Neural networks and nonlinear regression modelling and control of depth of anaesthesia for spontaneously breathing and ventilated patients.
Various attempts have been made to control the depth of anaesthesia by observing different variables. In some studies, depth of anaesthesia has been correlated with inferential parameters and the control has been made through these inferred parameters. No single system has been reported which provides a fully developed architecture to control the depth of anaesthesia. This study is concerned with the development of controllers and patient models via Artificial Neural Networks and regression analysis. Two types of data sets were used for the training and development of models and controllers. The first set was for spontaneously breathing and the second set for ventilated patients. All of the controllers and patient models gave satisfactory results when tested individually. Later these two sets of controllers and patient models were studied in closed-loop modes. The robustness to the sensitivity of the regression patient model was also investigated. Various tests were performed with these closed-loop situations. Results and performance of these tests are discussed in the paper.
Rehman
H U
HU
Department of Automatic Control and Systems Engineering, University of Sheffield, UK.
Linkens
D A
DA
Asbury
A J
AJ
D000758:Anesthesia; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012121:Respiration, Artificial; D012680:Sensitivity and Specificity