Why the study?
Observational error in heart rate variability analysis can arise from factors such as limited sampling frequency and the QRS complexes detection process, but its magnitude across parameters is unclear.
Observational errors from sampling frequency and QRS detection significantly impact specific HRV parameters like pNN50 and frequency markers, highlighting the need to account for recorder differences in population studies.
Error-sensitive HRV markers like pNN50 warrant cautious use in studies; leaves open standardization of robust parameters across recorders.
An observational error of heart rate variability (HRV) may arise from many factors, such as a limited sampling frequency, QRS complexes detection process, preprocessing procedures and others. In our study, we focused on the first two origins of measurement error. We introduced a model of observational error and suggested universal descriptors for the assessment of its resultant magnitude in terms of time, frequency as well as nonlinear parameters. For this purpose, we applied Monte Carlo simulations which showed that the most sensitive to observational error are: pNN50 (the proportion of pairs of successive RR intervals that differ by more than 50 ms) and markers obtained from frequency analysis. On the other hand, the most resistant are other time domain parameters as well as the short and long-term slopes of Detrended Fluctuation Analysis (DFA). We postulate that the observational error should be considered in population studies, when different recorders are used in the research centres. Additionally, in the case of patients with similar etiology of disease but with different heart rhythms abnormalities the scatter of HRV parameters will also be observed due to the subject's the time series variability.
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Petelczyc et al. (2020) studied this question.
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