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 artifact occurrence rates and distributions, to be mimicked in simulations. After a rigorous analysis, only 16 recordings ( n = 16) with artifact-free segments of at least 10,000 beats were selected. RR interval losses were then simulated in the artifact-free (reference) time series according to real observations. Correction methods applied to simulated series were deletion, linear interpolation, cubic spline interpolation, modified moving average window, and nonlinear predictive interpolation. 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. NEW & NOTEWORTHY This work analyzes the performance of some correction techniques commonly applied to the missing beats problem in RR time series. From artifact-free RR series, spurious values were inserted based on actual data of experimental settings. We intend our work to be a guide to show how artifacts should be corrected to preserve as much as possible the original heart rate variability properties.
PEMODELAN AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE DENGAN EFEK EXPONENTIAL GARCH (ARFIMA-EGARCH) UNTUK PREDIKSI HARGA BERAS DI KOTA SEMARANG
