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Comparison of Detrending Methods in Spectral Analysis of Heart Rate Variability

Authors: Liping Li, Ke Li, Changchun Liu, Chengyu Liu


Non-stationary trend in R-R interval series is considered as a main factor that could highly influence the evaluation of spectral analysis. It is suggested to remove trends in order to obtain reliable results. 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 spectral analysis of R-R interval series. Results indicated that the wavelet method showed a better overall performance than the other two methods, and more time-saving, too. Therefore it was selected for spectral analysis of real R-R interval series of thirty-seven healthy subjects. Significant decreases (19.94±5.87% in the low frequency band and 18.97±5.78% in the ratio (p<0.001)) were found. Thus the wavelet method is recommended as an optimal choice for use.

Keywords: Wavelet, heart rate variability, empirical mode decomposition, signal detrending, smoothness priors

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[1] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, "Heart rate variability: standards of measurement, physiological interpretation and clinical use," Circulation, vol.93, no.5, pp. 1043-1065, 1996.
[2] G. Berntson, J. Bigger, D. Eckberg, et al., "Heart rate variability: origins, methods, and interpretive caveats," Psychophysiology, vol.34, no.6, pp. 623-648, 1997.
[3] A. Rajendra, J. Paul, N. Kannathal, et al., "Heart rate variability: a review," Med Bio Eng Comput, vol.44, no.12, pp. 1031-1051, 2006.
[4] M. Malik and A. Camm, "Heart rate variability," Clin Cardiol, vol.13, no.8, pp. 570-576, 1990.
[5] Y. Gang and M. Malik, "Heart Rate Variability: Measurements and Risk Stratification," in Electrical Diseases of the Heart, 1st ed. I. Gussak, C. Antzelevitch, A. Wilde, et al. Ed. London, Springer, 2008, pp. 365-378.
[6] G. Clifford and L. Tarassenko, "Quantifying errors in spectral estimates of HRV due to beat replacement and resampling," IEEE Trans Biomed Eng, vol.52, no.4, pp. 630-638, 2005.
[7] N. Lomb, "Least-squares frequency analysis of unequally spaced data," Astrophys Space Sci, vol.39, no.2, pp. 447-462, 1976.
[8] J. Scargle, "Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data," Astrophys J, vol.263, pp. 835-853, 1982.
[9] P. Laguna, G. Moody, and R. Mark, "Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals," IEEE Trans Biomed Eng, vol.45, no.6, pp. 698-715, 1998.
[10] D. Litvack, T. Oberlander, L. Camey, et al., "Time and frequency domain methods for heart rate variability analysis: A methodological comparison," Psychophysiology, vol.32, no.5, pp. 492-504, 1995.
[11] I. Mitov, "A method for assessment and processing of biomedical signals containing trend and periodic components," Med Eng Phys, vol.20, no.9, pp. 660-668, 1998.
[12] M. Tarvainen, P. Ranta-aho, and P. Karjalainen, "An advanced detrending method with application to HRV analysis," IEEE Trans Biomed Eng, vol.49, no.2, pp. 172-175, 2002.
[13] R. Thuraisingham, "Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals," Comput Meth Programs Biomed, vol.83, no.1, pp. 78-82, 2006.
[14] P. Flandrin, P. Goncalves, and G. Rilling, "Detrending and denoising with empirical mode decomposition", in Proc. Europ Signal Process Conf., vol.12, 2004, pp. 1581-1584.
[15] P. McSharry, G. Clifford, L. Tarassenko, et al., "A dynamical model for generating synthetic electrocardiogram signals", IEEE Trans Biomed Eng, vol.50, no.3, pp. 289-294, 2003.
[16] C. Li, C. Zheng, and C. Tai, "Detection of ECG characteristic points using wavelet transforms", IEEE Trans Biomed Eng, vol.42, no.1, pp. 21-28, 1995.
[17] J. Martínez, R. Almeida, S. Olmos, et al., "A wavelet-based ECG delineator: evaluation on standard databases", IEEE Trans Biomed Eng, vol.51, no.4, pp. 570-581, 2004.
[18] P. Addison, "Wavelet transforms and the ECG: a review", Physiol Meas, vol.26, pp. R155-R199, 2005.
[19] N. Huang, Z. Shen, S. Long, et al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proc. Math, Physic Eng Sci, vol.454, no.1971, pp. 903-995, 1998.
[20] Z. Wu, N. Huang, S. Long, et al., "On the trend, detrending, and variability of nonlinear and nonstationary time series". Proc. Natl Acad Sci, USA, vol.104, no.38, pp. 14889-14894, 2007.
[21] K. Shafqat, S. Pal, and P. Kyriacou. "Evaluation of two detrending techniques for application in heart rate variability", in Proc. Annu. Int. Conf. IEEE-EMBS. Lyon, vol.29, 2007, pp. 267-270.
[22] D. Knuth, "The Art of Computer Programming, Volume 1: Fundamental Algorithms", Addison-Wesley Professional, 1997.
[23] H. Resnikoff and R. Wells, "Wavelet analysis: the scalable structure of information", New York: Springer-Verlag, 1998.