Approximate Entropy and Sample Entropy: A Comprehensive Tutorial
By Alfonso Delgado-Bonal, Alexander Marshak. Entropy, 2019
603 citations recorded by the source corpus.
Approximate Entropy and Sample Entropy are two algorithms for determining the regularity of series of data based on the existence of patterns. Despite their similarities, the theoretical ideas behind those techniques are different but usually ignored. This paper aims to be a complete guideline of the theory and application of the algorithms, intended to explain their characteristics in detail to researchers from different fields. While initially developed for physiological applications, both algorithms have been used in other fields such as medicine, telecommunications, economics or Earth sciences. In this paper, we explain the theoretical aspects involving Information Theory and Chaos Theory, provide simple source codes for their computation, and illustrate the techniques with a step by step example of how to use the algorithms properly. This paper is not intended to be an exhaustive review of all previous applications of the algorithms but rather a comprehensive tutorial where no previous knowledge is required to understand the methodology.
Subfields: Cognitive Neuroscience, Economics and Econometrics, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/6b70052446adc0e529ce56bb4c2601259a71bea6
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Citations in the corpus
- Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial (2021, background): “SampEn was built on ApEn [78] and addresses the latter’s tendency to overestimate the amount of regularity in the signal [75, 80,81], being in turn more consistent in measuring complexity across different time series lengths [67].”