Assessment of anaesthetic depth by clustering analysis and autoregressive modelling of electroencephalograms.

ABSTRACT

The brain activity electroencephalogram (EEG) was recorded from 30 healthy women scheduled for hysterectomy. The patients were anaesthetized with isoflurane, halothane or etomidate/fentanyl. A multiparametric method was used for extraction of amplitude and frequency information from the EEG. The method applied autoregressive modelling of the signal, segmented in 2 s fixed intervals. The features from the EEG segments were used for learning and for classification. The learning process was unsupervised and hierarchical clustering analysis was used to construct a learning set of EEG amplitude-frequency patterns for each of the three anaesthetic drugs. These EEG patterns were assigned to a colour code corresponding to similar clinical states. A common learning set could be used for all patients anaesthetized with the same drug. The classification process could be performed on-line and the results were displayed in a class probability histogram. This histogram reflected in all patients the depth of anaesthesia, when the concentration of the anaesthetic agent was adjusted either based on clinical signs or according to the protocol. This uniform display, where colours in a class probability histogram indicate the depth of anaesthesia, may in the future serve as on-line advice for the administration of anaesthetics. A comparison of multiparametric with single parametric methods, based on calculation of median, spectral edge and peak frequencies, questions the reliability of the single parametric methods in monitoring anaesthetic depth.