Biomedical subjects

K Vinod

Publications and source records attributed to K Vinod.

3 recordsLinked to original sources

PubMedDate not supplied

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.

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PubMedDate not supplied

An improved windowing technique for heart rate variability power spectrum estimation.

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. Discrete Fourier transform (DFT) based methods are widely used for their easy applicability, computational speed and the possibility for direct interpretation of results. This study assesses the limitation of windowing of the RR interval series of power spectrum estimation using DFT for heart rate variability studies. The mean value of the RR interval series should be subtracted before windowing. This may leave a small residual DC component after windowing, but the RR interval series is properly tapered to zero at the beginning and end of the window. However, if the windowed RR interval series has a non-zero mean then subtracting this mean will create an abrupt transition between the first and last data points, and the padded zeros. This is equivalent to superimposing upon the RR interval series a rectangular pulse of the same length as the window, with a height equal to the subtracted mean value. In the present paper an approach to overcome the above effects of the window in reducing the signal energy and introducing the low frequency component into spectrum has been suggested and incorporated. Result have been compared for DC biasing of windowed data spectrum, bias of windowed data removed by substraction of mean data, and data processed to remove windowed mean level and to maintain mean power. Thus the preprocessing of RR interval series with this method improves the accuracy of HRV analysis methods. The study was carried out by smoothing the complete RR interval series by single Hann window and by 50% overlapping the data segments of 256 data points followed by the DFT. Overlapping the data segments provides equal weight to all values in the RR interval series and smoothed spectral estimate with clearly dominant peaks in low- and high-frequency regions.

Algorithms↗

PubMedDate not supplied

Spectral evaluation of aging effects on blood pressure and heart rate variations in healthy subjects.

The background to heart rate variability (HRV) and blood pressure variability (BPV), and their determinants and physiological correlates, remain obscure. The impact of age must be taken into account if HRV and BPV are used for predictive purposes in clinical settings. Healthy subjects show wide inter-individual variation in their heart rate behaviour and the factors affecting heart rate dynamics are not well known. This paper has undertaken to evaluate heart rate variability (HRV) and baroreflex sensitivity (BRS) in a random sample of subjects without evidence of heart disease, and to estimate the relation of HRV and BPV behaviour to age. The aim of this study was to analyse the effects of ageing on HRV and BPV for simultaneous recordings of electrocardiograph (ECG) and blood pressure (BP) signals at rest in healthy subjects. We studied eight young (21-34 years old) and eight elderly (68-85 years old) rigorously screened subjects from the Fantasia Database to make the reproducibility and comparability of the results more extensive. Time- and frequency-domain analysis of HRV and BPV was performed on 5-minute ectopic-free recordings. BRS on the heart was estimated by frequency-domain analysis of spontaneous variability of systolic blood pressure (SBP) and RR interval. It has been observed that compared to young the elderly subjects have (i) diminished HRV; (ii) a shift in the power spectral density and median frequency to low frequency side for HRV and to higher frequency side for BPV; and (iii) increased low-frequency alpha index and decreased high-frequency alpha index of BRS with overall alpha index augmented. The results convey that normal ageing in the absence of disease is associated with lesser parasympathetic regulation of heart rate. Thus it is concluded that the age is an important factor to be considered for prognosis and diagnosis by HRV and BPV. For reliable clinical applications, more research needs to be done on a broad spectrum of subjects. In addition, these observations will prove to be useful for dynamic modelling of cardiovascular regulation for testing the authentication of new techniques for analysis purposes.

Adult↗