Depth of Anaesthesia Assessment with Generative Polyspectral Models
Iead Rezek, Stephen J. Roberts, Ellie Siva and Regina Conradt
In: International Conference on Machine Learning and Applications 2005, 15-17 Dec 2005, Los Angeles, CA.

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Abstract

The application of anaesthetic agents is known to have significant effects on the EEG waveforms. Information extraction now routinely goes beyond second order statistics 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) [Currently Displayed]
  • Generative Polyspectral Models for Depth of Anaesthesia Assessment (deposited 25 February 2008)

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