System identification and control for general anesthesia based on parsimonious Wiener models

Abstract: The effect of anesthetics in the human body is usually described by Wiener models. The high number of patient-dependent parameters in the standard models, the poor excitatory pattern of the input signals (administered anesthetics) and the small amount of available input-output data make application of system identification strategies difficult.The idea behind this thesis is that, by reducing the number of parameters to describe the system, improved results may be achieved when system identification algorithms and control strategies based on those models are designed. The choice of the appropriate number of parameters matches the parsimony principle of system identification.The three first papers in this thesis present Wiener models with a reduced number of parameters for the neuromuscular blockade and the depth of anesthesia. Batch and recursive system identification algorithms are presented. Taking advantage of the small number of continuous time model parameters, adaptive controllers are proposed in the two last papers. The controller structure combines an inversion of the static nonlinearity of the Wiener model with a linear controller for the exactly linearized system, using the parameter estimates obtained recursively by an extended Kalman filter. The performance of the adaptive nonlinear controllers is tested in a database of realistic patients with good results.