Advertisement

Time-variant parametric estimation of transient quadratic phase couplings between heart rate components in healthy neonates

  • K. Schwab
  • M. Eiselt
  • P. Putsche
  • M. Helbig
  • H. Witte
Original Article

Abstract

The heart rate variability (HRV) can be taken as an indicator of the coordination of the cardio-respiratory rhythms. Bispectral analysis using a direct (fast Fourier transform based) and time-invariant approach has shown the occurrence of a quadratic phase coupling (QPC) between a low-frequency (LF: 0.1 Hz) and a high-frequency (HF: 0.4–0.6 Hz) component of the HRV during quiet sleep in healthy neonates. The low-frequency component corresponds to the Mayer–Traube–Hering waves in blood pressure and the high-frequency component to the respiratory sinus arrhythmia (RSA). Time-variant, parametric estimation of the bispectrum provides the possibility of quantifying QPC in the time course. Therefore, the aim of this work was a parametric, time-variant bispectral analysis of the neonatal HRV in the same neonates used in the direct, time-invariant approach. For the first time rhythms in the time course of QPC between the HF component and the LF component could be shown in the neonatal HRV.

Keywords

Heart Rate Variability Radial Basis Function Network Respiratory Sinus Arrhythmia High Model Order Healthy Neonate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This study was supported by the Deutsche Forschungsgemeinschaft (DFG, Wi 1166/2-3 and 2-4).

References

  1. 1.
    Arnold M, Witte H, Schelenz C (2002) Time-variant investigation of quadratic phase couplings caused by amplitude modulation in electroencephalic burst-suppression patterns. J Clin Monit Comput 17:115–123CrossRefGoogle Scholar
  2. 2.
    Chen S, Cowan CFN, Grant PM (1991) Orthogonal least-squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2:302–309CrossRefGoogle Scholar
  3. 3.
    Dykes FD, Ahmann PA, Baldzer K, Carrigan TA, Kitney R, Giddens DP (1986) Breath amplitude modulation of heart rate variability in normal full term neonates. Pediatr Res 20:301–308CrossRefGoogle Scholar
  4. 4.
    Leonard JA, Kramer MA, Ungar LH (1992) Using radial basis functions to approximate a function and its error-bounds. IEEE Trans Neural Netw 3:624–626CrossRefGoogle Scholar
  5. 5.
    Mhaskar HN (1996) Neural networks for optimal approximation of smooth and analytic functions. Neural Comput 8:164–177Google Scholar
  6. 6.
    Nikias L, Petropulu AP (1993) Higher-order spectra analysis: a nonlinear signal processing framework. Prentice-Hall, NJzbMATHGoogle Scholar
  7. 7.
    Park J, Sandberg IW (1993) Approximation and radial-basis-function networks. Neural Comput 5:305–316Google Scholar
  8. 8.
    Powell JD (1987) Radial basis functions for multivariable interpolation: a review. In: Cox MG (ed) Algorithms for approximation. Clarendon Press, OxfordGoogle Scholar
  9. 9.
    Saul JP (1991) Cardiorepiratory variability: fractals, white noise, nonlinear oscillators, and linear modeling. What’s to be learned? In: Haken H, Koeppchen HP (eds) Rhythms in physiological systems. Springer, Berlin Heidelberg New York, pp 115–126Google Scholar
  10. 10.
    Schlögl A (2000) The electroencephalogram and the adaptive autoregressive model: theory and applications. Shaker, AachenGoogle Scholar
  11. 11.
    Schwab K, Putsche P, Eiselt M, Helbig M, Witte H (2004) On the rhythmicity of quadratic phase coupling in the trace alternant EEG in healthy neonates. Neurosci Lett 369:179–182CrossRefGoogle Scholar
  12. 12.
    Schwab K, Eiselt M, Schelenz C, Witte H (2005) Time-variant parametric estimation of transient quadratic phase couplings during electroencephalographic burst activity. Methods Inf Med 44:374–383Google Scholar
  13. 13.
    Swami A (1988) System identification using cumulants. USC–SIPI report, vol 140Google Scholar
  14. 14.
    Swami A, Mendel JM (1988) Adaptive cumulant-based estimation of ARMA parameters. American control conference, ACC-88. Atlanta, GAGoogle Scholar
  15. 15.
    Witte H, Schack B, Helbig M, Putsche P, Schelenz C, Schmidt K, Specht M (2000) Quantification of transient quadratic phase couplings within EEG burst patterns in sedated patients during electroencephalic burst-suppression period. J Physiol Paris 94:427–434CrossRefGoogle Scholar
  16. 16.
    Witte H, Putsche P, Eiselt M, Arnold M, Schmidt K, Schack B (2001) Technique for the quantification of transient quadratic phase couplings between heart rate components. Biomed Tech 46:42–49CrossRefGoogle Scholar
  17. 17.
    Witte H, Putsche P, Schwab K, Eiselt M, Helbig M, Suesse T (2004) On the spatio-temporal organisation of quadratic phase-couplings in ‘trace alternant’ EEG pattern in full-term newborns. Clin Neurophysiol 115:2308–2315CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2006

Authors and Affiliations

  • K. Schwab
    • 1
  • M. Eiselt
    • 1
  • P. Putsche
    • 1
  • M. Helbig
    • 1
  • H. Witte
    • 1
  1. 1.Institute of Medical Statistics, Computer Sciences and DocumentationMedical Faculty of the Friedrich Schiller University JenaJenaGermany

Personalised recommendations