Increasing the depth of anesthesia assessment

The application of anesthetic agents is known to have significant effects on the electroencephalogram (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 article, we present a model that generalizes the autoregressive class of polyspectral models by having a semiparametric 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 anesthesia 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 anesthetic agents, better classification can be achieved with higher-order spectral features