EEG classification for estimating anesthetic depth during halothane anesthesia

This study establishes the feasibility of using computer-based EEG recognition system to monitor anesthetic depth during halothane anesthesia. Experiments were carried out on ten dogs, at different levels of halothane, recording four channels of EEG data. The anesthetic state of the patient was tested using a tail clamping stimulus. A tenth order autoregressive (AR) model was used to represent the spectral information contained in the EEG signals. The AR model parameters were used as input to a three layer perceptron feedforward neural network. The network was able to correctly classify the depth in 85% of the cases as compared to 65% when only hemodynamic parameters were used as input to the network. This shows that the AR parameters obtained from the EEG signals can be used for decision making during administration of general anesthesia.