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Comparison of neural networks, fuzzy and stochastic prediction models for return of consciousness after general anesthesia

2005Conference object Restricted access
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dc.contributor.authorNunes, Catarina S.
dc.contributor.authorMendonca, T. F.
dc.contributor.authorAmorim, Pedro
dc.contributor.authorFerreira, D. A.
dc.contributor.authorAntunes, L.
dc.date.accessioned2023-05-31T14:57:09Z
dc.date.available2023-05-31T14:57:09Z
dc.date.issued2005
dc.description.abstractThis paper presents three modeling techniques to predict return of consciousness (ROC) after general anesthesia, considering the effect concentration of the anesthetic drug at awakening. First, several clinical variables were statistically analysed to determine their correlation with the awakening concentration. The anesthetic and the analgesic mean dose during surgery, and the age of the patient, proved to have significantly high correlation coefficients. Variables like the mean bispectral index value during surgery, duration of surgery did not present a statistical relation with ROC. Stochastic regression models were built using the variables with higher correlation. Secondly, fuzzy models were built using an Adaptive Network-Based Fuzzy Inference System (ANFIS) also relating different sets of variables. Thirdly, radial basis function (RBF) neural networks were trained relating different sets of clinical values with the anesthetic drug effect concentration at awakening. Clinical data was used to train and test the models. The stochastic models and the fuzzy models proved to have good prediction properties. The RBF network models were more biased towards the training set. The best balanced performance was achieved with the fuzzy models.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationC. S. Nunes, T. F. Mendonca, P. Amorim, D. A. Ferreira and L. Antunes, "Comparison of Neural Networks, Fuzzy and Stochastic Prediction Models for return of consciousness after general anesthesia," Proceedings of the 44th IEEE Conference on Decision and Control, Seville, Spain, 2005, pp. 4827-4832pt_PT
dc.identifier.doi10.1109/CDC.2005.1582925pt_PT
dc.identifier.isbn0-7803-9567-0
dc.identifier.issn0191-2216
dc.identifier.urihttp://hdl.handle.net/10400.2/13940
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.titleComparison of neural networks, fuzzy and stochastic prediction models for return of consciousness after general anesthesiapt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceSeville, Spainpt_PT
oaire.citation.endPage4832pt_PT
oaire.citation.startPage4827pt_PT
oaire.citation.title44th IEEE Conference on Decision and Controlpt_PT
person.familyNameDa Costa Nunes Duarte
person.familyNameAmorim
person.givenNameCatarina Sofia
person.givenNamePedro
person.identifier.ciencia-id691F-CDC2-E26A
person.identifier.orcid0000-0002-8357-0994
person.identifier.orcid0000-0001-7466-4174
person.identifier.scopus-author-id55429654100
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationcc3069ec-f930-455f-9226-b77e5d2dc14b
relation.isAuthorOfPublication1ef321dd-4f09-493c-b801-e9a99b28947d
relation.isAuthorOfPublication.latestForDiscovery1ef321dd-4f09-493c-b801-e9a99b28947d

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