Evaluation of Systems’ Irregularity and Complexity: Sample Entropy, Its Derivatives, and Their Applications across Scales and Disciplines

Edited by
November 2018
264 pages
  • ISBN978-3-03897-332-4 (Paperback)
  • ISBN978-3-03897-333-1 (PDF)

This book is a reprint of the Special Issue Evaluation of Systems’ Irregularity and Complexity: Sample Entropy, Its Derivatives, and Their Applications across Scales and Disciplines that was published in

Chemistry & Materials Science
Computer Science & Mathematics
Physical Sciences

Based on information theory, a number of entropy measures have been proposed since the 1990s to assess systems’ irregularity, such as approximate entropy, sample entropy, permutation entropy, intrinsic mode entropy, and dispersion entropy, to cite only a few. Among them, sample entropy has been used in a very large variety of disciplines for both univariate and multivariate data. However, improvements to the sample entropy algorithm are still being proposed because sample entropy is unstable for short time series, may be sensitive to parameter values, and can be too time-consuming for long data. At the same time, it is worth noting that sample entropy does not take into account the multiple temporal scales inherent in complex systems. It is maximized for completely random processes and is used only to quantify the irregularity of signals on a single scale. This is why analyses of irregularity—with sample entropy or its derivatives—at multiple time scales have been proposed to assess systems’ complexity. This Book presents contributions related to new and original research based on the use of sample entropy or its derivatives.

  • Paperback
License and Copyright
© 2019 by the authors; CC BY license
Myocardial infarction (MI); electrocardiogram (ECG) beats; flexible analytic wavelet transform (FAWT); sample entropy; classification; exercise; short-term heart rate variability (HRV); complexity; entropy; approximate entropy (ApEn); conditional entropy (CE); distribution entropy (DistEn); fuzzy entropy (FuzzyEn); permutation entropy (PermEn); sample entropy (SampEn); Alzheimer’s disease; electroencephalogram; non-linear analysis; complexity; irregularity; Fuzzy Entropy; Sample Entropy; Alzheimer’s disease; mild cognitive impairment; electroencephalography (EEG); spectral analysis; nonlinear analysis; multiclass classification approach; multi-bay; three-dimensional; structural health monitoring; multi-scale; cross-sample entropy; Sample Entropy; algorithm; fast computation; kd-trees; bucket-assisted algorithm; sample entropy; dispersion entropy; multiscale entropy; complexity; heart rate variability; rat; exercise; physical training; conditioning; multivariate sample entropy; time series synchronization; structural complexity; multiscale entropy; regularity; skin blood flow; diabetic foot ulcers; complexity; multiscale dispersion and sample entropy; refined composite technique; intrinsic mode dispersion and sample entropy; moving average; Butterworth filter; empirical mode decomposition; downsampling; entropy; fuzzy entropy; sample entropy; irregularity; fetal heart rate; time series; symmetrical m-patterns; entropy; irregularity; asthma; physiological signal; individualized treatment; hierarchical cosine similarity entropy; multiscale entropy; sample entropy; feature extraction; complexity; complexity; entropy; carbon market; multi-scale entropy; heart rate variability; irregularity; sample entropy; functional systems; individual development; stress; alcohol administration; learning; driving fatigue; sample entropy; kernel principal component analysis; support vector machine; sample entropy; multiscale entropy; fuzzy entropy; multivariate data

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