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Fuzzy logic model to describe anesthetic effect and muscular influence on EEG Cerebral State Index

Publication . 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.

2013Journal article

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EMG contributes to improve cerebral state Index modeling in dogs anesthesia

Publication . 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.

2011Conference object

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