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Ageing effects on HRV dynamics: a comparative study with FFT and AR models

Ageing effects on HRV dynamics: a comparative study with FFT and AR models The present study aims to establish normal limits of short–term Heart Rate Variability (HRV) indices with the two popular spectral domain methods of Fast Fourier Transform (FFT) and Autoregressive (AR) model in three psychosomatically important age groups of healthy male subjects. The three considered age groups are 18–30 years, 30–45 years and 45–60 years and HRV decreases in the higher age groups when evaluated with both the FFT and AR models. Vagal control becomes weak with the ageing process. However, the corresponding HRV indices computed with both the FFT and AR models differ quantitatively, which may be due to methodological differences. The FFT–based technique evaluates HRV indices on the actual RR interval series length and hence, the HRV indices using this technique are considered to be highly accurate, where as the AR model works on the basis of linear prediction. This study may help towards optimisation of AR models such that the HRV indices calculated with both FFT and AR models are nearly the same. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Ageing effects on HRV dynamics: a comparative study with FFT and AR models

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2013.056636
Publisher site
See Article on Publisher Site

Abstract

The present study aims to establish normal limits of short–term Heart Rate Variability (HRV) indices with the two popular spectral domain methods of Fast Fourier Transform (FFT) and Autoregressive (AR) model in three psychosomatically important age groups of healthy male subjects. The three considered age groups are 18–30 years, 30–45 years and 45–60 years and HRV decreases in the higher age groups when evaluated with both the FFT and AR models. Vagal control becomes weak with the ageing process. However, the corresponding HRV indices computed with both the FFT and AR models differ quantitatively, which may be due to methodological differences. The FFT–based technique evaluates HRV indices on the actual RR interval series length and hence, the HRV indices using this technique are considered to be highly accurate, where as the AR model works on the basis of linear prediction. This study may help towards optimisation of AR models such that the HRV indices calculated with both FFT and AR models are nearly the same.

Journal

International Journal of Signal and Imaging Systems Engineering – Inderscience Publishers

Published: Jan 1, 2013

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