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Coarse quantization in calculations of entropy measures for experimental time series

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Abstract

Entropy measures have been widely used to quantify the complexity of theoretical and experimental dynamical systems. In this paper, two novel entropy measures are developed based on using coarse quantization to classify and compare dynamical features within a time series: quantized dynamical entropy and a quantization-based approximation of sample entropy. The properties of the developed entropy measures are examined alongside sample entropy and permutation entropy by studying the logistic map, bifurcations in a nonsmooth predator-prey model and the real-world case study of characterizing changes in gait dynamics caused by age and cognitive interference. Based on these investigations, it is shown that the developed measures have the best overall performance in instances where computational efficiency is vital.

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Acknowledgments

We gratefully acknowledge funding from the Natural Sciences and Engineering Research Council of Canada.

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Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, University of Manitoba, Winnipeg, MB, Canada

    Graham Leverick & Christine Wu

  2. School of Medical Rehabilitation, University of Manitoba, Winnipeg, MB, Canada

    Tony Szturm

Authors
  1. Graham Leverick
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  2. Christine Wu
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  3. Tony Szturm
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Correspondence to Christine Wu.

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Leverick, G., Wu, C. & Szturm, T. Coarse quantization in calculations of entropy measures for experimental time series. Nonlinear Dyn 79, 93–100 (2015). https://doi.org/10.1007/s11071-014-1647-z

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  • DOI: https://doi.org/10.1007/s11071-014-1647-z

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