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  1. Article ; Online: Entropy Analysis of COVID-19 Cardiovascular Signals.

    Bajić, Dragana / Đajić, Vlado / Milovanović, Branislav

    Entropy (Basel, Switzerland)

    2021  Volume 23, Issue 1

    Abstract: ... with COVID-19 can only be achieved by cross-entropies of heart rate signals and systolic pressure ... 19 was shown by sample entropy applied to integrated transformed probability signals, common symbolic ... to heart rate and systolic blood pressure signals collected from 116 patients with COVID-19 and 77 healthy ...

    Abstract The world has faced a coronavirus outbreak, which, in addition to lung complications, has caused other serious problems, including cardiovascular. There is still no explanation for the mechanisms of coronavirus that trigger dysfunction of the cardiac autonomic nervous system (ANS). We believe that the complex mechanisms that change the status of ANS could only be solved by advanced multidimensional analysis of many variables, obtained both from the original cardiovascular signals and from laboratory analysis and detailed patient history. The aim of this paper is to analyze different measures of entropy as potential dimensions of the multidimensional space of cardiovascular data. The measures were applied to heart rate and systolic blood pressure signals collected from 116 patients with COVID-19 and 77 healthy controls. Methods that indicate a statistically significant difference between patients with different levels of infection and healthy controls will be used for further multivariate research. As a result, it was shown that a statistically significant difference between healthy controls and patients with COVID-19 was shown by sample entropy applied to integrated transformed probability signals, common symbolic dynamics entropy, and copula parameters. Statistical significance between serious and mild patients with COVID-19 can only be achieved by cross-entropies of heart rate signals and systolic pressure. This result contributes to the hypothesis that the severity of COVID-19 disease is associated with ANS disorder and encourages further research.
    Language English
    Publishing date 2021-01-09
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e23010087
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Entropy Analysis of COVID-19 Cardiovascular Signals

    Dragana Bajić / Vlado Đajić / Branislav Milovanović

    Entropy, Vol 23, Iss 1, p

    2021  Volume 87

    Abstract: ... with COVID-19 can only be achieved by cross-entropies of heart rate signals and systolic pressure ... 19 was shown by sample entropy applied to integrated transformed probability signals, common symbolic ... to heart rate and systolic blood pressure signals collected from 116 patients with COVID-19 and 77 healthy ...

    Abstract The world has faced a coronavirus outbreak, which, in addition to lung complications, has caused other serious problems, including cardiovascular. There is still no explanation for the mechanisms of coronavirus that trigger dysfunction of the cardiac autonomic nervous system (ANS). We believe that the complex mechanisms that change the status of ANS could only be solved by advanced multidimensional analysis of many variables, obtained both from the original cardiovascular signals and from laboratory analysis and detailed patient history. The aim of this paper is to analyze different measures of entropy as potential dimensions of the multidimensional space of cardiovascular data. The measures were applied to heart rate and systolic blood pressure signals collected from 116 patients with COVID-19 and 77 healthy controls. Methods that indicate a statistically significant difference between patients with different levels of infection and healthy controls will be used for further multivariate research. As a result, it was shown that a statistically significant difference between healthy controls and patients with COVID-19 was shown by sample entropy applied to integrated transformed probability signals, common symbolic dynamics entropy, and copula parameters. Statistical significance between serious and mild patients with COVID-19 can only be achieved by cross-entropies of heart rate signals and systolic pressure. This result contributes to the hypothesis that the severity of COVID-19 disease is associated with ANS disorder and encourages further research.
    Keywords COVID-19 ; sample entropy ; dependency structures ; composite multiscale entropy ; copula ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Subject code 610
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery.

    Alassafi, Madini O / Aziz, Wajid / AlGhamdi, Rayed / Alshdadi, Abdulrahman A / Nadeem, Malik Sajjad Ahmed / Khan, Ishtiaq Rasool / Albishry, Nabeel / Bahaddad, Adel / Altalbe, Ali

    Computers in biology and medicine

    2024  Volume 170, Page(s) 108032

    Abstract: ... approaches in the context of classifying OSV signals from COVID-19 patients during their illness and ... a valuable approach for categorizing OSV signals obtained during and after COVID-19, ultimately aiding ... a reduction in signal complexity during the course of COVID-19. Leveraging these SBE measures as feature sets ...

    Abstract COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact the cardiovascular system, leading to a range of cardiorespiratory complications. The current forefront in analyzing the dynamical characteristics of physiological systems and aiding clinical decision-making involves the integration of entropy-based complexity techniques with artificial intelligence. Entropy-based measures offer promising prospects for identifying disturbances in cardiorespiratory control system (CRCS) among COVID-19 patients by assessing the oxygen saturation variability (OSV) signals. In this investigation, we employ scale-based entropy (SBE) methods, including multiscale entropy (MSE), multiscale permutation entropy (MPE), and multiscale fuzzy entropy (MFE), to characterize the dynamical characteristics of OSV signals. These measurements serve as features for the application of traditional machine learning (ML) and deep learning (DL) approaches in the context of classifying OSV signals from COVID-19 patients during their illness and subsequent recovery. We use the Beurer PO-80 pulse oximeter which non-invasively acquired OSV and pulse rate data from COVID-19 infected patients during the active infection phase and after a two-month recovery period. The dataset comprises of 88 recordings collected from 44 subjects(26 men and 18 women), both during their COVID-19 illness and two months post-recovery. Prior to analysis, data preprocessing is performed to remove artifacts and outliers. The application of SBE measures to OSV signals unveils a reduction in signal complexity during the course of COVID-19. Leveraging these SBE measures as feature sets, we employ two DL techniques, namely the radial basis function network (RBFN) and RBFN with dynamic delay algorithm (RBFNDDA), for the classification of OSV data collected during and after COVID-19 recovery. To evaluate the classification performance, we employ standard metrics such as sensitivity, specificity, false positive rate (FPR), and the area under the receiver operator characteristic curve (AUC). Among the three scale-based entropy measures, MFE outperformed MSE and MPE by achieving the highest classification performance using RBFN with 13 best features having sensitivity (0.84), FPR (0.30), specificity (0.70) and AUC (0.77). The outcomes of our study demonstrate that SBE measures combined with DL methods offer a valuable approach for categorizing OSV signals obtained during and after COVID-19, ultimately aiding in the detection of CRCS dysfunction.
    MeSH term(s) Male ; Humans ; Female ; Entropy ; Artificial Intelligence ; Deep Learning ; Electroencephalography/methods ; COVID-19
    Language English
    Publishing date 2024-02-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2024.108032
    Database MEDical Literature Analysis and Retrieval System OnLINE

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