Key result
Wavelet detrending outperforms smoothness prior and empirical mode decomposition for artificial R-R interval analysis.
Why the study?
Non-stationary trends in R-R interval series need to be removed to obtain reliable frequency domain analysis of heart rate variability.
Wavelet detrending may improve HRV spectral reliability; leaves open optimal method selection for clinical use.
Non-stationary trend in R-R interval series is suggested to be removed to obtain reliable results for it could highly influence the frequency domain analysis of heart rate variability. In this study, three detrending methods, the smoothness prior approach, the wavelet and the empirical mode decomposition, were compared on artificial R-R interval series with four types of simulated trends. The Lomb-Scargle periodogram was used for frequency domain analysis. Results indicated that the wavelet method showed a better overall performance than the other two methods, and more time-saving, too. Thus the wavelet method is recommended as an optimal choice for use.
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Li et al. (2011) studied Heart rate variability analysis. Wavelet detrending method vs. Smoothness prior approach and empirical mode decomposition was evaluated on Overall performance and time efficiency in frequency domain analysis. The wavelet detrending method demonstrated better overall performance and time efficiency than smoothness prior and empirical mode decomposition for artificial R-R interval series.
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