A Permutation Entropy analysis to determine significant daily intervals to improve risk stratification tasks from COVID patients

Abstract

SARS-CoV-2 infection has a wide range of clinical manifestations making its diagnosis difficult, which is an important problem to solve. We evaluated heart rate data extracted from the Stanford University database. The data set considers heart rate and step records of 118 patients, where 90 correspond to healthy individuals and 28 patients with COVID. Each daily record was divided into 5-minute segments, providing 288 data per patient. The date of symptom onset was considered as a reference point to extract subsets of data whose variability was considerable, such as 30 days before the date and 30 days after it. Each of the 60 segments of 288 data per patient was treated using Permutation Entropy, Approximate Entropy, Spectral Entropy and Singular Value Decomposition Entropy. The average of the data from each group was used to construct the circadian profiles which were analyzed using the Mann-Whitney-Wilcoxon test, determining the most relevant 5-minute segments, whose p-value was less than 0.05. In this way, the Spectral Entropy was discarded as it did not show any significantly different segment. The efficiency of the method was reflected in the performance of a logistic model for binary classification proposed in this work, which reflected an accuracy of 94.12% in the PE case, 88% in the ApEn case and 94% in the SVDE case. The proposed analysis turns out to be highly efficient when detecting significant segments that allow improving the classification tasks carried out by Machine Learning models, which provides a basis for the study of statistics such as entropy to delimit databases and improve the performance of classifier models.