Browsing by Author "Antunes, L."
Now showing 1 - 6 of 6
Results Per Page
Sort Options
- Comparison of neural networks, fuzzy and stochastic prediction models for return of consciousness after general anesthesiaPublication . Nunes, Catarina S.; Mendonca, T. F.; Amorim, Pedro; Ferreira, D. A.; Antunes, L.This 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.
- Electroencephalogram-based indices applied to dogs' depth of anaesthesia monitoringPublication . Brás, Susana; Georgakis, A.; Ribeiro, L.; Ferreira, David A.; Silva, A.; Antunes, L.; Nunes, Catarina S.Hypnotic drug administration causes alterations in the electroencephalogram (EEG) in a dose-dependent manner. These changes cannot be identified easily in the raw EEG, therefore EEG based indices were adopted for assessing depth of anaesthesia (DoA). This study examines several indices for estimating dogs' DoA. Data (EEG, clinical end-points) were collected from 8 dogs anaesthetized with propofol. EEG was initially collected without propofol. Then, 100 ml h−1 (1000 mg h−1) of propofol 1% infusion rate was administered until a deep anaesthetic stage was reached. The infusion rate was temporarily increased to 200 ml h−1 (2000 mg h−1) to achieve 80% of burst suppression. The index performance was accessed by correlation coefficient with the propofol concentrations, and prediction probability with the anaesthetic clinical end-points. The temporal entropy and the averaged instantaneous frequency were the best indices because they exhibit: (a) strong correlations with propofol concentrations, (b) high probabilities of predicting anaesthesia clinical end-points.
- EMG contributes to improve cerebral state Index modeling in dogs anesthesiaPublication . Brás, S.; Ferreira, D. A.; Antunes, L.; Ribeiro, L.; Nunes, Catarina S.; Gouveia, S.Cerebral State Index (CSI) is a measure of depth of anesthesia (DoA) developed for humans, which is traditionally modeled with the Hill equation and the propofol effect-site concentration (Ce). The CSI has been studied in dogs and showed several limitations related to the interpretation of EEG data. Nevertheless, the CSI has a lot of potential for DoA monitoring in dogs, it just needs to be adjusted for this species. In this work, an adapted CSI model is presented for dogs considering a) both Ce and EMG as inputs and b) a fuzzy logic structure with parameters optimized using the ANFIS method. The new model is compared with traditional Hill model using data from dogs in routine surgery. The results showed no significant impact in the model performance with the change of model structure (Fuzzy instead of Hill). The residuals of the Hill model were significantly correlated with the EMG, indicating that the latter should be considered in the model. In fact, the EMG introduction in CSI model significantly decreased the modeling error: 11.8 [8.6; 15.2] (fuzzy logic) versus 20.9 [16.4; 29.0] (Hill). This work shows that CSI modeling in dogs can be improved using the current human anesthesia set-up, once the EMG signal is acquired simultaneously with the CSI index. However, it does not invalidate the search of new DoA indices more adjusted to use in dog’s anesthesia.
- Fuzzy logic model to describe anesthetic effect and muscular influence on EEG Cerebral State IndexPublication . Brás, S.; Gouveia, S.; Ribeiro, L.; Ferreira, David A.; Antunes, L.; Nunes, Catarina S.The well-known Cerebral State Index (CSI) quantifies depth of anesthesia and is traditionally modeled with Hill equation and propofol effect-site concentration (Ce). This work brings out two novelties: introduction of electromyogram (EMG) and use of fuzzy logic models with ANFIS optimized parameters. The data were collected from dogs (n = 27) during routine surgery considering two propofol administration protocols: constant infusion (G1, n = 14) and bolus (G2, n = 13). The median modeling error of the fuzzy logic model with Ce and EMG was lower or similar than that of the Hill with Ce (p = 0.012-G1, p = 0.522-G2). Furthermore, there was no significant performance impact due to model structure alteration (p = 0.288-G1, p = 0.330-G2) and EMG introduction increased or maintained the performance (p = 0.036-G1, p = 0.798-G2). Therefore, the new model can achieve higher performance than Hill model, mostly due to EMG information and not due to changes in the model structure. In conclusion, the fuzzy models adequately describe CSI data with advantages over traditional Hill models.
- Modelling the dynamics of depth of anaesthesia: cerebral state index in dogsPublication . Bressan, Nadja; Castro, A.; Bras, S.; Ribeiro, L.; Ferreira, D. A.; Silva, A.; Antunes, L.; Nunes, Catarina S.The goal of this study was to obtain models that described the relation between the anaesthetic drug infusions (propofol) and an electroencephalogram (EEG) derived index (Cerebral State Index - CSI) during general anaesthesia in dogs. The first phase integrated the adaptation of hardware for EEG acquisition and exploration for the best electrodes position in dogs skull. The clinical protocol implementation and data collection were the next steps followed by CSI modeling. CSI showed adequate response to changes in drug infusion, reflecting the changes of depth of anaesthesia in dogs. The models obtained adjusted well to the original CSI data and also predicted the CSI trend during surgery. Using this monitor in current practice might improve quality in the anaesthesia procedure providing a useful tool to administer a correct sedation.
- A step towards effect-site target-controlled infusion with propofol in dogs: a ke0 for propofolPublication . Brás, S.; Bressan, Nadja; Ribeiro, L.; Ferreira, D. A.; Antunes, L.; Nunes, Catarina S.Target-controlled infusion (TCI) anesthesia using target effect-site concentration rather than plasma concentration provides less drug consumption, safer anesthesia, less undesired side effects and improved animal welfare. The aim of this study was to calculate the constant that converts propofol plasma into effect-site concentration (ke0) in dogs, and to implement it in a TCI system and compare it with the effect on the central nervous system (CNS). All dogs were subjected to general anesthesia using propofol. Fourteen dogs were used as the pilot group to calculate ke0, using the tpeak method. Fourteen dogs were used as the test group to test and validate the model. Rugloop ii® software was used to drive the propofol syringe pump and to collect data from S/5 Datex monitor and cerebral state monitor. The calculated ke0 was incorporated in an existing pharmacokinetic model (Beths Model). The relationship between propofol effect site concentrations and anesthetic planes, and propofol plasma and effect-site concentrations was compared using Pearson’s correlation analysis. Average tpeak was 3.1 min resulting in a ke0 of 0.7230 min−1. The test group showed a positive correlation between anesthetic planes and propofol effect-site concentration (R = 0.69; P < 0.0001). This study proposes a ke0 for propofol with results that demonstrated a good adequacy for the pharmacokinetic model and the measured effect. The use of this ke0 will allow an easier propofol titration according to the anesthetic depth, which may lead to a reduction in propofol consumption and less undesired side effects usually associated to high propofol concentrations in dogs.