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Entropy in Biomedical Engineering II
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Entropy in Biomedical Engineering II

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 3844

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 gained increasing popularity, with the entropy-based ones being of major importance. The 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 always inspired researchers working on entropy, whilst significant entropy definitions have been initiated from the biomedical engineering field. The inherent ability of entropy to extract sensitive information from complex systems was catalytic in this wide acceptance.

The success of the first Special issue on Entropy in Biomedical Engineering motivated the opening of a new Special Issue with the same topic, as volume II. Like the first volume, this Special Issue focuses on contributions on the use 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 topics; entropy definitions evaluated with biomedical data; computing algorithms; and entropy as a feature in machine learning methods applied to 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
  • non-linear 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 Issue

Published Papers (3 papers)

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Research

15 pages, 5469 KiB  
Article
A Fast Algorithm for Estimating Two-Dimensional Sample Entropy Based on an Upper Confidence Bound and Monte Carlo Sampling
by Zeheng Zhou, Ying Jiang, Weifeng Liu, Ruifan Wu, Zerong Li and Wenchao Guan
Entropy 2024, 26(2), 155; https://doi.org/10.3390/e26020155 - 10 Feb 2024
Viewed by 969
Abstract
The two-dimensional sample entropy marks a significant advance in evaluating the regularity and predictability of images in the information domain. Unlike the direct computation of sample entropy, which incurs a time complexity of O(N2) for the series with N [...] Read more.
The two-dimensional sample entropy marks a significant advance in evaluating the regularity and predictability of images in the information domain. Unlike the direct computation of sample entropy, which incurs a time complexity of O(N2) for the series with N length, the Monte Carlo-based algorithm for computing one-dimensional sample entropy (MCSampEn) markedly reduces computational costs by minimizing the dependence on N. This paper extends MCSampEn to two dimensions, referred to as MCSampEn2D. This new approach substantially accelerates the estimation of two-dimensional sample entropy, outperforming the direct method by more than a thousand fold. Despite these advancements, MCSampEn2D encounters challenges with significant errors and slow convergence rates. To counter these issues, we have incorporated an upper confidence bound (UCB) strategy in MCSampEn2D. This strategy involves assigning varied upper confidence bounds in each Monte Carlo experiment iteration to enhance the algorithm’s speed and accuracy. Our evaluation of this enhanced approach, dubbed UCBMCSampEn2D, involved the use of medical and natural image data sets. The experiments demonstrate that UCBMCSampEn2D achieves a 40% reduction in computational time compared to MCSampEn2D. Furthermore, the errors with UCBMCSampEn2D are only 30% of those observed in MCSampEn2D, highlighting its improved accuracy and efficiency. Full article
(This article belongs to the Special Issue Entropy in Biomedical Engineering II)
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15 pages, 5113 KiB  
Article
Use of Intrinsic Entropy to Assess the Instantaneous Complexity of Thoracoabdominal Movement Patterns to Indicate the Effect of the Iso-Volume Maneuver Trial on the Performance of the Step Test
by Po-Hsun Huang and Tzu-Chien Hsiao
Entropy 2024, 26(1), 27; https://doi.org/10.3390/e26010027 - 26 Dec 2023
Viewed by 1017
Abstract
The recent surge in interest surrounds the analysis of physiological signals with a non-linear dynamic approach. The measurement of entropy serves as a renowned method for indicating the complexity of a signal. However, there is a dearth of research concerning the non-linear dynamic [...] Read more.
The recent surge in interest surrounds the analysis of physiological signals with a non-linear dynamic approach. The measurement of entropy serves as a renowned method for indicating the complexity of a signal. However, there is a dearth of research concerning the non-linear dynamic analysis of respiratory signals. Therefore, this study employs a novel method known as intrinsic entropy (IE) to assess the short-term dynamic changes in thoracoabdominal movement patterns, as measured by respiratory inductance plethysmography (RIP), during various states such as resting, step test, recovery, and iso-volume maneuver (IVM) trials. The findings reveal a decrease in IE of thoracic wall movement (TWM) and an increase in IE of abdominal wall movement (AWM) following the IVM trial. This suggests that AWM may dominate the breathing exercise after the IVM trial. Moreover, due to the high temporal resolution of IE, it proves to be a suitable measure for assessing the complexity of thoracoabdominal movement patterns under non-stationary states such as the step test and recovery. The results also demonstrate that the instantaneous complexity of TWM and AWM can effectively capture instantaneous changes during non-stationary states, which may prove valuable in understanding the respiratory mechanism for healthcare purposes in daily life. Full article
(This article belongs to the Special Issue Entropy in Biomedical Engineering II)
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20 pages, 3572 KiB  
Article
Multiscale Cumulative Residual Dispersion Entropy with Applications to Cardiovascular Signals
by Youngjun Kim and Young-Seok Choi
Entropy 2023, 25(11), 1562; https://doi.org/10.3390/e25111562 - 20 Nov 2023
Cited by 1 | Viewed by 1230
Abstract
Heart rate variability (HRV) is used as an index reflecting the adaptability of the autonomic nervous system to external stimuli and can be used to detect various heart diseases. Since HRVs are the time series signal with nonlinear property, entropy has been an [...] Read more.
Heart rate variability (HRV) is used as an index reflecting the adaptability of the autonomic nervous system to external stimuli and can be used to detect various heart diseases. Since HRVs are the time series signal with nonlinear property, entropy has been an attractive analysis method. Among the various entropy methods, dispersion entropy (DE) has been preferred due to its ability to quantify the time series’ underlying complexity with low computational cost. However, the order between patterns is not considered in the probability distribution of dispersion patterns for computing the DE value. Here, a multiscale cumulative residual dispersion entropy (MCRDE), which employs a cumulative residual entropy and DE estimation in multiple temporal scales, is presented. Thus, a generalized and fast estimation of complexity in temporal structures is inherited in the proposed MCRDE. To verify the performance of the proposed MCRDE, the complexity of inter-beat interval obtained from ECG signals of congestive heart failure (CHF), atrial fibrillation (AF), and the healthy group was compared. The experimental results show that MCRDE is more capable of quantifying physiological conditions than preceding multiscale entropy methods in that MCRDE achieves more statistically significant cases in terms of p-value from the Mann–Whitney test. Full article
(This article belongs to the Special Issue Entropy in Biomedical Engineering II)
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