Browsing by Author "Mendonca, Teresa"
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- Control of depth of anesthesia using MUSMAR: exploring electromyography and the analgesic dose as accessible disturbancesPublication . Nunes, Catarina S.; Mendonca, Teresa; Lemos, Joao M.; Amorim, PedroThe problem of controlling the level of depth of anesthesia measured by the Bispectral Index (BIS) of the electroencephalogram of patients under general anesthesia, is considered. It is assumed that the manipulated variable is the infusion rate of the hypnotic drug propofol, while the drug remifentanil is also administered for analgesia. Since these two drugs interact, the administration rate of remifentanil is considered as an accessible disturbance in combination with the level of electromyography (EMG) that also interferes with the BIS signal. In order to tackle the high uncertainty present on the system, the predictive adaptive controller MUSMAR is used. The performance of the controller is illustrated by means of simulation with 45 patient individual adjusted models, which incorporate the effect of the drugs interaction on BIS. This controller structure proved to be robust to the EMG and remifentanil disturbances, patient variability, changing reference values and noise.
- Modeling anesthetic drugs' pharmacodynamic interaction on the bispectral index of the EEG: the influence of heart ratePublication . Nunes, Catarina S.; Mendonca, Teresa; Bras, Susana; Ferreira, David A.; Amorim, PedroThe effect of drugs’ interaction on the brain signal Bispectral Index (BIS) is of great importance for an anesthesia control drug infusion system. In this study, the objective was to inspect the influence of patient’s heart rate on the effect of the drugs on BIS. With this goal, the patient’s heart rate was incorporated in an drug interaction model. The model was fitted per patient during anesthesia induction, and tested for prediction under surgery. The results showed that the model with time changing parameters incorporating patient’s heart rate has a better performance than a non adjusted model. Three clusters of models were also identified using the fuzzy cmeans algorithm. These clusters will help to distinguish between different patients’ dynamics.
- Predictive adaptive control of unconsciousness: exploiting remifentanil as an accessible disturbancePublication . Mendonca, Teresa; Nunes, Catarina S.; Magalhaes, Hugo; Lemos, Joao M.; Amorim, PedroThe problem of controlling the level of unconsciousness measured by the BIS index of patients under anesthesia, is considered. It is assumed that the manipulated variable is the administration rate of propofol, while remifentanil is also administered for analgesia. Since these two drugs interact, the administration rate of remifentanil is considered as an accessible disturbance. A predictive adaptive controller structure that explores this fact is proposed and illustrated by means of simulation.
- Towards the control of depth of anaesthesia: identification of patient variabilityPublication . Nunes, Catarina S.; Alonso, Hugo; Castro, Ana; Amorim, Pedro; Mendonca, TeresaDepth of anaesthesia (DOA) is usually assessed through the Bispectral Index (BIS) and State Entropy (SE), which derived EEG signals. Studying the effect of drug interaction on these signals is of great importance for the development of a suitable drug infusion system designed to control DOA. In this paper, two renowned pharmacokinetic (PK) models for the anaesthetic drug propofol are considered, and their influence on the fitting and prediction abilities of a drug interaction model for BIS and SE is assessed. This interaction model is fitted to the individual patient data during anaesthesia induction and tested for prediction during surgery. Two identification methods are considered for the fitting purpose: a hybrid method and a nonlinear least squares curve-fitting algorithm. The results obtained for 7 patients show that the choice of the PK model has influence on the overall performance of the interaction model; in particular, only one PK model leads to good results in the prediction phase. The choice of the identification method is equally important, being the hybrid method the better suited. The successful identification of patient variability here obtained is a key step towards the control of DOA.