Summary
Objectives:
Electroencephalographic burst activity characteristic of burst-suppression pattern
(BSP) in sedated patients and of burst-interburst pattern (BIP) in the quiet sleep
of healthy neonates have similar linear and non-linear signal properties. Strong interrelations
between a slow frequency component and rhythmic, spindle-like activities with higher
frequencies have been identified in previous studies. Time-varying characteristics
of BSP and BIP prevent a definite pattern-related analysis. A continuous estimation
of the bispectrum is essential to analyze these patterns. Parametric bispectral approaches
provide this opportunity.
Methods:
The adaptation of an AR model leads to a parametric bispectrum by using the transfer
function of the estimated AR filter. Time-variant parametric bispectral approaches
require an estimation of AR parameters which consider higher order moments to preserve
phase information. Accordingly, a time-variant parametric estimation of the bispectrum
was introduced. Data driven simulations were performed to provide optimal parameters.
BSP (12 patients) and BIP (6 neonates) were analyzed using this novel approach.
Results:
Significant differences in the time course of burst pattern during BSP and burst-like
pattern before the onset of BSP could be shown. A rhythmic quadratic phase coupling
(period 10 sec) was identified during BIP in all neonates.
Conclusion:
Quadratic phase couplings during BSP increases in the time course depending on depth
of sedation. The visually detected burst activity in BIP is only the temporarily observable
EEG correlate of a hidden neural process. Time-variant bispectral approaches offer
the possibility of a better characterization of underlying neural processes leading
to improved diagnostic tools used in clinical routine.
Keywords
Quadratic phase coupling - time-variant parametric bispectrum - electroencephalographic
burst activity - intensive care patients - neonates
