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EMG contributes to improve cerebral state Index modeling in dogs anesthesia
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Advisor(s)
Abstract(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.
Description
Keywords
Pedagogical Context
Citation
S. Brás, D. A. Ferreira, L. Antunes, L. Ribeiro, C. S. Nunes and S. Gouveia, "EMG contributes to improve Cerebral State Index modeling in dogs anesthesia," 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 2011, pp. 6593-6596
Publisher
IEEE