Approximate entropy as a measure of system complexity.
TLDR
Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes.Abstract:
Techniques to determine changing system complexity from data are evaluated. Convergence of a frequently used correlation dimension algorithm to a finite value does not necessarily imply an underlying deterministic model or chaos. Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes. The capability to discern changing complexity from such a relatively small amount of data holds promise for applications of ApEn in a variety of contexts.read more
Citations
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Physiological time-series analysis using approximate entropy and sample entropy
TL;DR: A new and related complexity measure is developed, sample entropy (SampEn), and a comparison of ApEn and SampEn is compared by using them to analyze sets of random numbers with known probabilistic character, finding SampEn agreed with theory much more closely than ApEn over a broad range of conditions.
Journal ArticleDOI
Heart rate variability: a review
TL;DR: The various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV are discussed.
Journal ArticleDOI
Multiscale entropy analysis of biological signals
TL;DR: The MSE method is applied to the analysis of coding and noncoding DNA sequences and it is found that the latter have higher multiscale entropy, consistent with the emerging view that so-called "junk DNA" sequences contain important biological information.
Journal ArticleDOI
Physiological time-series analysis: What does regularity quantify?
TL;DR: This work provides a formal mathematical description of approximate entropy and provides a multistep description of the algorithm as applied to two contrasting clinical heart rate data sets, indicating the utility of ApEn to test this hypothesis.
Journal ArticleDOI
Pathophysiology of the neuroregulation of growth hormone secretion in experimental animals and the human
TL;DR: The pathophysiology of the GHRH somatostatin-GH-IGF-I feedback axis is reviewed and it is proposed that this system is best viewed as a multivalent feedback network that is exquisitely sensitive to an array of neuroregulators and environmental stressors and genetic restraints.
References
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Book
Practical Numerical Algorithms for Chaotic Systems
Thomas S. Parker,Leon O. Chua +1 more
TL;DR: The goal of this book is to present an elementary introduction on chaotic systems for the non-specialist, and to present and extensive package of computer algorithms for simulating and characterizing chaotic phenomena.
BookDOI
Entropy, large deviations, and statistical mechanics
TL;DR: In this paper, the authors introduce the concept of large deviations for random variables with a finite state space, which is a generalization of the notion of large deviation for random vectors.
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