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Comparison of neural networks, fuzzy and stochastic prediction models for return of consciousness after general anesthesia
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Advisor(s)
Abstract(s)
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.
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Keywords
Citation
C. S. Nunes, T. F. Mendonca, P. Amorim, D. A. Ferreira and L. Antunes, "Comparison of Neural Networks, Fuzzy and Stochastic Prediction Models for return of consciousness after general anesthesia," Proceedings of the 44th IEEE Conference on Decision and Control, Seville, Spain, 2005, pp. 4827-4832
