Use of parametric modelling and statistical pattern recognition in detection of awareness during general anaesthesia

Awareness is a rare but important complication of general anaesthesia. In its worst manifestation the patient is completely paralysed yet fully conscious and suffering the pain of the operative procedure. The sequelae from such an experience may be significant and lifelong. The paper describes a method, based on parametric modelling and statistical pattern recognition techniques, including neural networks, whereby awareness during general anaesthesia may be detected when present. Two systems are described, the first based solely on the use of the bispectrum, while the second makes use of both spectral and bispectral features. An evaluation on independent test sets shows that both systems have an average accuracy of >80%, but the variation across individuals is less using the spectral-bispectral system (standard deviation of 16.4% compared with 20.5%). The spectral-bispectral system operates in near real time, requiring only 5s of data to produce a new estimate of awareness. These estimates are obtained from the output of a trained neural network, which has as its input a set of features extracted from a single channel of electroencephalogram (EEG). The pre-processing of the data prior to input into the neural network is a critical component of the work, and it is here that parametric models have been extensively utilised. The spectral features are extracted from the EEG using a is segment and a lattice filter as the primary model, while the bispectral features are extracted using a 5s segment and a transversal filter as the underlying model