Estimating spectral HRV features with missing data

Abstract

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 R-R interval data and optimal method was observed through the simulated real R-R interval tachograms for missing data. About 0 to 6 percentage data were removed according to the exponential Poisson distribution from the real R-R 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. Power spectral density (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. In addition, this study revealed that power spectral entropy measurement from power spectral density which differentiates between different arrhythmias.