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Entropy
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Entropy in Biomedical Engineering, 3rd Edition
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Entropy in Biomedical Engineering, 3rd Edition

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 1270

Special Issue Editor

Dr. George Manis

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Interests: biomedical engineering; entropy analysis; biomedical signal processing; computing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of nonlinear methods in biomedical engineering has grown increasingly popular, with entropy-based ones being of major importance. Various definitions of entropy have been extensively used in biomedical engineering, where in some topics, the vast majority of papers employ entropy analysis. Biomedical engineering, with complex and multidimensional problems, has long inspired researchers working on entropy, leading to the development of significant entropy definitions. The inherent capability of entropy analysis to extract sensitive information from complex systems has been the key factor in its widespread acceptance and adoption.

This is the third Special Issue on entropy in Biomedical Engineering. The success of the previous issues has motivated us to open a new Issue on the same topic. This series of Special Issues focuses on the contribution of entropy in biomedical engineering, including, but not limited to, biomedical applications; the analysis of biomedical data using entropy; entropy definitions inspired by biomedical engineering challenges; entropy metrics evaluated with biomedical data; computational algorithms; and the use of entropy as features in machine learning applications analyzing biomedical data.

Dr. George Manis
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 submissions that pass pre-check are 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 2600 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

  • entropy
  • approximate entropy
  • sample entropy
  • nonlinear analysis
  • biomedical engineering
  • entropy in biomedical applications
  • entropy in biomedical signals analysis
  • entropy in biomedical imaging
  • entropy in machine learning
  • fast computation of entropy

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Related Special Issues

Published Papers (2 papers)

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Research

17 pages, 461 KiB  
Article
Weibull-Type Incubation Period and Time of Exposure Using γ-Divergence
by Daisuke Yoneoka, Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura and Akifumi Eguchi
Entropy 2025, 27(3), 321; https://doi.org/10.3390/e27030321 - 19 Mar 2025
Viewed by 214
Abstract
Accurately determining the exposure time to an infectious pathogen, together with the corresponding incubation period, is vital for identifying infection sources and implementing targeted public health interventions. However, real-world outbreak data often include outliers—namely, tertiary or subsequent infection cases not directly linked to [...] Read more.
Accurately determining the exposure time to an infectious pathogen, together with the corresponding incubation period, is vital for identifying infection sources and implementing targeted public health interventions. However, real-world outbreak data often include outliers—namely, tertiary or subsequent infection cases not directly linked to the initial source—that complicate the estimation of exposure time. To address this challenge, we introduce a robust estimation framework based on a three-parameter Weibull distribution in which the location parameter naturally corresponds to the unknown exposure time. Our method employs a γ-divergence criterion—a robust generalization of the standard cross-entropy criterion—optimized via a tailored majorization–minimization (MM) algorithm designed to guarantee a monotonic decrease in the objective function despite the non-convexity typically present in robust formulations. Extensive Monte Carlo simulations demonstrate that our approach outperforms conventional estimation methods in terms of bias and mean squared error as well as in estimating the incubation period. Moreover, applications to real-world surveillance data on COVID-19 illustrate the practical advantages of the proposed method. These findings highlight the method’s robustness and efficiency in scenarios where data contamination from secondary or tertiary infections is common, showing its potential value for early outbreak detection and rapid epidemiological response. Full article
(This article belongs to the Special Issue Entropy in Biomedical Engineering, 3rd Edition)
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17 pages, 887 KiB  
Article
Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images
by Muqaddas Abid, Muhammad Suzuri Hitam, Rozniza Ali, Hamed Azami and Anne Humeau-Heurtier
Entropy 2025, 27(1), 80; https://doi.org/10.3390/e27010080 - 17 Jan 2025
Viewed by 733
Abstract
Entropy algorithms are widely applied in signal analysis to quantify the irregularity of data. In the realm of two-dimensional data, their two-dimensional forms play a crucial role in analyzing images. Previous works have demonstrated the effectiveness of one-dimensional increment entropy in detecting abrupt [...] Read more.
Entropy algorithms are widely applied in signal analysis to quantify the irregularity of data. In the realm of two-dimensional data, their two-dimensional forms play a crucial role in analyzing images. Previous works have demonstrated the effectiveness of one-dimensional increment entropy in detecting abrupt changes in signals. Leveraging these advantages, we introduce a novel concept, two-dimensional increment entropy (IncrEn2D), tailored for analyzing image textures. In our proposed method, increments are translated into two-letter words, encoding both the size (magnitude) and direction (sign) of the increments calculated from an image. We validate the effectiveness of this new entropy measure by applying it to MIX2D(p) processes and synthetic textures. Experimental validation spans diverse datasets, including the Kylberg dataset for real textures and medical images featuring colon cancer characteristics. To further validate our results, we employ a support vector machine model, utilizing multiscale entropy values as feature inputs. A comparative analysis with well-known bidimensional sample entropy (SampEn2D) and bidimensional dispersion entropy (DispEn2D) reveals that IncrEn2D achieves an average classification accuracy surpassing that of other methods. In summary, IncrEn2D emerges as an innovative and potent tool for image analysis and texture characterization, offering superior performance compared to existing bidimensional entropy measures. Full article
(This article belongs to the Special Issue Entropy in Biomedical Engineering, 3rd Edition)
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