Disturbance Modelling in Anaesthesia Control Using Regression Models Based on Gauss Processes

When addressing control challenges, the presence of disturbances can pose a significant obstacle, particularly in scenarios where the temporal evolution of the disturbance is uncertain. Modelling disturbances in biological systems further compounds this challenge due to the inherent complexity of such systems. The anaesthesia model is situated within the domain of complex systems, for which there is presently no universally endorsed approach for modelling disturbances. However, this paper introduces a statistical methodology for characterising the trajectory between known points through the application of Gaussian Process Regression. The simulation results proves the introduced advantages.