Monitoring depth of hypnosis under propofol general anaesthesia granger causality and Hidden Markov Models

Intra-operative awareness is experienced when a patient regains consciousness during surgery. This work presents a Brain-Computer Interface system that can be used as part of routine surgery for monitoring the patient state of hypnosis in order to prevent intra-operative awareness. The underlying state of hypnosis is estimated using causality-based features extracted from the spontaneous electrical brain activity (EEG) of the patient and a probabilistic classification framework (Hidden Markov Models). The proposed method is applied to EEG activity from 20 patients under propofol anaesthesia. The mean discrimination performance obtained was 98% and 85% for wakefulness and anaesthesia respectively, with an overall performance accuracy of 92%. The use of a probabilistic framework increases the anaesthetist's confidence on the estimated state of hypnosis based on the marginal probabilities of the underlying state.