Entropy Analysis of COVID-19 Cardiovascular Signals.

Dragana Bajić,Vlado Đajić,Branislav Milovanović

doi:10.3390/e23010087

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.

Highlights

There are still more questions than answers considering the effects of COVID-19 viral that alter the status of the cardiac autonomic nervous system could be tackled only by infection
The motivation for our study is a hypothesis that the complex mechanisms that alter the status of the cardiac autonomic nervous system could be tackled only by advanced multidimensional analysis of many variables, obtained both from the source cardiovascular signals, from laboratory analysis, and from detailed patient’s history
The final aim of our integral research is to extract a huge set of parameters from patients, conadvanced multidimensional analysis of many variables, obtained both from the source cardiovascular signals, from laboratory analysis, and from detailed patient’s history

Summary

Introduction

The world has faced an outbreak of a new coronavirus disease (COVID-19) at a pandemic level. It is caused by severe acute respiratory syndrome-coronavirus 2 (SARSCoV-2), and it may initiate grave respiratory problems, so the primary research focus is on pulmonary complications. Other problems have been reported, including cardiovascular [1,2,3], shown to be a possible contributor to the mortality associated with COVID-19 [4,5]. Despite numerous databases that collect the papers devoted to COVID-19 [6,7], we found only one case study of heart rate (HR) and its variability (HRV) time-series, performed in a single patient [8]

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