EntropyHub: An open-source toolkit for entropic time series analysis

Abstract

An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be estimated in many ways, with newly developed methods being continuously introduced in the scientific literature. Despite the growing interest in entropic time series and image analysis, there is a shortage of validated, open-source software tools that enable researchers to apply these methods. To date, packages for performing entropy analysis are often run using graphical user interfaces, lack the necessary supporting documentation, or do not include functions for more advanced entropy methods, such as cross-entropy, multiscale cross-entropy or bidimensional entropy. In light of this, this paper introduces EntropyHub, an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia. EntropyHub (version 0.1) provides an extensive range of more than forty functions for estimating cross-, multiscale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allows the user to specify multiple parameters in the entropy calculation. Instructions for installation, descriptions of function syntax, and examples of use are fully detailed in the supporting documentation, available on the EntropyHub website– www.EntropyHub.xyz. Compatible with Windows, Mac and Linux operating systems, EntropyHub is hosted on GitHub, as well as the native package repository for MATLAB, Python and Julia, respectively. The goal of EntropyHub is to integrate the many established entropy methods into one complete resource, providing tools that make advanced entropic time series analysis straightforward and reproducible.

Pressure Injury Link to Entropy of Abdominal Temperature

“…In this study, refined multiscale SampEn was estimated for each abdominal temperature series using the EntropyHub library [ 39 ] that has been developed for use with multiple programming languages, including Python , which was used here. A sixth-order Butterworth filter with cutoff frequency for timescale was used for low-pass filtering of the time series.…”

Pressure Injury Link to Entropy of Abdominal Temperature

“…In this study, refined multiscale SampEn was estimated for each abdominal temperature series using the EntropyHub library [34] that has been developed for use with multiple programming languages, including Python, which was used here. A sixth order Butterworth filter with cutoff frequency 1/2τ for timescale τ was used for low-pass filtering of the time series.…”

The Structure of Chaos: An Empirical Comparison of Fractal Physiology Complexity Indices Using NeuroKit2

“…We would like to thank the contributors of NeuroKit2, as well as the people that developed and shared open-source code which helped implementing the complexity algorithms in NeuroKit2. In particular, the contributors and maintainers of packages such as nolds [ 30 ], AntroPy [ 31 ], pyEntropy , and EntropyHub [ 8 ].…”

“…Additionally, the lack of a comprehensive open-source and user-friendly software for computing various complexity indices likely contributes to the scarcity of empirical comparisons [ 8 ]. Indeed, many complexity indices are only described mathematically in journal articles, with reusable code seldom made available, therefore limiting their further application and validation [ 5 , 8 ].…”

“…Additionally, the lack of a comprehensive open-source and user-friendly software for computing various complexity indices likely contributes to the scarcity of empirical comparisons [ 8 ]. Indeed, many complexity indices are only described mathematically in journal articles, with reusable code seldom made available, therefore limiting their further application and validation [ 5 , 8 ]. Even when available and open-source, the code implementations of complexity measures are typically found scattered across different packages or scripts, or embedded within a larger goal-directed framework (e.g., HCTSA , a time-series comparison tool [ 9 ]).…”