Abstract
Heart Rate Variability (HRV) analysis aims to characterize the physiological state affecting heart rate, and identify potential markers of underlying pathologies. This typically involves calculating various HRV indices for each recording of two or more populations. Then, statistical tests are used to find differences. The normality of the indices, the number of groups being compared, and the correction of the significance level should be considered in this step. Especially for large studies, this process is tedious and error-prone. This paper presents RHRVEasy, an R open-source package that automates all the steps of HRV analysis. RHRVEasy takes as input a list of folders, each containing all the recordings of the same population. The package loads and preprocesses heart rate data, and computes up to 31 HRV time, frequency, and non-linear indices. Notably, it automates the computation of non-linear indices, which typically demands manual intervention. It then conducts hypothesis tests to find differences between the populations, adjusting significance levels if necessary. It also performs a post-hoc analysis to identify the differing groups if there are more than two populations. RHRVEasy was validated using a database of healthy subjects, and another of congestive heart failure patients. Significant differences in many HRV indices are expected between these groups. Two additional groups were constructed by random sampling of the original databases. Each of these groups should present no statistically significant differences with the group from which it was sampled, and it should present differences with the other two groups. All tests produced the expected results, demonstrating the software's capability in simplifying HRV analysis. Code is available on https://github.com/constantino-garcia/RHRVEasy.