The effect of missing RR-interval data on heart rate variability analysis in the frequency domain

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.

CardioGuard: A Brassiere-Based Reliable ECG Monitoring Sensor System for Supporting Daily Smartphone Healthcare Applications

“…Then, it interpolates the missing peaks using a piecewise-cubic Hermite interpolation method, which shows a superior performance. 21 Finally, HR and HRV are calculated from the peaks.…”

Measurement fidelity of heart rate variability signal processing: The devil is in the details

“…Alternatively, they can be based on complex statistical algorithms that use linear or nonlinear filters, different transformations, or discriminant function analysis (Köhler et al, 2003;Pan and Tompkins, 1985;Romhilt and Estes, 1968). Interpolation algorithms, to replace missing or abnormal heart period series, include proximal, piecewise cubic Hermite, non-linear predictive interpolation, linear, and cubic spline interpolations (Kim et al, 2009;Lippman et al, 1994;Malik and Camm, 1995).…”

“…Power spectral density decomposes RR intervals into their fundamental frequency components and provides information on the distribution of power as a function of frequency. Spectral analyses can include parametric (autoregressive; Yule-Walker, Burg) or nonpara-metric methods (Fast Fourier Transform, FFT;Kim et al, 2009). FFT is most commonly used to calculate the maximum variability in heart period series, based on ranges of frequency-specific oscillations of the RR intervals that reflect different branches of the cardiac system (Lahiri et al, 2008;Spiers et al, 1993).…”

“…Time-and frequency-domain HRV indices are vulnerable to artifacts, missing data, temporal factors, and trends in RR intervals (Kim et al, 2009(Kim et al, , 2007Spiers, et al, 1993;Task Force, 1996), and are thus highly influenced by decisions for data reduction, artifact detection and removal, and technical specifications (e.g., digital filtering, sampling frequency, detector or interpolation algorithms, windowing; Kim et al, 2009;Task Force, 1996;Welch, 1967).…”

Applying learning algorithms to extract anxiety levels using the heart rate variability measure

“…Most of these analyses include techniques of data compression searching for the better quality of the signal or to decompose it aiming to classify the differences between the heart rate variability in specific conditions. [5][6][7][8] Statistical techniques are used to extract biological information about the volunteer conditions and tools like fuzzy algorithms or neural networks are common in these biological applications. In this vein, the most traditional technique to analyze the HRV signal is the Fourier Transform and Series.…”