Generative Polyspectral Models for Depth of Anaesthesia Assessment
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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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