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A comprehensive comparison and overview of R packages for calculating sample entropy.

Chang Chen1, Shixue Sun1, Zhixin Cao2,3,4

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Sample entropy analysis of physiological signals is complex. This study compares R packages, guiding researchers to select appropriate tools for complexity and irregularity analysis in health research.

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Area of Science:

  • Physiological signal analysis
  • Complexity science
  • Bioinformatics

Background:

  • Sample entropy quantifies physiological signal complexity, crucial for health assessment.
  • Existing R packages for sample entropy calculation vary in functionality and application.
  • Researchers face challenges in selecting appropriate R packages due to parameter and algorithm diversity.

Purpose of the Study:

  • To compare the capabilities of five R packages for calculating sample entropy.
  • To guide researchers in selecting suitable R packages for physiological signal analysis.
  • To evaluate performance based on input parameters, entropy types, and computation time.

Main Methods:

  • Comparative analysis of five R packages for sample entropy calculation.
  • Utilized four public datasets (respiratory and heart rate).
  • Evaluated input parameters, entropy types (including multiscale and multivariate), and program running time.

Main Results:

  • NonlinearTseries and CGManalyzer support adjustable embedding dimensions and similarity thresholds.
  • CGManalyzer excels in multiscale sample entropy with simultaneous scale analysis and visualizations.
  • MSMVSampEn is unique for multivariate multiscale entropies; NonlinearTseries, CGManalyzer, and MSMVSampEn offer superior computing speed.

Conclusions:

  • CGManalyzer is recommended for multiscale sample entropy analysis of physiological signals.
  • MSMVSampEn is the sole option for multivariate multiscale entropy calculations.
  • This comparison provides essential guidelines for researchers utilizing sample entropy in R.