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Multifractal estimates of monofractality in RR-heart series in power spectrum ranges

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  • Makowiec, Danuta
  • Dudkowska, Aleksandra
  • Gała̧ska, Rafał
  • Rynkiewicz, Andrzej

Abstract

Two popular estimators of multifractal properties: the Wavelet Transform Modulus Maxima method and Multifractal Detrended Fluctuation Analysis are applied to investigate signals consisting of normal RR-series in 39 healthy subjects and 90 patients suffering from systolic dysfunction of the left ventricle. However, differently from standards for multifractal analysis the scaling is performed separately in intervals corresponding to standard power spectral bands: low (LF), very low (VLF) and ultra low frequencies (ULF). Tests on fractional Brownian motions (fBm) are done to quantify properties of the estimators as detectors of monofractality in LF, VLF and ULF bands. Arguments are given that multifractal analysis of RR-series performed in these bands has a physiological meaning. The increased activation of the sympathetic nervous system caused by heart disease is detected evidently only by analysis in LF. The transition in multifractal characteristics between diurnal and nocturnal activity takes place when the analysis moves from LF and VLF to ULF. Only in ULF, the diurnal heart rate variability can be approximated by fBm with a self-similarity index of H=0.20.

Suggested Citation

  • Makowiec, Danuta & Dudkowska, Aleksandra & Gała̧ska, Rafał & Rynkiewicz, Andrzej, 2009. "Multifractal estimates of monofractality in RR-heart series in power spectrum ranges," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3486-3502.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:17:p:3486-3502
    DOI: 10.1016/j.physa.2009.05.005
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    Cited by:

    1. Todd Zorick & Mark A Mandelkern, 2013. "Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-7, July.
    2. Faheem Aslam & Paulo Ferreira & Haider Ali & Ana Ercília José, 2022. "Application of Multifractal Analysis in Estimating the Reaction of Energy Markets to Geopolitical Acts and Threats," Sustainability, MDPI, vol. 14(10), pages 1-23, May.
    3. Paolo Castiglioni & Davide Lazzeroni & Paolo Coruzzi & Andrea Faini, 2018. "Multifractal-Multiscale Analysis of Cardiovascular Signals: A DFA-Based Characterization of Blood Pressure and Heart-Rate Complexity by Gender," Complexity, Hindawi, vol. 2018, pages 1-14, January.
    4. Stanis{l}aw Dro.zd.z & Rafa{l} Kowalski & Pawe{l} O'swic{e}cimka & Rafa{l} Rak & Robert Gc{e}barowski, 2018. "Dynamical variety of shapes in financial multifractality," Papers 1809.06728, arXiv.org.
    5. Pawe{l} O'swik{e}cimka & Stanis{l}aw Dro.zd.z & Mattia Frasca & Robert Gk{e}barowski & Natsue Yoshimura & Luciano Zunino & Ludovico Minati, 2020. "Wavelet-based discrimination of isolated singularities masquerading as multifractals in detrended fluctuation analyses," Papers 2004.03319, arXiv.org.
    6. Loiseau, Patrick & Médigue, Claire & Gonçalves, Paulo & Attia, Najmeddine & Seuret, Stéphane & Cottin, François & Chemla, Denis & Sorine, Michel & Barral, Julien, 2012. "Large deviations estimates for the multiscale analysis of heart rate variability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5658-5671.

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