@inproceedings{02c6a8e097174bf88eafc115fbabefb6,
title = "Identification of a set of optimal EEG parameters for estimation of depth of anaesthesia",
abstract = "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 <95 %) the ANNs perform better than the sole use of spectral entropy - BIS performs considerably worse.,",
keywords = "depth of anaesthesia, EEG analysis, factor analysis, artificial neural networks, OAA/S",
author = "{van Gils}, Mark and H. Vierti{\"o}-Oja and Arvi Yli-Hankala and Ilkka Korhonen",
year = "2002",
language = "English",
isbn = "3-901351-62-0",
series = "IFMBE Proceedings",
publisher = "Springer",
pages = "390--391",
booktitle = "2nd European Medical and Biomedical Engineering Conference, EMBEC02",
note = "2nd European Medical and Biomedical Engineering Conference ; Conference date: 04-12-2002 Through 08-12-2002",
}