Comparison of Detrending Methods in Spectral Analysis of Heart Rate Variability
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:60991", author = "Liping Li and Changchun Liu and Ke Li and Chengyu Liu", title = "Comparison of Detrending Methods in Spectral Analysis of Heart Rate Variability", abstract = "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", keywords = "empirical mode decomposition, heart rate variability,signal detrending, smoothness priors, wavelet", volume = "5", number = "9", pages = "447-5", }
