Korhonen, Ilkka

van Gils, Mark

Viertiö-Oja, H.

Yli-Hankala, Arvi

From a set of neurophysiologic variables an optimal combination was sought for determining the depth of anaesthesia. Performances of different comibations of variables were assessed on their ability to perdict the OAA/S score levels annotated by an anaesthesiologist. From the EEG of 32 patient nine features plus BIS-values were used. Factor analysis lead to 4 factors representing; spectral entropies, SynchFastSlow and betaratio; burst-suppression related variables; spectral edge frequency; and EMG information. The prediction probability of artificial neural network (ANN)-based classifiers trained with as inputs a) extracted factor variables and b) a combination of original variables was compared to the prediction probabilities of spectral entropy and BIS. The results indicated that when the performance was close to 100% this was so for all classifiers. For more difficult cases (performance &lt;95 %) the ANNs perform better than the sole use of spectral entropy - BIS performs considerably worse.

Identification of a set of optimal EEG parameters for estimation of depth of anaesthesia

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