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Special Issue "Multiscale Entropy Approaches and Their Applications"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: 31 March 2019

Special Issue Editor

Guest Editor
Dr. Anne Humeau-Heurtier

Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), University of Angers, IUT, GEII Department, 4 boulevard Lavoisier, BP 42018, 49016 Angers cedex, France
Interests: entropy; multiscale entropy; nonlinear analysis; empirical mode decomposition; biomedical data

Special Issue Information

Dear Colleagues,

Multiscale entropy measures have been proposed from the beginning of the 2000s to evaluate the complexity of time series, by taking into account the multiple time scales in physical systems. Since then, these approaches have received a great deal of attention and have been used in a large range of applications. Multivariate approaches have also been developed.

The algorithms for a multiscale entropy approach are composed of two main steps: i) a coarse-graining procedure to represent the system’s dynamics on different scales; ii) the entropy computation for the original signal and for the coarse-grained time series to evaluate the irregularity for each scale. Moreover, different entropy measures have been associated with the coarse-graining approach, each one having its advantages and drawbacks: approximate entropy, sample entropy, permutation entropy, fuzzy entropy, distribution entropy, dispersion entropy, etc.

In this Special Issue, we would like to collect papers focusing on both the theory and applications of multiscale entropy approaches. Any kind of entropy measure is considered (see above).

The main topics of this Special Issue include (but are not limited to):

  • improvement of the coarse-graining concept
  • improvement in the entropy measure itself
  • applications of the multiscale approach on univariate or multivariate time series; one-dimensional, but also bi-dimensional data are welcome. Applications can include biomedical engineering, chemical engineering, hydrology, pharmaceutical sciences, financial analyses, neurosciences, industrial engineering, geosciences, information sciences, etc.

This issue is to continue with the first issue of Multiscale Entropy,


Dr. Anne Humeau-Heurtier
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Open AccessArticle Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability
Entropy 2018, 20(12), 952; https://doi.org/10.3390/e20120952
Received: 12 November 2018 / Revised: 8 December 2018 / Accepted: 9 December 2018 / Published: 11 December 2018
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Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time
[...] Read more.
Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series. Full article
(This article belongs to the Special Issue Multiscale Entropy Approaches and Their Applications)

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