ArticleBiology methods & protocols2019
A comprehensive comparison and overview of R packages for calculating sample entropy.
Article in Biology methods & protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- RespirAnalyzer: an R package for analyzing data from continuous monitoring of respiratory signals.Bioinformatics advances · 2024Article
- Article
- Sample Entropy as a Tool to Assess Lumbo-Pelvic Movements in a Clinical Test for Low-Back-Pain Patients.Entropy (Basel, Switzerland) · 2022Article
- Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach.Journal of biological physics · 2021Article
- Time series and fractal analyses of wheezing: a novel approach.Physical and engineering sciences in medicine · 2020Article
- Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation.Chaos, solitons, and fractals · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sample entropy is a powerful tool for analyzing the complexity and irregularity of physiology signals which may be associated with human health. Nevertheless, the sophistication of its calculation hinders its universal application. As of today, the R language provides multiple open-source packages for calculating sample entropy. All of which, however, are designed for different scenarios. Therefore, when searching for a proper package, the investigators would be confused on the parameter setting and selection of algorithms. To ease their selection, we have explored the functions of five existing R packages for calculating sample entropy and have compared their computing capability in several dimensions. We used four published datasets on respiratory and heart rate to study their input parameters, types of entropy, and program running time. In summary,
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