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Entropy Algorithms

In: Fuzzy Recurrence Plots and Networks with Applications in Biomedicine

Author

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  • Tuan D. Pham

    (Prince Mohammad Bin Fahd University, The Center for Artificial Intelligence)

Abstract

Although the purpose of entropy algorithms is not for studying chaosChaos, their mathematical formulations for quantifying fluctuations in time seriesTime series bear a resemblance to the recurrence analysis with respect to the definition of similaritySimilarity between sub-vectors of time seriesTime series. This chapter presents several algorithms for measuring the irregularityIrregularity or predictabilityPredictability of time seriesTime series, which can also be considered as measures of complexity of the data. These methods include the approximate entropyApproximate entropy, sample entropySample entropy, multiscale entropyMultiscale entropy, and time-shift multiscaleTime-shift multiscale entropy entropyMultiscale entropy. These selected methods are addressed herein to provide the reader with sufficient background for the presentation of an application described in Chap. 7 . Most content of this chapter was from the author’s work reported in Pham (Entropy 19:257, 2017).

Suggested Citation

  • Tuan D. Pham, 2020. "Entropy Algorithms," Springer Books, in: Fuzzy Recurrence Plots and Networks with Applications in Biomedicine, chapter 0, pages 81-97, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-37530-0_6
    DOI: 10.1007/978-3-030-37530-0_6
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