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Multiscale entropy-based methods for heart rate variability complexity analysis

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  • Silva, Luiz Eduardo Virgilio
  • Cabella, Brenno Caetano Troca
  • Neves, Ubiraci Pereira da Costa
  • Murta Junior, Luiz Otavio

Abstract

Physiologic complexity is an important concept to characterize time series from biological systems, which associated to multiscale analysis can contribute to comprehension of many complex phenomena. Although multiscale entropy has been applied to physiological time series, it measures irregularity as function of scale. In this study we purpose and evaluate a set of three complexity metrics as function of time scales. Complexity metrics are derived from nonadditive entropy supported by generation of surrogate data, i.e. SDiffqmax, qmax and qzero. In order to access accuracy of proposed complexity metrics, receiver operating characteristic (ROC) curves were built and area under the curves was computed for three physiological situations. Heart rate variability (HRV) time series in normal sinus rhythm, atrial fibrillation, and congestive heart failure data set were analyzed. Results show that proposed metric for complexity is accurate and robust when compared to classic entropic irregularity metrics. Furthermore, SDiffqmax is the most accurate for lower scales, whereas qmax and qzero are the most accurate when higher time scales are considered. Multiscale complexity analysis described here showed potential to assess complex physiological time series and deserves further investigation in wide context.

Suggested Citation

  • Silva, Luiz Eduardo Virgilio & Cabella, Brenno Caetano Troca & Neves, Ubiraci Pereira da Costa & Murta Junior, Luiz Otavio, 2015. "Multiscale entropy-based methods for heart rate variability complexity analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 422(C), pages 143-152.
    • Handle: RePEc:eee:phsmap:v:422:y:2015:i:c:p:143-152
    DOI: 10.1016/j.physa.2014.12.011
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    References listed on IDEAS

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    1. Cabella, Brenno C.T. & Sturzbecher, Marcio J. & de Araujo, Draulio B. & Neves, Ubiraci P.C., 2009. "Generalized relative entropy in functional magnetic resonance imaging," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(1), pages 41-50.
    2. Borges, Ernesto P., 2004. "A possible deformed algebra and calculus inspired in nonextensive thermostatistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(1), pages 95-101.
    3. Tedeschi, W. & Müller, H.-P. & de Araujo, D.B. & Santos, A.C. & Neves, U.P.C. & Ernè, S.N. & Baffa, O., 2005. "Generalized mutual information tests applied to fMRI analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(2), pages 629-644.
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    Cited by:

    1. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Azami, Hamed & Escudero, Javier, 2017. "Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 261-276.
    3. Pastor, Marissa & Song, Juyong & Hoang, Danh-Tai & Jo, Junghyo, 2016. "Minimal perceptrons for memorizing complex patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 31-37.
    4. He, Shaobo & Sun, Kehui & Wang, Huihai, 2016. "Multivariate permutation entropy and its application for complexity analysis of chaotic systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 812-823.

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