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Publicação
Neuro-fuzzy models to predict the required propofol amount for loss of consciousness during general anesthesia: a preliminary study
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Orientador(es)
Resumo(s)
This study presents several models to predict the total amount of the anesthetic drug propofol required to achieve loss of consciousness during the induction phase of anesthesia, considering different clinical variables. The data from 49 patients under anesthesia for neurosurgical procedures, were used in this study. Takagi-Sugeno-Kang (TSK) fuzzy models were used to describe the effect of clinical variables on the amount of propofol required for loss of consciousness. The parameters of the TSK
models were optimized using an Adaptive Network-Fuzzy
Interference System. All models were trained with the data of 35
patients and tested with the data of 14 patients. These models
proved to have reasonable prediction properties. The fuzzy model
with the best balanced performance used only two inputs: the
systolic arterial pressure and the Bispectral Index of the EEG.
Descrição
Palavras-chave
Personalized anesthesia Loss of consciousness Fuzzy logic ANFIS
Contexto Educativo
Citação
A. L. Ferreira, J. Mendes, P. Amorim and C. S. Nunes, "Neuro-fuzzy models to predict the required propofol amount for loss of consciousness during general anesthesia: a preliminary study," 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, 2018, pp. 1-5. (ISBN: 978-1-5386-3392-2)
Editora
IEEE