Title :
Depth of anaesthesia assessment with generative polyspectral models
Author :
Rezek, I. ; Roberts, S.J. ; Siva, E. ; Conradt, R.
Author_Institution :
Dept. of Eng. Sci., Oxford Univ., UK
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.
Keywords :
autoregressive processes; belief networks; electroencephalography; generalisation (artificial intelligence); medical signal processing; medicine; probability; spectral analysis; EEG data sets; EEG waveform; anaesthesia assessment; anaesthesia classification; anaesthetic agents; generative polyspectral models; higher order spectral methods; information extraction; power spectral methods; residual probability density; second order spectral analysis; variational Bayesian framework; Bayesian methods; Brain modeling; Data mining; Electroencephalography; Entropy; Noise robustness; Public healthcare; Spectral analysis; Statistical analysis; Testing;
Conference_Titel :
Machine Learning and Applications, 2005. Proceedings. Fourth International Conference on
Print_ISBN :
0-7695-2495-8
DOI :
10.1109/ICMLA.2005.21