Improved Power Spectrum Estimation for RR-Interval Time Series
The RR interval series is non-stationary and unevenly
spaced in time. For estimating its power spectral density (PSD) using
traditional techniques like FFT, require resampling at uniform
intervals. The researchers have used different interpolation
techniques as resampling methods. All these resampling methods
introduce the low pass filtering effect in the power spectrum. The
lomb transform is a means of obtaining PSD estimates directly from
irregularly sampled RR interval series, thus avoiding resampling. In
this work, the superiority of Lomb transform method has been
established over FFT based approach, after applying linear and
cubicspline interpolation as resampling methods, in terms of
reproduction of exact frequency locations as well as the relative
magnitudes of each spectral component.
[1] S. Akselrod, D. Gordon, F. A. Ubel, S. C. Shanon, A. C. Barger and
R. J. Cohen, "Power spectrum analysis of heart rate fluctuation: A
quantitative probe of beat-to-beat cardiovascular control," Science, vol.
213, pp. 220 - 222, 1981.
[2] A. Boardman, F. S. Schlindwein, A. P. Rocha and A. Leite., "A study on
optimum order of autoregressive models for heart rate variability,"
Physiol. Meas., vol. 23, pp. 325-36, 2002.
[3] Mark Ebden, "A Comparison of HRV Techniques: The Lomb
Periodogram versus The Smoothed Pseudo Wigner-Ville Distribution,"
A report submitted to Prof. Lionel Tarassenko, November 19, 2002,
Avaliable: www.robots.ox.ac.uk/~mebden/reports/report3.doc,
[4] P. Laguna, G. B. Moody and R. G. Mark, "Power spectral density of
unevenly sampled data by least-square analysis: performance and
application to heart rate signals," IEEE Transactions on Biomedical
Engineering, pp. 698-715, 1998.
[5] N. R. Lomb, "Least-squares frequency analysis of unequally spaced
data," Astrophysical and space science, vol. 39, pp. 447-462, 1976.
[6] "Lomb Periodogram," from a website Avaliable:
http://www.cbi.dongnocchi.it/glossary/Lomb.html.
[7] M. Merri, D. C. Arden, J. G. Motley and E. L. Titlebaum, "Sampling
frequency of the electrocardiogram for spectral analysis of heart rate
variability," IEEE Transactions on Biomedical Engineering, vol. 37, pp.
99 - 106, 1990.
[8] W. H. Press, S. A. Teukolsky, W. T. Vertterling, B. P. Flannery,
Numerical Recipes in C++, Cambrige University Press, 2nd Edition,
1982.
[9] Scargle J.D., "Studies in astronomical time series analysis II: Statistical
aspects of spectral analysis of unequally spaced data," Astrophysical
Journal, vol 263, pp. 835-853, 1982.
[10] B. S. Saini, Dilbag Singh, "Comparison of re-sampling methods in the
spectral analysis of RR-interval series data (Submitted for publication)"
a paper submitted in the IETE Journal of Research, July 18, 2008.
[11] Dilbag Singh, B. S. Saini, "Heart rate variability-A bibliographical
survey," IETE Journal of research on Biomedical Signal and Image
Processing, vol. 54, no. 3, pp. 209-216, 2008.
[12] Dilbag Singh, Vinod Kumar, S. C. Saxena and K. K. Deepak, "Effects of
RR segment duration on HRV spectrum estimation," Physiol. Meas., vol.
25, pp. 721-735, 2004.
[13] K. S. Shin, H. Minamitani, S. Onishi, H. Yamazaki, M. H. Lee, "The
direct power spectrum estimation of unevenly sampled cardiac event
series," in Proceedings of the 16th Annual International Conference of
the IEEE EMBS, 1994, vol. 2, pp. 1254-1255.
[14] "Task Force of the European Society of, Heart rate variability -
standards of measurement, physiological interpretation and clinical use,"
European Heart Journal, vol. 17, pp. 354 - 381.
[15] T. Thong, J. McNames, M. Aboy and B. Oken, "Averaged Lomb
periodogram for nonuniform sampling," International Conference
Biosignal 04, 2004.
[1] S. Akselrod, D. Gordon, F. A. Ubel, S. C. Shanon, A. C. Barger and
R. J. Cohen, "Power spectrum analysis of heart rate fluctuation: A
quantitative probe of beat-to-beat cardiovascular control," Science, vol.
213, pp. 220 - 222, 1981.
[2] A. Boardman, F. S. Schlindwein, A. P. Rocha and A. Leite., "A study on
optimum order of autoregressive models for heart rate variability,"
Physiol. Meas., vol. 23, pp. 325-36, 2002.
[3] Mark Ebden, "A Comparison of HRV Techniques: The Lomb
Periodogram versus The Smoothed Pseudo Wigner-Ville Distribution,"
A report submitted to Prof. Lionel Tarassenko, November 19, 2002,
Avaliable: www.robots.ox.ac.uk/~mebden/reports/report3.doc,
[4] P. Laguna, G. B. Moody and R. G. Mark, "Power spectral density of
unevenly sampled data by least-square analysis: performance and
application to heart rate signals," IEEE Transactions on Biomedical
Engineering, pp. 698-715, 1998.
[5] N. R. Lomb, "Least-squares frequency analysis of unequally spaced
data," Astrophysical and space science, vol. 39, pp. 447-462, 1976.
[6] "Lomb Periodogram," from a website Avaliable:
http://www.cbi.dongnocchi.it/glossary/Lomb.html.
[7] M. Merri, D. C. Arden, J. G. Motley and E. L. Titlebaum, "Sampling
frequency of the electrocardiogram for spectral analysis of heart rate
variability," IEEE Transactions on Biomedical Engineering, vol. 37, pp.
99 - 106, 1990.
[8] W. H. Press, S. A. Teukolsky, W. T. Vertterling, B. P. Flannery,
Numerical Recipes in C++, Cambrige University Press, 2nd Edition,
1982.
[9] Scargle J.D., "Studies in astronomical time series analysis II: Statistical
aspects of spectral analysis of unequally spaced data," Astrophysical
Journal, vol 263, pp. 835-853, 1982.
[10] B. S. Saini, Dilbag Singh, "Comparison of re-sampling methods in the
spectral analysis of RR-interval series data (Submitted for publication)"
a paper submitted in the IETE Journal of Research, July 18, 2008.
[11] Dilbag Singh, B. S. Saini, "Heart rate variability-A bibliographical
survey," IETE Journal of research on Biomedical Signal and Image
Processing, vol. 54, no. 3, pp. 209-216, 2008.
[12] Dilbag Singh, Vinod Kumar, S. C. Saxena and K. K. Deepak, "Effects of
RR segment duration on HRV spectrum estimation," Physiol. Meas., vol.
25, pp. 721-735, 2004.
[13] K. S. Shin, H. Minamitani, S. Onishi, H. Yamazaki, M. H. Lee, "The
direct power spectrum estimation of unevenly sampled cardiac event
series," in Proceedings of the 16th Annual International Conference of
the IEEE EMBS, 1994, vol. 2, pp. 1254-1255.
[14] "Task Force of the European Society of, Heart rate variability -
standards of measurement, physiological interpretation and clinical use,"
European Heart Journal, vol. 17, pp. 354 - 381.
[15] T. Thong, J. McNames, M. Aboy and B. Oken, "Averaged Lomb
periodogram for nonuniform sampling," International Conference
Biosignal 04, 2004.
@article{"International Journal of Electrical, Electronic and Communication Sciences:56015", author = "B. S. Saini and Dilbag Singh and Moin Uddin and Vinod Kumar", title = "Improved Power Spectrum Estimation for RR-Interval Time Series", abstract = "The RR interval series is non-stationary and unevenly
spaced in time. For estimating its power spectral density (PSD) using
traditional techniques like FFT, require resampling at uniform
intervals. The researchers have used different interpolation
techniques as resampling methods. All these resampling methods
introduce the low pass filtering effect in the power spectrum. The
lomb transform is a means of obtaining PSD estimates directly from
irregularly sampled RR interval series, thus avoiding resampling. In
this work, the superiority of Lomb transform method has been
established over FFT based approach, after applying linear and
cubicspline interpolation as resampling methods, in terms of
reproduction of exact frequency locations as well as the relative
magnitudes of each spectral component.", keywords = "HRV, Lomb Transform, Resampling, RR-intervals.", volume = "2", number = "10", pages = "2255-5", }
