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.

中文翻译：

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COVID-19心血管信号的熵分析

世界面临着冠状病毒的爆发，除了肺部并发症外，它还引起了其他严重的问题，包括心血管疾病。对于冠状病毒引发心脏自主神经系统（ANS）功能障碍的机制，仍然没有解释。我们认为，改变 ANS 状态的复杂机制只能通过对许多变量的高级多维分析来解决，这些变量来自原始心血管信号以及实验室分析和详细的患者病史。本文的目的是分析作为心血管数据多维空间的潜在维度的熵的不同度量。这些措施应用于从 116 名 COVID-19 患者和 77 名健康对照者收集的心率和收缩压信号。表明不同感染水平的患者与健康对照之间存在统计学显着差异的方法将用于进一步的多变量研究。结果表明，应用于集成转换概率信号的样本熵、常见的符号动态熵和 copula 参数表明，健康对照和 COVID-19 患者之间存在统计学上的显着差异。只有通过心率信号和收缩压的交叉熵才能实现 COVID-19 重症和轻度患者之间的统计显着性。这一结果有助于假设 COVID-19 疾病的严重程度与 ANS 障碍相关，并鼓励进一步研究。