Generative Polyspectral Models for Depth of Anaesthesia Assessment
Iead Rezek, Stephen J. Roberts and Regina Conradt
Engineering in Medicine and Biology Magazine Volume 26, Number 2, pp. 64-73, 2005.

This is the latest version of this eprint.

Abstract

The application of anaesthetic agents is known to have significant effects on the EEG waveform. Information extraction now routinely goes beyond second order spectral analysis, as obtained via power spectral methods, and uses higher order spectral methods. In this paper we present a model which generalises the autoregressive class of polyspectral models by having a semi-parametric description of the residual probability density. We estimate the model in the Variational Bayesian framework and extract higher order spectral features. Testing their importance for depth of anaesthesia classification is done on three different EEG data sets collected under exposure to different agents. The results show that significant improvements can be made over standard methods of estimating higher order spectra. The results also indicate that in two out of three anaesthetic agents, better classification can be achieved with higher order spectral features.

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Available Versions of this Item

• Depth of Anaesthesia Assessment with Generative Polyspectral Models (deposited 13 October 2005)
  • Generative Polyspectral Models for Depth of Anaesthesia Assessment (deposited 25 February 2008) [Currently Displayed]

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