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Entropy
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Entropy and Cardiac Physics III
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Entropy and Cardiac Physics III

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4603

Special Issue Editors

Dr. Gianfranco Raimondi

E-Mail Website
Guest Editor
Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 5 Piazzale Aldo Moro, 00185 Rome, Italy
Interests: cardiovascular disease; neurovegetative cardiovascular control; heart rate variability; hypertension; heart failure
Dr. Luca Barsi

E-Mail Website
Guest Editor
General Directorate for Dams, Italian Ministry of Sustainable Infrastructures and Transport, 2 Viale del Policlinico, 00161 Rome, Italy
Interests: heart rate variability; signal processing; nonlinear dynamics; wave physics; fluid dynamics

Special Issue Information

Dear Colleagues,

We aim to present innovative approaches to and applications of heart rate variability (HRV) in the interdisciplinary field of neurovegetative cardiovascular control. By presenting the cutting-edge research in this area, we hope to foster new collaborations and increase the dissemination of ideas, as well as to emphasize the importance of interdisciplinary work.

HRV provides non-invasive markers of the functioning of the autonomous nervous system (ANS). Traditionally, HRV is quantified by linear time-domain measures such as standard deviation and root mean square, or by spectral analysis of the HRV power. However, ANS is not a linear system, and it has been argued that non-linear analysis would be more suitable for HRV.

The most used non-linear methods to assess heart-rate dynamics are based on the concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, and entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy).

For this Special Issue, we welcome submissions related to the use of entropy measures in cardiovascular physics. We envisage contributions that clarify the benefit of using entropy in the neurovegetative cardiovascular control, with the ultimate goal of demonstrating its profound impact on this field.

Dr. Gianfranco Raimondi
Dr. Luca Barsi
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

  • biomedical signal processing
  • time-series analysis
  • heart rate variability
  • neurovegetative cardiovascular control
  • nonlinear dynamics
  • biomedical engineering

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Published Papers (2 papers)

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Research

11 pages, 831 KiB  
Article
Comparison of Heart Autonomic Control between Hemodynamically Stable and Unstable Patients during Hemodialysis Sessions: A Bayesian Approach
by Natália de Jesus Oliveira, Alinne Alves Oliveira, Silvania Moraes Costa, Uanderson Silva Pirôpo, Mauro Fernandes Teles, Verônica Porto de Freitas, Dieslley Amorim de Souza and Rafael Pereira
Entropy 2023, 25(6), 883; https://doi.org/10.3390/e25060883 - 31 May 2023
Cited by 1 | Viewed by 1691
Abstract
Intradialytic hypotension is a common complication during hemodialysis sessions. The analysis of successive RR interval variability using nonlinear methods represents a promising tool for evaluating the cardiovascular response to acute volemic changes. Thus, the present study aims to compare the variability of successive [...] Read more.
Intradialytic hypotension is a common complication during hemodialysis sessions. The analysis of successive RR interval variability using nonlinear methods represents a promising tool for evaluating the cardiovascular response to acute volemic changes. Thus, the present study aims to compare the variability of successive RR intervals between hemodynamically stable (HS) and unstable (HU) patients during a hemodialysis session, through linear and nonlinear methods. Forty-six chronic kidney disease patients volunteered in this study. Successive RR intervals and blood pressures were recorded throughout the hemodialysis session. Hemodynamic stability was defined based on the delta of systolic blood pressure (higher SBP-lower SBP). The cutoff for hemodynamic stability was defined as 30 mm Hg, and patients were stratified as: HS ([n = 21]: ≤29.9 mm Hg) or HU ([n = 25]: ≥30 mm Hg). Linear methods (low-frequency [LFnu] and high-frequency [HFnu] spectra) and nonlinear methods (multiscale entropy [MSE] for Scales 1–20, and fuzzy entropy) were applied. The area under the MSE curve at Scales 1–5 (MSE1–5), 6–20 (MSE6–20), and 1–20 (MSE1–20) were also used as nonlinear parameters. Frequentist and Bayesian inferences were applied to compare HS and HU patients. The HS patients exhibited a significantly higher LFnu and lower HFnu. For MSE parameters, Scales 3–20 were significantly higher, as well as MSE1–5, MSE6–20, and MSE1–20 in HS, when compared to HU patients (p < 0.05). Regarding Bayesian inference, the spectral parameters demonstrated an anecdotal (65.9%) posterior probability favoring the alternative hypothesis, while MSE exhibited moderate to very strong probability (79.4 to 96.3%) at Scales 3–20, and MSE1–5, MSE6–20, and MSE1–20. HS patients exhibited a higher heart-rate complexity than HU patients. In addition, the MSE demonstrated a greater potential than spectral methods to differentiate variability patterns in successive RR intervals. Full article
(This article belongs to the Special Issue Entropy and Cardiac Physics III)
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14 pages, 10274 KiB  
Article
Detection of Respiratory Events during Sleep Based on Fusion Analysis and Entropy Features of Cardiopulmonary Signals
by Xinlei Yan, Juan Liu, Lin Wang, Shaochang Wang, Senlin Zhang and Yi Xin
Entropy 2023, 25(6), 879; https://doi.org/10.3390/e25060879 - 30 May 2023
Cited by 1 | Viewed by 2186
Abstract
Sleep apnea hypopnea syndrome (SAHS) is a common sleep disorder with a high prevalence. The apnea hypopnea index (AHI) is an important indicator used to diagnose the severity of SAHS disorders. The calculation of the AHI is based on the accurate identification of [...] Read more.
Sleep apnea hypopnea syndrome (SAHS) is a common sleep disorder with a high prevalence. The apnea hypopnea index (AHI) is an important indicator used to diagnose the severity of SAHS disorders. The calculation of the AHI is based on the accurate identification of various types of sleep respiratory events. In this paper, we proposed an automatic detection algorithm for respiratory events during sleep. In addition to the accurate recognition of normal breathing, hypopnea and apnea events using heart rate variability (HRV), entropy and other manual features, we also presented a fusion of ribcage and abdomen movement data combined with the long short-term memory (LSTM) framework to achieve the distinction between obstructive and central apnea events. While only using electrocardiogram (ECG) features, the accuracy, precision, sensitivity, and F1 score of the XGBoost model are 0.877, 0.877, 0.876, and 0.876, respectively, demonstrating that it performs better than other models. Moreover, the accuracy, sensitivity, and F1 score of the LSTM model for detecting obstructive and central apnea events were 0.866, 0.867, and 0.866, respectively. The research results of this paper can be used for the automatic recognition of sleep respiratory events as well as AHI calculation of polysomnography (PSG), which provide a theoretical basis and algorithm references for out-of-hospital sleep monitoring. Full article
(This article belongs to the Special Issue Entropy and Cardiac Physics III)
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