Approximate Entropy and Sample Entropy: A Comprehensive Tutorial
| Autor: | Alexander Marshak, Alfonso Delgado-Bonal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
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Theoretical computer science Computer science media_common.quotation_subject Computation approximate entropy General Physics and Astronomy lcsh:Astrophysics Review 02 engineering and technology sample entropy Information theory Approximate entropy Chaos theory 03 medical and health sciences 0302 clinical medicine lcsh:QB460-466 0202 electrical engineering electronic engineering information engineering lcsh:Science media_common information theory lcsh:QC1-999 Sample entropy 020201 artificial intelligence & image processing chaos theory lcsh:Q 030217 neurology & neurosurgery lcsh:Physics |
| Zdroj: | Entropy, Vol 21, Iss 6, p 541 (2019) Entropy Volume 21 Issue 6 |
| ISSN: | 1099-4300 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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