Special Issue "Entropy in Biomedical Engineering"

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

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editor

Prof. George Manis
Website
Guest Editor
Department of Computer Science and Engineering, University of Ioannina, Ioannina 45110, Greece
Interests: biomedical engineering; entropy analysis; biomedical signal processing; computing systems

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.

This Special Issue focuses on contributions of the use of entropy in biomedical engineering, including but not limited to biomedical applications; analysis of biomedical data using entropy; contribution on entropy definitions inspired by biomedical engineering topics; entropy definitions evaluated with biomedical data; computing algorithms; and entropy as features in machine learning methods applied on biomedical data.

Prof. 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 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 1800 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 analysis
  • Biomedical applications
  • Biomedical data analysis
  • Biomedical signal processing
  • Biomedical time series analysis
  • Heart rate (HR, HRV, FHR) analysis
  • Electroencephalography (EEG) analysis
  • Photoplethysmography (PPG) analysis
  • Polysomnography (PSG) analysis
  • Computing algorithms and complexity
  • Machine learning based classification

Published Papers (3 papers)

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Research

Open AccessArticle
Multistage Segmentation of Prostate Cancer Tissues Using Sample Entropy Texture Analysis
Entropy 2020, 22(12), 1370; https://doi.org/10.3390/e22121370 - 04 Dec 2020
Viewed by 435
Abstract
In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features [...] Read more.
In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features along with sample entropy values. In a multistage segmentation process, the mean-shift algorithm is applied on the pre-processed images to perform a coarse segmentation of the tissue. Wavelet packets are employed in the second stage to obtain fine details of the structured shape of glands. Finally, the texture of the gland is modeled by the sample entropy values, which identifies epithelial regions from stroma patches. Although there are three stages of the proposed algorithm, the computation is fast as wavelet packet features and sample entropy values perform robust modeling for the required regions of interest. A comparative analysis with other state-of-the-art texture segmentation techniques is presented and dice ratios are computed for the comparison. It has been observed that our algorithm not only outperforms other techniques, but, by introducing sample entropy features, identification of cancerous regions of tissues is achieved with 90% classification accuracy, which shows the robustness of the proposed algorithm. Full article
(This article belongs to the Special Issue Entropy in Biomedical Engineering)
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Open AccessArticle
On the Variability of Heart Rate Variability—Evidence from Prospective Study of Healthy Young College Students
Entropy 2020, 22(11), 1302; https://doi.org/10.3390/e22111302 - 15 Nov 2020
Viewed by 536
Abstract
Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability [...] Read more.
Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability of healthy HRV under different settings, we recorded electrocardiograph (ECG) signals from 56 healthy young college students (20 h for each participant) at campus using wearable single-lead ECG device. Accurate R peak to R peak (RR) intervals were extracted by combing the advantages of five commonly used R-peak detection algorithms to eliminate data quality influence. Thorough and detailed linear and nonlinear HRV analyses were performed. Variability of HRV metrics were evaluated from five categories: (1) different states of daily activities; (2) different recording time period in the same day during free-running daily activities; (3) body postures of sitting and lying; (4) lying on the left, right and back; and (5) gender influence. For most of the analyzed HRV metrics, significant differences (p < 0.05) were found among different recording conditions within the five categories except lying on different positions. Results suggested that the standardization of ECG data collection and HRV analysis should be implemented in HRV related studies, especially for entropy and multi-scale entropy based analyses. Furthermore, this preliminary study provides reference values of HRV indices under various recording conditions of healthy young subjects that could be useful information for different applications (e.g., health monitoring and management). Full article
(This article belongs to the Special Issue Entropy in Biomedical Engineering)
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Open AccessArticle
Improving Accuracy of Heart Failure Detection Using Data Refinement
Entropy 2020, 22(5), 520; https://doi.org/10.3390/e22050520 - 02 May 2020
Cited by 1 | Viewed by 833
Abstract
Due to the wide inter- and intra-individual variability, short-term heart rate variability (HRV) analysis (usually 5 min) might lead to inaccuracy in detecting heart failure. Therefore, RR interval segmentation, which can reflect the individual heart condition, has been a key research challenge for [...] Read more.
Due to the wide inter- and intra-individual variability, short-term heart rate variability (HRV) analysis (usually 5 min) might lead to inaccuracy in detecting heart failure. Therefore, RR interval segmentation, which can reflect the individual heart condition, has been a key research challenge for accurate detection of heart failure. Previous studies mainly focus on analyzing the entire 24-h ECG recordings from all individuals in the database which often led to poor detection rate. In this study, we propose a set of data refinement procedures, which can automatically extract heart failure segments and yield better detection of heart failure. The procedures roughly contain three steps: (1) select fast heart rate sequences, (2) apply dynamic time warping (DTW) measure to filter out dissimilar segments, and (3) pick out individuals with large numbers of segments preserved. A physical threshold-based Sample Entropy (SampEn) was applied to distinguish congestive heart failure (CHF) subjects from normal sinus rhythm (NSR) ones, and results using the traditional threshold were also discussed. Experiment on the PhysioNet/MIT RR Interval Databases showed that in SampEn analysis (embedding dimension m = 1, tolerance threshold r = 12 ms and time series length N = 300), the accuracy value after data refinement has increased to 90.46% from 75.07%. Meanwhile, for the proposed procedures, the area under receiver operating characteristic curve (AUC) value has reached 95.73%, which outperforms the original method (i.e., without applying the proposed data refinement procedures) with AUC of 76.83%. The results have shown that our proposed data refinement procedures can significantly improve the accuracy in heart failure detection. Full article
(This article belongs to the Special Issue Entropy in Biomedical Engineering)
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