Chen Chang, Sun Shixue, Cao Zhixin, Shi Yan, Sun Baoqing, Zhang Xiaohua Douglas
Faculty of Health Sciences, University of Macau, Taipa, Macau, China.
Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Biol Methods Protoc. 2019 Dec 13;4(1):bpz016. doi: 10.1093/biomethods/bpz016. eCollection 2019.
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, and can provide the analysis of sample entropy with different embedding dimensions and similarity thresholds. is a good choice for calculating multiscale sample entropy of physiological signal because it not only shows sample entropy of all scales simultaneously but also provides various visualization plots. is the only package that can calculate multivariate multiscale entropies. In terms of computing time, , , and run significantly faster than the other two packages. Moreover, we identify the issues in package. This article provides guidelines for researchers to find a suitable R package for their analysis and applications using sample entropy.
样本熵是一种用于分析生理信号复杂性和不规则性的强大工具，这些生理信号可能与人类健康相关。然而，其计算的复杂性阻碍了它的广泛应用。截至目前，R语言提供了多个用于计算样本熵的开源包。然而，所有这些包都是针对不同场景设计的。因此，在寻找合适的包时，研究人员会在参数设置和算法选择上感到困惑。为了便于他们选择，我们探索了五个现有的用于计算样本熵的R包的功能，并在几个维度上比较了它们的计算能力。我们使用了四个已发表的关于呼吸和心率的数据集来研究它们的输入参数、熵的类型和程序运行时间。总之，[具体包名1]和[具体包名2]可以提供不同嵌入维度和相似性阈值下的样本熵分析。[具体包名3]是计算生理信号多尺度样本熵的一个不错选择，因为它不仅能同时显示所有尺度的样本熵，还提供各种可视化图。[具体包名4]是唯一能计算多变量多尺度熵的包。在计算时间方面，[具体包名1]、[具体包名2]和[具体包名3]的运行速度明显快于其他两个包。此外，我们还发现了[具体包名5]中的问题。本文为研究人员寻找适合其使用样本熵进行分析和应用的R包提供了指导方针。
