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Information Theory in Emerging Biomedical Applications

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 16276

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Prof. Dr. Dragana Bajic

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Guest Editor

Faculty of Technical Sciences, University of Novi Sad, Novi Sad 21000, Serbia
Interests: sequences; synchronization; entropy; cardiovascular time series; dependency structures
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Special Issue Information

Dear Colleagues,

The majority of the human population possess mobile phones, but not many people know that the capacity to transmit an enormous quantity of—digital—data are a contribution of a single person: Claude Elwood Shannon. His now historical papers from 1948-49 are devoted to sampling, entropy, and cryptography, from which information theory emerged. It took almost 40 years to penetrate other scientific disciplines, with biomedical sciences leading the way. The biomedical papers that use variations on entropy themes are counted in the thousands, and indeed, there is no field that offers such a variety of different signals as in biomedicine.

The focus of this Special Issue is the application of tools that emerged from information theory to (multivariable) analysis of biomedical data. This covers a variety of topics, from extracting otherwise hidden information that reveals the complex relationship between seemingly unrelated variables to DNA data storage. The topics include, but are not limited to, entropy and complexity analysis, copula density and copula entropy, the question of whether information can be squeezed from the bottleneck principle, dimensionality reduction and machine learning, compression and complexity, DNA storage and alignment, nano-signals, but also practical problem-solving applications considering biomedical time series and images. Review papers are welcomed. The scope is broad and provides many opportunities for contributions.

Prof. Dr. Dragana Bajic
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

biomedical signals and images
entropy
copula
DNA alignment and storage
dimensionality reduction
multivariable analysis

Published Papers (5 papers)

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Research

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19 pages, 3151 KiB

Open AccessArticle

Multivariate Gaussian Copula Mutual Information to Estimate Functional Connectivity with Less Random Architecture

by Mahnaz Ashrafi and Hamid Soltanian-Zadeh

Entropy 2022, 24(5), 631; https://doi.org/10.3390/e24050631 - 29 Apr 2022

Cited by 2 | Viewed by 2296

Abstract

Recognition of a brain region’s interaction is an important field in neuroscience. Most studies use the Pearson correlation to find the interaction between the regions. According to the experimental evidence, there is a nonlinear dependence between the activities of different brain regions that is ignored by Pearson correlation as a linear measure. Typically, the average activity of each region is used as input because it is a univariate measure. This dimensional reduction, i.e., averaging, leads to a loss of spatial information across voxels within the region. In this study, we propose using an information-theoretic measure, multivariate mutual information (mvMI), as a nonlinear dependence to find the interaction between regions. This measure, which has been recently proposed, simplifies the mutual information calculation complexity using the Gaussian copula. Using simulated data, we show that the using this measure overcomes the mentioned limitations. Additionally using the real resting-state fMRI data, we compare the level of significance and randomness of graphs constructed using different methods. Our results indicate that the proposed method estimates the functional connectivity more significantly and leads to a smaller number of random connections than the common measure, Pearson correlation. Moreover, we find that the similarity of the estimated functional networks of the individuals is higher when the proposed method is used. Full article

(This article belongs to the Special Issue Information Theory in Emerging Biomedical Applications)

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20 pages, 6525 KiB

Open AccessArticle

CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network

by Jameel Ahmed Bhutto, Lianfang Tian, Qiliang Du, Zhengzheng Sun, Lubin Yu and Muhammad Faizan Tahir

Entropy 2022, 24(3), 393; https://doi.org/10.3390/e24030393 - 11 Mar 2022

Cited by 17 | Viewed by 4188

Abstract

Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat–top-hat strategy. Then, grey-principal component analysis (grey-PCA) is used to transform RGB images into gray images that can preserve detailed features. After that, the local shift-invariant shearlet transform (LSIST) method decomposes the images into the low-pass (LP) and high-pass (HP) sub-bands, efficiently restoring all significant characteristics in various scales and directions. The HP sub-bands are fed to two branches of the Siamese convolutional neural network (CNN) by process of feature detection, initial segmentation, and consistency verification to effectively capture smooth edges, and textures. While the LP sub-bands are fused by employing local energy fusion using the averaging and selection mode to restore the energy information. The proposed method is validated by subjective and objective quality assessments. The subjective evaluation is conducted by a user case study in which twelve field specialists verified the superiority of the proposed method based on precise details, image contrast, noise in the fused image, and no loss of information. The supremacy of the proposed method is further justified by obtaining 0.6836 to 0.8794, 0.5234 to 0.6710, and 3.8501 to 8.7937 gain for

Q_{F}^{AB}

, CRR, and AG and noise reduction from 0.3397 to 0.1209 over other methods for objective parameters. Full article

(This article belongs to the Special Issue Information Theory in Emerging Biomedical Applications)

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19 pages, 38303 KiB

Open AccessArticle

On Quantization Errors in Approximate and Sample Entropy

by Dragana Bajić and Nina Japundžić-Žigon

Entropy 2022, 24(1), 73; https://doi.org/10.3390/e24010073 - 31 Dec 2021

Cited by 5 | Viewed by 1632

Abstract

Approximate and sample entropies are acclaimed tools for quantifying the regularity and unpredictability of time series. This paper analyses the causes of their inconsistencies. It is shown that the major problem is a coarse quantization of matching probabilities, causing a large error between their estimated and true values. Error distribution is symmetric, so in sample entropy, where matching probabilities are directly summed, errors cancel each other. In approximate entropy, errors are accumulating, as sums involve logarithms of matching probabilities. Increasing the time series length increases the number of quantization levels, and errors in entropy disappear both in approximate and in sample entropies. The distribution of time series also affects the errors. If it is asymmetric, the matching probabilities are asymmetric as well, so the matching probability errors cease to be mutually canceled and cause a persistent entropy error. Despite the accepted opinion, the influence of self-matching is marginal as it just shifts the error distribution along the error axis by the matching probability quant. Artificial lengthening the time series by interpolation, on the other hand, induces large error as interpolated samples are statistically dependent and destroy the level of unpredictability that is inherent to the original signal. Full article

(This article belongs to the Special Issue Information Theory in Emerging Biomedical Applications)

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22 pages, 4727 KiB

Open AccessArticle

Reduction of Artifacts in Capacitive Electrocardiogram Signals of Driving Subjects

by Tamara Škorić

Entropy 2022, 24(1), 13; https://doi.org/10.3390/e24010013 - 22 Dec 2021

Cited by 4 | Viewed by 2541

Abstract

The development of smart cars with e-health services allows monitoring of the health condition of the driver. Driver comfort is preserved by the use of capacitive electrodes, but the recorded signal is characterized by large artifacts. This paper proposes a method for reducing artifacts from the ECG signal recorded by capacitive electrodes (cECG) in moving subjects. Two dominant artifact types are coarse and slow-changing artifacts. Slow-changing artifacts removal by classical filtering is not feasible as the spectral bands of artifacts and cECG overlap, mostly in the band from 0.5 to 15 Hz. We developed a method for artifact removal, based on estimating the fluctuation around linear trend, for both artifact types, including a condition for determining the presence of coarse artifacts. The method was validated on cECG recorded while driving, with the artifacts predominantly due to the movements, as well as on cECG recorded while lying, where the movements were performed according to a predefined protocol. The proposed method eliminates 96% to 100% of the coarse artifacts, while the slow-changing artifacts are completely reduced for the recorded cECG signals larger than 0.3 V. The obtained results are in accordance with the opinion of medical experts. The method is intended for reliable extraction of cardiovascular parameters to monitor driver fatigue status. Full article

(This article belongs to the Special Issue Information Theory in Emerging Biomedical Applications)

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Review

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23 pages, 1055 KiB

Open AccessReview

Breaking Barriers in Emerging Biomedical Applications

by Konstantinos Katzis, Lazar Berbakov, Gordana Gardašević and Olivera Šveljo

Entropy 2022, 24(2), 226; https://doi.org/10.3390/e24020226 - 31 Jan 2022

Cited by 4 | Viewed by 4360

Abstract

The recent global COVID-19 pandemic has revealed that the current healthcare system in modern society can hardly cope with the increased number of patients. Part of the load can be alleviated by incorporating smart healthcare infrastructure in the current system to enable patient’s remote monitoring and personalized treatment. Technological advances in communications and sensing devices have enabled the development of new, portable, and more power-efficient biomedical sensors, as well as innovative healthcare applications. Nevertheless, such applications require reliable, resilient, and secure networks. This paper aims to identify the communication requirements for mass deployment of such smart healthcare sensors by providing the overview of underlying Internet of Things (IoT) technologies. Moreover, it highlights the importance of information theory in understanding the limits and barriers in this emerging field. With this motivation, the paper indicates how data compression and entropy used in security algorithms may pave the way towards mass deployment of such IoT healthcare devices. Future medical practices and paradigms are also discussed. Full article

(This article belongs to the Special Issue Information Theory in Emerging Biomedical Applications)

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