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Application of Information Theory and Entropy in Cardiology

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

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 8578

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Dr. Hiroshi Ashikaga

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Guest Editor

Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Interests: ventricular fibrillation; information theory; complex systems; artificial intelligence

Special Issue Information

Dear Colleagues,

The human heart is a complex system composed of 5 billion autonomous cardiomyocytes that interact with each other with simple rules of operation and minimal central control. This interaction, through a reaction–diffusion process, leads to system behaviors at multiple scales. At the microscopic scale, the system behavior is characterized by transitions of cardiomyocyte states between excitation and relaxation. This relatively simple micro-scale behavior, by creating a series of traveling waves, can generate a multitude of arrhythmia at the macroscopic scale that controls the life and death of millions of human beings worldwide.

Like any other complex system, the heart is non-Markovian and non-ergodic, because it is out-of-equilibrium, path- and history-dependent, has a long memory, and has long-range interactions. As such, the heart violates Shannon–Khinchin’s composition axiom, a requirement for an entropy to be a measure for uncertainty. Nevertheless, information theory continues to be an important and useful tool to improve the understanding of heart disease and complex systems in general.

This Special Issue will focus on the application of information theory in Cardiology to understand 1) the relationship between micro- and macro-scale behaviors of the heart, 2) phase transitions in the cardiac system, and 3) the mechanism of heart disease.

Papers exploring topics from molecular to population scales will be considered. Papers describing information-theoretic approaches and findings that are applicable to other complex systems are particularly preferred. Theoretical and numerical investigations are also welcome.

Prof. Hiroshi Ashikaga
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.

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Research

18 pages, 3473 KiB

Open AccessArticle

Towards the Development of Nonlinear Approaches to Discriminate AF from NSR Using a Single-Lead ECG

by Jieun Lee, Yugene Guo, Vasanth Ravikumar and Elena G. Tolkacheva

Entropy 2020, 22(5), 531; https://doi.org/10.3390/e22050531 - 08 May 2020

Cited by 3 | Viewed by 2433

Abstract

Paroxysmal atrial fibrillation (Paro. AF) is challenging to identify at the right moment. This disease is often undiagnosed using currently existing methods. Nonlinear analysis is gaining importance due to its capability to provide more insight into complex heart dynamics. The aim of this study is to use several recently developed nonlinear techniques to discriminate persistent AF (Pers. AF) from normal sinus rhythm (NSR), and more importantly, Paro. AF from NSR, using short-term single-lead electrocardiogram (ECG) signals. Specifically, we adapted and modified the time-delayed embedding method to minimize incorrect embedding parameter selection and further support to reconstruct proper phase plots of NSR and AF heart dynamics, from MIT-BIH databases. We also examine information-based methods, such as multiscale entropy (MSE) and kurtosis (Kt) for the same purposes. Our results demonstrate that embedding parameter time delay (

τ

), as well as MSE and Kt values can be successfully used to discriminate between Pers. AF and NSR. Moreover, we demonstrate that

τ

and Kt can successfully discriminate Paro. AF from NSR. Our results suggest that nonlinear time-delayed embedding method and information-based methods provide robust discriminating features to distinguish both Pers. AF and Paro. AF from NSR, thus offering effective treatment before suffering chaotic Pers. AF. Full article

(This article belongs to the Special Issue Application of Information Theory and Entropy in Cardiology)

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15 pages, 1167 KiB

Open AccessArticle

Entropy-Based Measures of Hypnopompic Heart Rate Variability Contribute to the Automatic Prediction of Cardiovascular Events

by Xueya Yan, Lulu Zhang, Jinlian Li, Ding Du and Fengzhen Hou

Entropy 2020, 22(2), 241; https://doi.org/10.3390/e22020241 - 20 Feb 2020

Cited by 14 | Viewed by 2540

Abstract

Surges in sympathetic activity should be a major contributor to the frequent occurrence of cardiovascular events towards the end of nocturnal sleep. We aimed to investigate whether the analysis of hypnopompic heart rate variability (HRV) could assist in the prediction of cardiovascular disease (CVD). 2217 baseline CVD-free subjects were identified and divided into CVD group and non-CVD group, according to the presence of CVD during a follow-up visit. HRV measures derived from time domain analysis, frequency domain analysis and nonlinear analysis were employed to characterize cardiac functioning. Machine learning models for both long-term and short-term CVD prediction were then constructed, based on hypnopompic HRV metrics and other typical CVD risk factors. CVD was associated with significant alterations in hypnopompic HRV. An accuracy of 81.4% was achieved in short-term prediction of CVD, demonstrating a 10.7% increase compared with long-term prediction. There was a decline of more than 6% in the predictive performance of short-term CVD outcomes without HRV metrics. The complexity of hypnopompic HRV, measured by entropy-based indices, contributed considerably to the prediction and achieved greater importance in the proposed models than conventional HRV measures. Our findings suggest that Hypnopompic HRV assists the prediction of CVD outcomes, especially the occurrence of CVD event within two years. Full article

(This article belongs to the Special Issue Application of Information Theory and Entropy in Cardiology)

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20 pages, 8566 KiB

Open AccessArticle

A New Physically Meaningful Threshold of Sample Entropy for Detecting Cardiovascular Diseases

by Jinle Xiong, Xueyu Liang, Tingting Zhu, Lina Zhao, Jianqing Li and Chengyu Liu

Entropy 2019, 21(9), 830; https://doi.org/10.3390/e21090830 - 25 Aug 2019

Cited by 8 | Viewed by 3086

Abstract

Sample Entropy (SampEn) is a popular method for assessing the regularity of physiological signals. Prior to the entropy calculation, certain common parameters need to be initialized: Embedding dimension m, tolerance threshold r and time series length N. Nevertheless, the determination of these parameters is usually based on expert experience. Improper assignments of these parameters tend to bring invalid values, inconsistency and low statistical significance in entropy calculation. In this study, we proposed a new tolerance threshold with physical meaning (

r_{p}

), which was based on the sampling resolution of physiological signals. Statistical significance, percentage of invalid entropy values and ROC curve were used to evaluate the proposed

r_{p}

against the traditional threshold (

r_{t}

). Normal sinus rhythm (NSR), congestive heart failure (CHF) as well as atrial fibrillation (AF) RR interval recordings from Physionet were used as the test data. The results demonstrated that the proposed

r_{p}

had better stability than

r_{t}

, hence more adaptive to detect cardiovascular diseases of CHF and AF. Full article

(This article belongs to the Special Issue Application of Information Theory and Entropy in Cardiology)

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