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

Mark van Gils, H. Viertiö-Oja, Arvi Yli-Hankala, Ilkka Korhonen

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


    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.,
    Original languageEnglish
    Title of host publication2nd European Medical and Biomedical Engineering Conference, EMBEC02
    Place of PublicationGraz, Austria
    Publication statusPublished - 2002
    MoE publication typeA4 Article in a conference publication
    Event2nd European Medical and Biomedical Engineering Conference - Vienna, Austria
    Duration: 4 Dec 20028 Dec 2002

    Publication series

    SeriesIFMBE Proceedings


    Conference2nd European Medical and Biomedical Engineering Conference


    • depth of anaesthesia
    • EEG analysis
    • factor analysis
    • artificial neural networks
    • OAA/S


    Dive into the research topics of 'Identification of a set of optimal EEG parameters for estimation of depth of anaesthesia'. Together they form a unique fingerprint.

    Cite this