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- Usefulness of the blink reflex to assess the effect of propofol during induction of anesthesia in surgical patientsPublication . Ferreira, Ana Isabel Leitão; Nunes, Catarina S.; Mendes, Joaquim; Amorim, PedroThe aim of this study was to investigate the relation between the blink reflex evoked by an electrical stimulus and the depth of anesthesia induced with intravenous anesthetic drug propofol. The blink reflex was stimulated before the propofol infusion started (baseline) and after, every 6 s. The electromyographic responses and the level of sedation/anesthesia scores as well as the estimated effect-site concentration of propofol were recorded in 11 patients. The blink reflex responses were abolished when patients were still conscious. The clinical scale of anesthesia increased with increasing concentrations of propofol. To predict the level of sedation/anesthesia a multinomial logistic regression was performed using blink reflex extracted features at the frequency domain. Several features proved to be good predictor estimates and the model showed to be useful. This information could be helpful to assess the moment of loss of consciousness and thus personalize anesthesia.
- Neuro-fuzzy models to predict the required propofol amount for loss of consciousness during general anesthesia: a preliminary studyPublication . Ferreira, Ana Isabel Leitão; Mendes, Joaquim; Amorim, Pedro; Nunes, Catarina 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.