hrv-analysis's Mean RR was calculated as 710.32 ms, with SDNN of 64.108 ms, showing similar results when compared with Kubios software with negligible relative errors.
The hrv-analysis Python package provides an open-source, accurate tool for heart rate variability analysis and signal preprocessing, comparable to standard proprietary software.
This paper presents 'hrv-analysis', a Python package for Heart Rate Variability (HRV) analysis. 'hrv-analysis' is an open-source package for the Python statistical computing environment, which supports a wide variety of time, frequency and non-linear HRV analysis methods. It also includes several functions for signal preprocessing like outliers and ectopic beat removal. This package is suitable for researchers, professionals working in healthcare as well as for developers and more widely for anyone willing to perform detailed analysis on heart rate variability. 'hrv-analysis' was developed as part of the Aura project. Aura is a non-profit project aiming to develop an open-source forecasting system to help people suffering from epilepsy regain autonomy in their everyday lives. This package was developed using Python 3.
Champseix et al. (Wed,) conducted a other in Heart Rate Variability (HRV). hrv-analysis vs. Kubios HRV (v3.1) was evaluated on Comparison of time-domain and non-linear domain features for HRV analysis. hrv-analysis's Mean RR was calculated as 710.32 ms, with SDNN of 64.108 ms, showing similar results when compared with Kubios software with negligible relative errors.