Special Issue "Approximate, Sample and Multiscale Entropy"

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

Deadline for manuscript submissions: closed (15 March 2020).

Special Issue Editor

Prof. Dr. Roberto Hornero
Website
Guest Editor
Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011, Valladolid, Spain
Interests: biomedical signal processing; information theory; non-linear dynamics, entropy and complexity; sleep disorders; neurodegenerative diseases
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Special Issue Information

Dear Colleagues,

Traditional entropy measures quantify the irregularity of time series on a single scale. For example, approximate entropy (ApEn) is a measure of the regularity of a process that is related to Shannon’s entropy. Sample Entropy (SampEn) is a refinement of the ApEn, which is a more robust and less biased statistic. On the other hand, multiscale entropy (MSE) has been proposed to evaluate the complexity of time series by taking into account the multiple time scales in physical systems. MSE can be applied both to physical and physiological data sets and can be used with a variety of entropy measures (ApEn, SampEn, permutation entropy, distribution entropy, dispersion entropy, etc.). MSE has found many applications in biosignal analysis and has been extended to multivariate MSE. These approaches (ApEn, SampEn, and MSE) have received a great deal of attention and have been used in a wide range of applications.

In this Special Issue, we would like to collect papers focusing on both the theory and applications of MSE, SampEn or ApEn. Applications can include (but are not limited to) biomedical engineering, chemical engineering, hydrology, pharmaceutical sciences, financial analyses, neurosciences, industrial engineering, geosciences, information sciences, etc.

Prof. Dr. Roberto Hornero
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 1600 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.

Keywords

  • Approximate entropy
  • Sample entropy
  • Multiscale entropy
  • Permutation entropy
  • Distribution entropy
  • Dispersion entropy
  • Irregularity
  • Complexity

Published Papers (2 papers)

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Open AccessArticle
Obtaining Information about Operation of Centrifugal Compressor from Pressure by Combining EEMD and IMFE
Entropy 2020, 22(4), 424; https://doi.org/10.3390/e22040424 - 09 Apr 2020
Abstract
Based on entropy characteristics, some complex nonlinear dynamics of the dynamic pressure at the outlet of a centrifugal compressor are analyzed, as the centrifugal compressor operates in a stable and unstable state. First, the 800-kW centrifugal compressor is tested to gather the time [...] Read more.
Based on entropy characteristics, some complex nonlinear dynamics of the dynamic pressure at the outlet of a centrifugal compressor are analyzed, as the centrifugal compressor operates in a stable and unstable state. First, the 800-kW centrifugal compressor is tested to gather the time sequence of dynamic pressure at the outlet by controlling the opening of the anti-surge valve at the outlet, and both the stable and unstable states are tested. Then, multi-scale fuzzy entropy and an improved method are introduced to analyze the gathered time sequence of dynamic pressure. Furthermore, the decomposed signals of dynamic pressure are obtained using ensemble empirical mode decomposition (EEMD), and are decomposed into six intrinsic mode functions and one residual signal, and the intrinsic mode functions with large correlation coefficients in the frequency domain are used to calculate the improved multi-scale fuzzy entropy (IMFE). Finally, the statistical reliability of the method is studied by modifying the original data. After analysis of the relationships between the dynamic pressure and entropy characteristics, some important intrinsic dynamics are captured. The entropy becomes the largest in the stable state, but decreases rapidly with the deepening of the unstable state, and it becomes the smallest in the surge. Compared with multi-scale fuzzy entropy, the curve of the improved method is smoother and could show the change of entropy exactly under different scale factors. For the decomposed signals, the unstable state is captured clearly for higher order intrinsic mode functions and residual signals, while the unstable state is not apparent for lower order intrinsic mode functions. In conclusion, it can be observed that the proposed method can be used to accurately identify the unstable states of a centrifugal compressor in real-time fault diagnosis. Full article
(This article belongs to the Special Issue Approximate, Sample and Multiscale Entropy)
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Review

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Open AccessReview
Approximate Entropy and Sample Entropy: A Comprehensive Tutorial
Entropy 2019, 21(6), 541; https://doi.org/10.3390/e21060541 - 28 May 2019
Cited by 7
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Approximate, Sample and Multiscale Entropy)
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