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Eeg-Based Assessment Of Anaesthetic Depth Using Neural Networks

this paper. One of the most important problems in EEG analysis is the extraction of appropriate features to describe the ongoing signal, and this can be tackled in various ways. The feature extraction stage of the work described in this paper was performed using methods of dynamic systems analysis and involved the extraction of stochastic and dynamic complexity features of the signal (spectral entropy and features from the embedding eigen-spectrum). Feature-spaces formed using these two methods were used as input to a Radial Basis Function (RBF) pattern classifier. We show that, despite the agent specificity of EEG changes in anaesthesia, a useful anaesthetic depth monitor may be created. We present results for two different anaesthetic agents, desflurane and propofol. Materials and Method

The Use Of Neural Networks In Eeg Analysis

this paper the problems of noise and artefact removal, nor the problems of modelling the passage of the EEG from cortex to scalp, but instead will concentrate on the EEG as i