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Entropy
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Entropy and Nonlinear Signal Processing in Cardiovascular Applications
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Entropy and Nonlinear Signal Processing in Cardiovascular Applications

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

Deadline for manuscript submissions: 1 May 2024 | Viewed by 2647

Special Issue Editors

Prof. Dr. José J. Rieta

E-Mail Website
Guest Editor
BioMIT, Department of Electronic Engineering, Polytechnic University of Valencia, 46022 Valencia, Spain
Interests: biomedical signal processing; nonlinear signal processing; cardiovascular signals; atrial arrythmias; wearables
Special Issues, Collections and Topics in MDPI journals
Dr. Adrian Luca

E-Mail Website
Guest Editor
Service of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
Interests: cardiovascular signal processing; atrial arrhythmia; time-series analysis; nonlinear dynamics; information theory

Special Issue Information

Dear Colleagues,

Cardiovascular diseases are the leading cause of death, accounting for more than 30% of all global deaths according to the World Health Organization. Screening for cardiovascular disease risk factors as well as cardiac arrhythmia is important so that management with counselling and medicines can begin as early as possible. Technological advances in the acquisition and processing of physiological signals have continued to foster clinically efficient decisions regarding the initiation of diagnostic steps, drug therapy, and invasive strategies.

Measures of nonlinearity and complexity, such as fractal dimension, recurrence plot analysis, detrended fluctuation analysis, or entropy (conditional, approximate, sample, or multiscale entropy) have been increasingly employed to characterize the dynamics of the cardiovascular system.

The aim of this Special Issue is to cover recent developments and novel research trends in the cardiovascular field on topics related to the application of entropies and/or nonlinear methods to information and recordings from the cardiovascular system. We encourage contributions that clarify the benefit of these concepts in understanding or predicting cardiovascular behavior and getting novel mechanistic insights into cardiac diseases. Comprehensive reviews addressing different perspectives of entropy and nonlinear dynamics in cardiovascular applications are also welcome.

Prof. Dr. José J. Rieta
Dr. Adrian Luca
Guest Editors

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
  • nonlinear dynamics
  • recurrence plots
  • heart rate variability
  • photoplethysmography
  • cardiovascular signals
  • cardiac arrhythmia detection
  • applications to cardiovascular
  • atrial arrhythmias
  • atrial fibrillation
  • catheter ablation
  • electrocardiogram
  • electrogram
  • signal processing

Published Papers (2 papers)

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Research

16 pages, 392 KiB  
Article
Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis
by Pilar Escribano, Juan Ródenas, Manuel García, Fernando Hornero, Juan M. Gracia-Baena, Raúl Alcaraz and José J. Rieta
Entropy 2024, 26(1), 28; https://doi.org/10.3390/e26010028 - 27 Dec 2023
Viewed by 1068
Abstract
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox–Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study [...] Read more.
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox–Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox–Maze concomitant surgery was analyzed. The AA was extracted from the lead V1 of these recordings and then characterized using novel predictors, such as the mean and standard deviation of the relative wavelet energy (RWEm and RWEs) across different scales, and an entropy-based metric that computes the stationary wavelet entropy variability (SWEnV). The individual predictors exhibited limited predictive capabilities to anticipate the outcome of the procedure, with the SWEnV yielding a classification accuracy (Acc) of 68.07%. However, the assessment of the RWEs for the seventh scale (RWEs7), which encompassed frequencies associated with the AA, stood out as the most promising individual predictor, with sensitivity (Se) and specificity (Sp) values of 80.83% and 67.09%, respectively, and an Acc of almost 75%. Diverse multivariate decision tree-based models were constructed for prediction, giving priority to simplicity in the interpretation of the forecasting methodology. In fact, the combination of the SWEnV and RWEs7 consistently outperformed the individual predictors and excelled in predicting post-surgery outcomes one year after the Cox–Maze procedure, with Se, Sp, and Acc values of approximately 80%, thus surpassing the results of previous studies based on anatomical predictors associated with atrial function or clinical data. These findings emphasize the crucial role of preoperative patient-specific ECG signal analysis in tailoring post-surgical care, enhancing clinical decision making, and improving long-term clinical outcomes. Full article
(This article belongs to the Special Issue Entropy and Nonlinear Signal Processing in Cardiovascular Applications)
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20 pages, 5020 KiB  
Article
Analysis of the Chaotic Component of Photoplethysmography and Its Association with Hemodynamic Parameters
by Xiaoman Xing, Wen-Fei Dong, Renjie Xiao, Mingxuan Song and Chenyu Jiang
Entropy 2023, 25(12), 1582; https://doi.org/10.3390/e25121582 - 24 Nov 2023
Viewed by 836
Abstract
Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic [...] Read more.
Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios. Full article
(This article belongs to the Special Issue Entropy and Nonlinear Signal Processing in Cardiovascular Applications)
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