Loading...
Publication
Radial basis function neural networks versus fuzzy models to predict return of consciousness after general anesthesia
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 124.75 KB | Adobe PDF |
Advisor(s)
Abstract(s)
This paper presents two modelling techniques to predict return of consciousness (ROC) after general
anaesthesia, considering the effect concentration of the anaesthetic drug at awakening. First, several clinical variables were statistically analysed to determine their correlation with the awakening concentration. The anaesthetic 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. Radial basis function (RBF) neural networks were trained relating different sets of clinical values with the anaesthetic drug effect concentration at awakening. Secondly, fuzzy models were built using an Adaptive Network-Based Fuzzy Inference System (ANFIS) also relating different sets of variables. Clinical data was used to train and test the models.
The fuzzy models and RBF neural networks proved to have good prediction properties and balanced results.
Description
Keywords
Neural networks ANFIS Fuzzy models Anesthesia Prediction
Citation
C. S. Nunes, T. F. Mendonca, P. Amorim, D. A. Ferreira and L. M. Antunes, "Radial basis function neural networks versus fuzzy models to predict return of consciousness after general anesthesia," The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, CA, USA, 2004, pp. 865-868
Publisher
IEEE