A set of EEG parameters to predict clinically anaesthetized state in humans for halothane anaesthesia

This article evaluates all the EEG parameters suggested in the literature that undergo changes due to anaesthetic dose, and suggests a set of EEG parameters that act as best signatures of anaesthetic state of a patient. This set of EEG parameters is validated by an artificial neural network.

The purpose of this study is to arrive at a set of EEG parameters that 'best' distinguish between awake and anaesthetized states of human patients for halothane anaesthesia.

A total of 21 EEG parameters were evaluated for 40 patients. Stepwise discriminant analysis (SDA) pruned them to a set of five parameters. They were fed to a 5-3-1 artificial neural network (ANN) for classification into awake and anaesthetized state. To confirm the results, variance analysis was applied to the set of 21 parameters. Five parameters were finalized after validation by the ANN.

The classification accuracy of the ANN with SDA parameters was found to be 96%. With variance analysis parameters, it returned an accuracy of 100%.

The set of five EEG parameters - approximate entropy, average frequency, Lempel Ziv (LZ) complexity, delta power and beta power forms the best set to distinguish between awake and anaesthetized state of human patients. Variance analysis is a better tool to converge at the optimal set than SDA.