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Estimation of Delay Times in Biological Systems

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Abstract

The problem of delay time estimation in biological systems is addressed with the focus on practical applicability of methods. Four delay time estimators are described: a cross correlation method and three increasingly sophisticated interpretations of the phase spectrum, ranging from a pointwise interpretation of the phase spectrum in terms of a delay to a Hilbert transform method. The four methods are compared through simulation studies showing that, in general, the Hilbert transform method performs best. The methods are then used to estimate delay times in three physiological systems: vestibular stimulation, cerebral autoregulation, and human orthostatic tremor. In all three cases, the Hilbert transform method yields the best results, leading in some cases to physiologically more sensible interpretations of experiments than the other methods. © 2003 Biomedical Engineering Society.

PAC2003: 8710+e, 8780Tq

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References

  1. Aaslid, R., K. F. Lindegaard, W. Sorteberg, and H. Nornes. Cerebral autoregulation dynamics in humans. Stroke 20:45–52, 1989.

    Google Scholar 

  2. Avitzour, D. Time delay estimation at high signal-to-noise ratio. IEEE Trans. Aerosp. Electron. Syst. 27:234–237, 1991.

    Google Scholar 

  3. Azenkot, Y., and I. Gertner. The least squares estimation of time delay between two signals with unknown relative phase shift. IEEE Trans. Acoust., Speech, Signal Process. 33:308–309, 1985.

    Google Scholar 

  4. Bloomfield, P. Fourier Analysis of Time Series: An Introduction. New York: Wiley, 1976, p. 225.

    Google Scholar 

  5. Brockwell, P. J., and R. A. Davis. Time Series: Theory and Methods. New York: Springer, 1991, p. 434.

    Google Scholar 

  6. Cabot, R. C. A note on the application of the Hilbert transform to time delay estimation. IEEE Trans. Acoust., Speech, Signal Process. 29:607–609, 1981.

    Google Scholar 

  7. Chan, Y. T., R. V. Hattin, and J. B. Plant. The least squares estimation of time delay and its use in signal detection. IEEE Trans. Acoust., Speech, Signal Process. 26:217–222, 1978.

    Google Scholar 

  8. Chan, Y. T., and R. K. Miskowicz. Estimation of coherence and time delay with ARMA models. IEEE Trans. Acoust., Speech, Signal Process. 32:295–303, 1984.

    Google Scholar 

  9. Chiu, C.-C., and S.-Y. Yeh. Assessment of cerebral autoregulation using time-domain cross-correlation analysis. Comput. Biol. Med. 31:471–480, 2001.

    Google Scholar 

  10. Cleveland, W. S., and E. Parzen. The estimation of coherence, frequency response, and envelope delay. Technometrics 17:167–172, 1975.

    Google Scholar 

  11. Clifford, C. G. Coherence and time delay estimation. Proc. IEEE 75:236–255, 1987.

    Google Scholar 

  12. Deaton, M. L., and R. V. Foutz. Group delay and the time–lag relationship between stochastic processes. J. Time Ser. Anal. 1:111–118, 1980.

    Google Scholar 

  13. Diehl, R. R., D. Linden, D. Lücke, and P. Berlit. Phase relationship between cerebral blood flow velocity and blood pressure. A clinical test of autoregulation. Stroke 26:1801–1804, 1995.

    Google Scholar 

  14. Hamon, B. V., and E. J. Hannan. Spectral estimation of time delay for dispersive and nondispersive systems. Appl. Stat. 23:134–142, 1974.

    Google Scholar 

  15. Hannan, E. J., and P. J. Thomson. The estimation of coherence and group delay. Biometrika 58:469–481, 1971.

    Google Scholar 

  16. Hannan, E. J., and P. J. Thomson. Estimating group delay. Biometrika 60:241–253, 1973.

    Google Scholar 

  17. Hannan, E. J., and P. J. Thomson. Delay estimation and the estimation of coherence and phase. IEEE Trans. Acoust., Speech, Signal Process. 29:485–490, 1981.

    Google Scholar 

  18. Hannan, E. J., and P. J. Thomson. Time delay estimation. J. Time Ser. Anal. 9:21–33, 1988.

    Google Scholar 

  19. Hertz, D., and M. Azaria. Time delay estimation between two phase shifted signals via generalized cross-correlation methods. Signal Process. 8:237–255, 1985.

    Google Scholar 

  20. Hinich, M. J., and G. R. Wilson. Time delay estimation using the cross bispectrum. IEEE Trans. Signal Process. 40:106–113, 1992.

    Google Scholar 

  21. Holm, S., and G. Ottesen. Bias in the cross spectrum and time delay estimates due to misalignment. IEEE Trans. Acoust., Speech, Signal Process. 34:1662–1665, 1986.

    Google Scholar 

  22. Honerkamp, J. Stochastic Dynamical Systems. New York: VCH, 1994.

    Google Scholar 

  23. Journee, H. L. Demodulation of amplitude modulated noise: a mathematical evaluation of a demodulator for pathological tremor EMG's. IEEE Trans. Biomed. Eng. 30:304–308, 1983.

    Google Scholar 

  24. Kloeden, P. E., E. Platen, and S. H. The numerical solution of nonlinear stochastic dynamical systems: A brief introduction. 1:277–286, 1991.

    Google Scholar 

  25. Knapp, C. H., and G. C. Carter. The generalized correlation method for estimation of time delay. IEEE Trans. Acoust., Speech, Signal Process. 24:320–327, 1976.

    Google Scholar 

  26. Köster, B., M. Lauk, J. Timmer, M. Poersch, B. Guschlbauer, G. Deuschl, and C. H. Lücking. Involvement of cranial muscles and high intermuscular coherence in orthostatic tremor. Ann. Neurol. 45:384–388, 1999.

    Google Scholar 

  27. Köster, B., M. Lauk, J. Timmer, M. Poersch, B. Guschlbauer, G. Deuschl, and C. H. Lücking. Involvement of cranial muscles and high intermuscular coherence in orthostatic tremor. Ann. Neurol. 45:384–388, 1999.

    Google Scholar 

  28. Kuo, T. B., C. M. Chern, W. Y. Sheng, W. J. Wong, and H. H. Hu. Frequency domain analysis of cerebral blood flow velocity and its correlation with arterial blood pressure. J. Cereb. Blood Flow Metab. 18:311–318, 1998.

    Google Scholar 

  29. Lauk, M., B. Köster, J. Timmer, B. Guschlbauer, G. Deuschl, and C. H. Lücking. Side-to-side correlation of muscle activity in physiological and pathological human tremor. 110:1774–1783, 1999.

    Google Scholar 

  30. Lindemann, M., J. Raethjen, J. Timmer, G. Deuschl, and G. Pfister. Delay estimation for cortico-peripheral relations. J. Neurosci. Methods 111:127–139, 2001.

    Google Scholar 

  31. Nakano, J., and S. Tagami. Delay estimation by a Hilbert transform method. 30:217–227, 1988.

    Google Scholar 

  32. Nikias, C. L., and R. Pan. Time delay estimation in unknown Gaussian spatially correlated noise. IEEE Trans. Acoust., Speech, Signal Process. 36:1706–1714, 1988.

    Google Scholar 

  33. Oppenheim, A. V., and R. W. Schafer. Digital Signal Processing. London: Prentice-Hall, 1975.

    Google Scholar 

  34. Panerai, R. B., R. P. White, H. S. Markus, and D. H. Evans. Grading of cerebral dynamic autoregulation from spontaneous fluctuations in arterial blood pressure. Stroke 29:2341–2346, 1998.

    Google Scholar 

  35. Pavlik, A. E., J. T. Inglis, M. Lauk, L. Oddsson, and J. J. Collins. The effects of stochastic galvanic vestibular stimulation on human postural sway. Exp. Brain Res. 124:273–280, 1999.

    Google Scholar 

  36. Piersol, A. G. Time delay estimation using phase data. IEEE Trans. Acoust., Speech, Signal Process. 29:471–477, 1981.

    Google Scholar 

  37. Press, W., B. Flannery, S. Saul, and W. Vetterling. Numerical Recipes, 2nd ed. London: Cambridge University Press, 1992.

    Google Scholar 

  38. Priestley, M. Spectral Analysis and Time Series. New York: Academic, 1989.

    Google Scholar 

  39. Schmidt, R. F., and G. Thews. Human Physiology, 2nd ed. Berlin: Springer, 1989.

    Google Scholar 

  40. Tiecks, F. P., A. M. Lam, R. Aaslid, and D. W. Newell. Comparison of static and dynamic cerebral autoregulation measurements. Stroke 26:1014–1019, 1995.

    Google Scholar 

  41. Timmer, J. Parameter estimation in nonlinear stochastic differential equations. Chaos, Solitons Fractals 11:2571–2578, 2000.

    Google Scholar 

  42. Timmer, J., M. Lauk, and G. Deuschl. Quantitative analysis of tremor time series. Electroencephalogr. Clin. Neurophysiol. 101:461–468, 1996.

    Google Scholar 

  43. Timmer, J., M. Lauk, W. Pfleger, and G. Deuschl. Cross-spectral analysis of physiological tremor and muscle activity. I. Theory and application to unsychronized EMG. Biol. Cybern. 78:349–357, 1998.

    Google Scholar 

  44. Tong, H. Threshold Models in Nonlinear Time Series Analysis, Vol. 21 of Lecture Notes in Statistics. New York: Springer, 1983.

    Google Scholar 

  45. Tribolet, J. M. A new phase unwrapping algorithm. IEEE Trans. Acoust., Speech, Signal Process. 25:170–177, 1977.

    Google Scholar 

  46. van der Pol, B. On oscillation-hysteresis in a simple triode generator. Philos. Mag. 43:177, 1922.

    Google Scholar 

  47. Youn, D. H., and N. Ahmed. Time delay estimation via coherence: An adaptive approach. J. Acoust. Soc. Am. 75:505–514, 1984.

    Google Scholar 

  48. Youn, D. H., N. Ahmed, and G. C. Carter. An adaptive approach for time delay estimation of band-limited signals. IEEE Trans. Acoust., Speech, Signal Process. 31:780–784, 1983.

    Google Scholar 

  49. Zhang, R., J. H. Zuckerman, C. A. Giller, and B. D. Levine. Transfer function analysis of dynamic cerebral autoregulation in humans. Am. J. Physiol. 274:H233–H241, 1998.

    Google Scholar 

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Müller, T., Lauk, M., Reinhard, M. et al. Estimation of Delay Times in Biological Systems. Annals of Biomedical Engineering 31, 1423–1439 (2003). https://doi.org/10.1114/1.1617984

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