EEG-based assessment of anaesthetic depth using neural networks

To determine the appropriate dose of anaesthetic drug to be used during surgery is a far from trivial problem. Signs of autonomic activity are not very accurate; hence, an alternative technique is required to monitor the depth of anaesthesia. In common with many biological systems, brain activity is a dynamical system having irregular and unpredictable characteristics. The mathematical analysis of the behaviour of dynamical systems has expanded rapidly with the advent of fast computers. This has given rise to a wide range of medical applications, including mathematical analysis of the human EEG. It is in this last genre that the automatic assessment of the depth of anaesthesia, using ongoing EEG as a measure of brain activity, is discussed. 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. Feature-spaces formed using these two methods were used as input to a radial basis function 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