as a fractal: Normal and cancerous cervical cells demonstrate different fractal behavior of surface adhesion maps at the nanoscale. Phys. Rev. Lett. 107 028101 (2011). https://doi.org/10.1103/PhysRevLett.107.028101

A.N. Pavlov, A.R. Ziganshin, O.A. Klimova. Multifractal characterization of blood pressure dynamics: Stressinduced phenomena. Chaos Solit. Fractals 24 57 (2004). https://doi.org/10.1016/S0960-0779(04)00557-0

J. Semmlow. Signals and Systems for Bioengineers (Academic Press, 2011) [ISBN: 978-0-123-84982-3].

E.N. Rumanov. Critical phenomena far from equilibrium. Physics-Usp. 56, 93 (2013) (in Russian). https://doi.org/10.3367/UFNe.0183.201301f.0103

R.S. Akhmetkhanov. Loss of multifractality - criterion of system transition to another condition. Safety Emerg. Probl. 5, 20 (2019) (in Russian).

H. Haken. Information and Self-Organization: A Macroscopic Approach to Complex Systems (Springer, 2006) [ISBN: 978-3-540-33021-9].

Published

2021-11-01

How to Cite

Kovalchuk, V., Svechnikova, O., & Bulavin, L. (2021). Multifractal Analysis of Cardiac Series and Predictors of Sudden Cardiac Death. Ukrainian Journal of Physics, 66(10), 879. https://doi.org/10.15407/ujpe66.10.879

Issue

Section

Physics of liquids and liquid systems, biophysics and medical physics

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