Nonlinear Interpretation of Respiratory Sinus Arrhythmia in Anesthesia

Abstract:

A non-parametric method is presented for modelling nonlinear dynamic mechanisms of respiratory sinus arrhythmia (RSA) in anesthesia caused by positive pressure ventilation. RR interval sequences are shown with Tsay’s linearity test to contain both short-term and long-term nonlinear components, which cannot completely be modelled with optimal linear methods. The nonlinear approach is based on Wiener’s theory for broad-band random input signal. The input-output model is formed for tracheal pressure and RR interval sequence. Second-order and third-order nonlinearities in RSA fluctuation are found and demonstrated.

Keywords

Respiratory Sinus Arrhythmia - Anesthesia - RR interval Variability - Nonlinear Time Series - Non-parametric Modelling

• REFERENCES

• 1 Yli-Hankala A, Jäntti V. EEG burst-suppression pattern correlates with the instantaneous heart rate under isoflurane anaesthesia. Acta Anaesthesiol Scand 1990; 34: 665-8.
• 2 Jantti V, Yli-Hankala A. Correlation of instantaneous heart rate and EEG suppression during enflurane anaesthesia: Synchronous inhibition of heart rate and cortical electrical activity. Electroenceph Clin Neurophysiol 1990; 76: 476-9.
• 3 Yli-Hankala A, Porkkala T, Kaukinen S, Hakkinen V, Jäntti V. Respiratory Sinus Arrhythmia is reversed during positive pressure ventilation. Acta Physiol Scand 1991; 141: 399-407.
• 4 Galletly DC, Corfiatis T, Westenberg M, Robinson BJ. Heart rate periodicities during induction of propofol-nitrous oxide-isoflurane anaesthesia. Br J Anaesth 1992; 68: 360-4.
• 5 Kato M, Komatsu T, Kimura T, Sugiyama F, Nakashima K, Shimada Y. Spectral analysis of heart rate variability during isoflurane anesthesia. Anesthesiol 1992; 77: 669-74.
• 6 Kalli S, Turjanmaa V, Suoranta R, Uusitalo A. Assessment of blood pressure and heart rate variability using multivariate autoregressive modelling. Biomed Meas Inf Control. 1987-88; 01. 46-56.
• 7 Baselli G, Bolis D, Cerutti S, Freschi C. Autoregressive modelling and power spectral estimate of R-R interval times series in arrhythmia patients. Comput Biomed Res 1985; 18: 531-43.
• 8 Tsay R. Nonlinearity tests for time series. Biometrika 1986; 73: 461-6.
• 9 Loula P, Jäntti V, Yli-Hankala A. Detail-preserving filtering of respiratory sinus arrhythmia in anaesthesia. In: Proceedings IEEE Winter Workshop on Nonlinear Digital Signal Processing. Tampere, Finland, Jan 1993: 4.2.1.1-6.
• 10 Heinonen P, Neuvo Y. FIR-Median Hybrid filters with predictive FIR substructure. IEEE Trans Acoust Speech Signal Proc 1988; 36: 892-9.
• 11 Narenda K, Gallman P. An iterative method for identification of nonlinear systems using a Hammerstein model. IEEE Trans Autom Control 1966; 11: 546.
• 12 Marmarelis P, Marmarelis V. Analysis of Physiological Systems – The White Noise Approach. New York: Plenum; 1978
• 13 Schetzen M. Nonlinear system modelling based on the Wiener theory. Proc IEEE 1981; 69: 1557-73.
• 14 Ljung L. System Identification: Theory for the User. New Jersey: Prentice-Hall; 1987
• 15 Wiener N. Nonlinear Problems in Random Theory. New York: Wiley; 1958