Estimating spectral heart rate variability (HRV) features with missing RR-interval data

Physiological signals, ECG signal, have been widely used for diagnosis, disease identification and nowadays for self-monitoring. Missing data represents the problem in spectral analysis. This study focuses on the HRV power spectral analysis in frequency-domain using three methods with simulated missing data in real RR-interval tachograms. Actual missing ECG data are collected from the unconstrained measurement. Parametric, Non-parametric and uneven sampling approach were used for calculating the power spectral density (PSD), and cubic spline interpolation method was used for the non-parametric method. Based on this studies outcome, the effect of missing RR-interval data and optimal method was observed through the simulated real RR-interval tachograms for missing data. About 0 to 6 percentage data were removed according to the exponential Poisson distribution from the real RR-interval data for normal sinus rhythm, atrial fibrillation, tachycardia and bradycardia patient which data obtained from MIT-BIH Arrhythmia database to simulate real-world packet loss. For this analysis, 5 min duration data were used in all and 1000 Monte Carlo runs is performed for certain percentage missing data. PSD corresponding each frequency component was estimated as the frequency-domain parameters in each run and error power percentage based on each element difference between with and without the missing data duration were calculated.