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Entropy Analysis of COVID-19 Cardiovascular Signals
Dragana Bajić1, Vlado Đajić2, Branislav Milovanović3,4
1Faculty of Technical Sciences, University of Novi Sad, Novi Sad 21000, Serbia.
Insights
COVID-19 can cause cardiovascular problems by disrupting the cardiac autonomic nervous system (ANS). Entropy measures reveal significant differences in ANS function between healthy individuals and COVID-19 patients, indicating potential disease severity markers.
Area of Science:
- Cardiology
- Computational Biology
- Infectious Diseases
Background:
- The coronavirus (COVID-19) outbreak has led to significant cardiovascular complications.
- The precise mechanisms by which COVID-19 affects the cardiac autonomic nervous system (ANS) remain unclear.
- Understanding these mechanisms requires advanced multidimensional analysis of cardiovascular signals and patient data.
Purpose of the Study:
- To investigate various entropy measures as potential dimensions for analyzing cardiovascular data in COVID-19 patients.
- To identify entropy-based markers that differentiate between COVID-19 patients and healthy controls.
- To explore entropy measures for distinguishing between different severity levels of COVID-19.
Main Methods:
- Analysis of heart rate and systolic blood pressure signals from 116 COVID-19 patients and 77 healthy controls.
- Application of diverse entropy measures, including sample entropy, symbolic dynamics entropy, and copula parameters.
- Utilizing statistical methods to identify significant differences between groups.
Main Results:
- Sample entropy on transformed probability signals, common symbolic dynamics entropy, and copula parameters showed statistically significant differences between COVID-19 patients and healthy controls.
- Cross-entropies of heart rate and systolic pressure signals revealed statistical significance between severe and mild COVID-19 cases.
- These findings suggest a link between COVID-19 severity and ANS dysfunction.
Conclusions:
- Entropy measures can effectively differentiate cardiovascular autonomic nervous system (ANS) function in COVID-19 patients compared to healthy individuals.
- Specific entropy metrics show promise in assessing COVID-19 disease severity.
- Further research into ANS dysfunction in COVID-19 is warranted, utilizing these advanced analytical approaches.
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.
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