Time-variant Parametric Estimation of Transient Quadratic Phase Couplings during Electroencephalographic Burst Activity

K. Schwab; M. Eiselt; C. Schelenz; H. Witte

doi:10.1055/s-0038-1633980

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2005; 44(03): 374-383
DOI: 10.1055/s-0038-1633980

Original Article

Schattauer GmbH

Time-variant Parametric Estimation of Transient Quadratic Phase Couplings during Electroencephalographic Burst Activity

K. Schwab

¹Institute of Medical Statistics, Computer Sciences and Documentation, Medical Faculty of the Friedrich Schiller University Jena, Jena, Germany

,

M. Eiselt

²Institute of Pathophysiology, Medical Faculty of the Friedrich Schiller University Jena, Jena, Germany

,

C. Schelenz

³Department of Anesthesiology and Intensive Care, Medical Faculty of the Friedrich Schiller University Jena, Jena, Germany

,

H. Witte

¹Institute of Medical Statistics, Computer Sciences and Documentation, Medical Faculty of the Friedrich Schiller University Jena, Jena, Germany

› Institutsangaben

Abstract

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

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Referenzen

References
1 Steriade M, Amzica F, Contreras D. Cortical and thalamic cellular correlates of electroencephalographic burst-suppression. Electroencephalogr Clin Neurophysiol 1994; 90 (01) 1-16.
2 Rother M, Teubner B, Winkler P, Eiselt M, Clausner B, Teubner B. Predictive value of pattern selective spectral analysis of neonatal EEG. Brain Dysfunction 1991; 4: 1-9.
3 Witte H, Putsche P, Eiselt M, Hoffmann K, Schack B, Arnold M. et al. Analysis of the interrelations between a low-frequency and a high-frequency signal component in human neonatal EEG during quiet sleep. Neurosci Lett 1997; 236 (03) 175-9.
4 Witte H, Schelenz C, Specht M, Jager H, Putsche P, Arnold M. et al. Interrelations between EEG frequency components in sedated intensive care patients during burst-suppression period. Neurosci Lett 1999; 260 (01) 53-6.
5 Niedermeyer E, Sherman DL, Geocadin RJ, Hansen HC, Hanley DF. The burst-suppression electroencephalogram. Clinical Electroencephalography 1999; 30 (03) 99-105.
6 Witte H, Schack B, Helbig M, Putsche P, Schelenz C, Schmidt K. et al. Quantification of transient quadratic phase couplings within EEG burst patterns in sedated patients during electroencephalic burst-suppression period. J Physiol Paris 2000; 94 5-6 427-34.
7 Schack B, Witte H, Helbig M, Schelenz C, Specht M. Time-variant non-linear phase-coupling analysis of EEG burst patterns in sedated patients during electroencephalic burst suppression period. Clin Neurophysiol 2001; 112 (08) 1388-99.
8 Arnold M, Witte H, Schelenz C. Time-variant investigation of quadratic phase couplings caused by amplitude modulation in electroencephalographic burst-suppression patterns. J Clin Monit 2002; 17: 115-23.
9 Eiselt M, Schindler J, Arnold M, Witte H, Zwiener U, Frenzel J. Functional interactions within the newborn brain investigated by adaptive coherence analysis of EEG. Neurophysiol Clin 2001; 31 (02) 104-13.
10 Arnold M, Doering A, Witte H, Dorschel J, Eisel M. Use of adaptive Hilbert transformation for EEG segmentation and calculation of instantaneous respiration rate in neonates. J Clin Monit 1996; 12 (01) 43-60.
11 Doering A, Jager H, Witte H, Galicki M, Schelenz C, Specht M. et al. Adaptable preprocessing units and neural classification for the segmentation of EEG signals. Methods Inf Med 1999; 38 (03) 214-24.
12 Leistritz L, Jager H, Schelenz C, Witte H, Putsche P, Specht M. et al. New approaches for the detection and analysis of electroncephalographi burst-suppression patterns in patients under sedation. J Clin Monit Comput 1999; 15 (06) 357-67.
13 Kleiner B, Huber PJ, Dummermuth G. Analysis of the interrelations between frequency bands of the EEG by means of the bispectrum. Electroencephalogr Clin Neurophysiol 1969; 27: 693
14 Bullock TH, Achimowicz JZ, Duckrow RB, Spencer SS, Iragui-Madoz VJ. Bicoherence of intracranial EEG in sleep, wakefulness and seizures. Electroencephalogr Clin Neurophysiol 1997; 103 (06) 661-78.
15 Gajraj RJ, Doi M, Mantzaridis H, Kenny GN. Analysis of the EEG bispectrum, auditory evoked potentials and the EEG power spectrum during repeated transitions from consciousness to unconsciousness. Br J Anaesth 1998; 80 (01) 46-52.
16 Muthuswamy J, Sherman DL, Thakor NV. Higherorder spectral analysis of burst patterns in EEG. IEEE Trans Biomed Eng 1999; 46 (01) 92-9.
17 Shen M, Chan FHY, Sun L, Beadle PJ. Parametric bispectral estimation of EEG signals in different functional states of the brain. IEE Proceedings – Science Measurement and Technology 2000; 147 (06) 374-7.
18 Swami A, Mendel JM. Adaptive cumulant-based estimation of ARMA parameters. In Amer Control Conf, ACC-88; 1988. Atlanta, GA: 1988
19 Swami A. System identification using cumulants. USC-SIPI report. 1988; 140
20 Swami A. Fast transversal versions of the RIV algorithm. International Journal Adaptive Control and Signal Processing 1996; 10 2-3 267-81.
21 Giannakis GB, Mendel JM. Cumulant-based order determination of non-gaussian ARMA models. IEEE Trans ASSP 1990; 38: 1411-21.
22 Nikias L, Petropulu AP. Higher-order spectra analysis – a nonlinear signal processing framework. New Jersey: Prentice Hall; 1993
23 Sarkela M, Mustola S, Seppanen T, Koskinen M, Lepola P, Suominen K. et al. Automatic analysis and monitoring of burst suppression in anesthesia. J Clin Monit Comput 2002; 17 (02) 125-34.
24 Kim YC, Powers EJ. Digital bispectral analysis and its application to nonlinear wave interactions. IEEE Trans Plasma Science 1979; 1: 120-31.
25 Shils JL, Litt M, Skolnick BE, Stecker MM. Bispectral analysis of visual interactions in humans. Electroencephalogr Clin Neurophysiol 1996; 98 (02) 113-25.
26 Schlögl A. The electroencephalogram and the adaptive autoregressive model: theory and applications. Aachen: Shaker; 2000
27 Powell JD. Radial basis functions for multivariable interpolation: a review. In. Cox MG. editor Algorithms for Approximation. Oxford: Clarendon Press; 1987
28 Witte H, Schack B. Quantification of phase coupling and information transfer between electroencephalografic (EEG) signals. Theory Biosci 2003; 122: 361-81.
29 McCormick DA, Bal T. Sleep and arousal: thalamocortical mechanisms. Annual Review of Neuroscience 1997; 20: 185-215.
30 Amzica F, Steriade M. Disconnection of intracortical synaptic linkages disrupts synchronisation of a slow oscillation. Journal of Neuroscience 1995; 15: 4658-77.
31 Contreras D, Steriade M. Spindle oscillation in cats: the role of corticothalamic feedback in a thalamically generated rhythm. J Physiol 1996; 490 ( Pt 1) 159-79.
32 Witte H, Putsche P, Eiselt M, Arnold M, Schmidt K, Schack B. Technique for the quantification of transient quadratic phase couplings between heart rate components. Biomed Tech (Berl) 2001; 46 (03) 42-9.
33 Laguzzi R, Reis DJ, Talman WT. Modulation of cardiovascular and electrocortical activity through seretonergic mechanism in the nucleus tractus solitarius of the rat. Brain Res 1984; 304: 321-8.
34 Lambertz M, Langhorst P. Simultaneous changes of rhythmic organization in brainstem neurons, respiration, cardiovascular system and EEG between 0.05 and 0.5 Hz. J Auton Nerv Syst 1998; 68: 58-77.
35 Onai T, Takayama K, Miura M. Projections to areas of the nucleus solitarii related to circulatory and respiratory responses in cats. J Auton Nerv Syst 1987; 18: 163-75.