The effect of missing RR-interval data on heart rate variability analysis in the frequency domain
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Updated: Jun 20, 2026

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
Published on: June 5, 2019
Ko Keun Kim1, Jung Soo Kim, Yong Gyu Lim
1Interdisciplinary Program in Medical and Biological Engineering, Seoul National University, Korea.
Abstract:
In this study, optimal methods for re-sampling and spectral estimation in frequency-domain heart rate variability (HRV) analysis were investigated through a simulation using artificial RR-interval data. Nearest-neighbour, linear, cubic spline and piecewise cubic Hermite interpolation methods were considered for re-sampling and representative non-parametric, parametric, and uneven approaches were used for spectral estimation. Based on this result, the effects of missing RR-interval data on frequency-domain HRV analysis were observed through the simulation of missing data using real RR-interval tachograms. For this simulation, data including the simulated artefact section (0-100 s) were used; these data were selected randomly from the real RR data obtained from the MIT-BIH normal sinus rhythm RR-interval database. In all, 7182 tachograms of 5 min durations were used for this analysis. The analysis for certain missing data durations is performed by 100 Monte Carlo runs. TF, VLF, LF and HF were estimated as the frequency-domain parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters were calculated. Rules obtained from the results of these simulations were evaluated with real missing RR-interval data derived from a capacitive-coupled ECG during sleep.
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