[Intraoperative EEG monitoring using a neural network].

OBJECTIVE To introduce a new EEG parameter for monitoring a patient's cerebral status during anaesthesia. METHOD EEG epochs (channel C3 P3, duration 30 see per epoch) yield patterns used as the training input of a self-organising neural network (neural gas algorithm). Each pattern contains spectral components. An additional "suppression parameter" reflects the proportion of flat curves during an EEG epoch and reduces the shortcomings of spectral analysis. The enhanced pattern vector enables the recognition of burst-suppression periods representing depressed cerebral activity during anaesthesia. Following training with 25549 EEG epochs ¿recorded in 196 consenting patients in the period beginning 5 minutes before induction of anaesthesia to extubation¿ the network knows 125 basic patterns (neural clusters). The neural clusters are responsible for different stages of anaesthesia: some neurons are activated by EEG epochs of awake patients, others are stimulated by deep stages. The homogeneity of the EEG epochs of cluster is checked. The portion of the EEG epochs not recognised by the network (7.5%) contains heterogeneous and sporadic patterns (mainly outlines caused by artefacts). RESULTS In comparison with commonly used variables of anaesthesiological EEG monitoring (spectral edge or median frequency) neural discriminant analysis achieves better discrimination between awake and deeply anaesthetised stages (reclassification of 96% vs. 70%). On the basis of 56 complete sequences of patterns (from the beginning of the infusion of anaesthetics to the occurrence of burst suppression) a trend value of between 0.0 and 100.0 is assigned to each neural cluster. In contrast to existing methods, induction of anaesthesia causes a strictly linear increase in the electroencephalographic trend. CONCLUSIONS Detailed neural cluster and discriminant analysis on the basis of the model described leads to an improved EEG parameter which better reflects the hypnotic effects of anaesthetic agents and arousal reactions caused by pain stimuli. Misclassifications of awake and anaesthetised stages are reduced. The neural network learns to recognize the complex changes in EEG patterns during induction of anaesthesia with different agents.