Radial basis function neural networks versus fuzzy models to predict return of consciousness after general anesthesia

• Autor(a) principal: NUNES, C.S.
• Data de Publicação: 2005
• Outros Autores: MENDONCA, T.F., AMORIM, P., FERREIRA, D.A., ANTUNES, L.M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.16/480

Conf Proc IEEE Eng Med Biol Soc. 2004;2:865-8. Radial basis function neural networks versus fuzzy models to predict return of consciousness after general anesthesia. Nunes CS, Mendonca TF, Amorim P, Ferreira DA, Antunes LM. Dept. of Appl. Math., Porto Univ., Portugal. Abstract This work presents two modelling techniques to predict return of consciousness (ROC) after general anaesthesia, considering the effect concentration of the anaesthetic drug at awakening. First, several clinical variables were statistically analysed to determine their correlation with the awakening concentration. The anaesthetic 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. Radial basis function (RBF) neural networks were trained relating different sets of clinical values with the anaesthetic drug effect concentration at awakening. Secondly, fuzzy models were built using an adaptive network-based fuzzy inference system (ANFIS) also relating different sets of variables. Clinical data was used to train and test the models. The fuzzy models and RBF neural networks proved to have good prediction properties and balanced results. PMID: 17271814 [PubMed]