Sampling frequency of the RR interval time series for spectral analysis of heart rate variability.
Spectral analysis of heart rate variability (HRV) is an accepted method for assessment of cardiac autonomic function and its relationship to numerous disorders and diseases. Various non-parametric methods for HRV estimation have been developed and extensive literature on their respective properties is available. The RR interval time series can be seen as a series of non-uniformly spaced samples. To analyse the power spectra of this series using the discrete Fourier transform (DFT), we need to interpolate the series for obtaining uniformly spaced intervals. The selection of sampling period plays a critical role in obtaining the power spectra in terms of computational efficiency and accuracy. In this paper, we shall analyse the RR interval time series from selected subjects for different sampling frequencies to compare the error introduced in selected frequency-domain measures of HRV at a constant frequency resolution for a specific duration of electrocardiogram (ECG) data. It should be pointed out that, although many other error causes are possible in the frequency-domain measures, our attention will be confined only to the performance comparison due to the different sampling frequencies. While the choice of RR interval sampling frequency (f(s)) is arbitrary, the sampling rate of RR interval series must be selected with due consideration to mean and minimum RR interval; f(s = )4 Hz was proposed for a majority of cases. This is an appropriate sampling rate for the study of autonomic regulation, since it enables us to compute reliable spectral estimates between dc and 1 Hz, which represents the frequency band within which the autonomic nervous system has significant response. Furthermore, resampled RR intervals are evenly spaced in time and are synchronized with the samples of the other physiologic signals, enabling cross-spectral estimates with these signals.
Singh
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Electrical Engineering Department Indian Institute of Technology Roorkee, (U.A.) India.
Vinod
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Saxena
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SC
D000465:Algorithms; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005583:Fourier Analysis; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D015233:Models, Statistical; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted