Recognition of patient anaesthetic levels: neural network systems, principal components analysis, and canonical discriminant variates.
The goal of this study was to examine the ability of Neural Networks to recognise the levels of anaesthetic state of a patient. Data obtained under different levels of anaesthesia have been modelled for the purpose. It is shown that inferential parameters can be used to recognise the levels of anaesthesia. In addition to demonstrating the ability of neural networks for classification we were interested in understanding the classification strategy discovered by the neural networks. Multivariate data analysis techniques, namely Principal Components Analysis and Canonical Discriminant Variates, were applied to analyse the resultant networks.
Linkens
D A
DA
Department of Automatic Control and Systems Engineering, University of Sheffield, UK. d.linkens@sheffield.ac.uk
Vefghi
L
L
D000758:Anesthesia; D016002:Discriminant Analysis; D006801:Humans; D008991:Monitoring, Physiologic; D015999:Multivariate Analysis; D016571:Neural Networks, Computer