The impact of artifact correction methods of RR series on heart rate variability parameters

Abstract

Heart rate variability (HRV) analysis is widely used to investigate the autonomic regulation of the cardiovascular system. HRV is often analyzed using RR time series, which can be affected by different types of artifacts. Although there are several artifact correction methods, there is no study that compares their performances in actual experimental contexts. This work aimed to evaluate the impact of different artifact correction methods on several HRV parameters. Initially, 36 ECG recordings of control rats or rats with heart failure or hypertension were analyzed to characterize artifacts occurrence rates and distributions, in order to be mimicked in simulations. After a rigorous analysis, only sixteen recordings (N=16) with artifact-free segments of at least 10.000 beats were selected. Then, RR interval losses were simulated in the artifact-free (reference) time series according to real observations. Correction methods applied to simulated series were deletion (DEL), linear interpolation (LI), cubic spline interpolation (CI), modified moving average window (mMAW) and nonlinear predictive interpolation (NPI). Linear (time- and frequency-domain) and nonlinear HRV parameters were calculated from corrupted-corrected time series, as well as for reference series to evaluate the accuracy of each correction method. Results show that NPI provides the overall best performance. However, several correction approaches, for example, the simple deletion procedure, can provide good performance in some situations, depending on the HRV parameters under consideration.