A compartmental model-based control strategy for NeuroMuscular Blockade level

This paper presents a compartmental model-based control strategy to drive the NeuroMuscular Blockade level of patients undergoing general anesthesia to a predefined target. For that purpose, a compartmental realization of a minimally parameterized nonlinear Wiener model was derived and used on an adapted version of the standard compartmental control law for linear systems. The identification of the model parameters was recursively performed by one Extended Kalman Filter during the initial bolus induction period and stopped afterwards. To overcome the fact that this identification is stopped during the closed-loop control period, uncertainties in the parameters are assumed to be present and included in the control law. Information taken from the identification of real collected cases was used to tune the parameter uncertainties. The feasibility of the whole strategy was evaluated in a bank of simulated models, giving rise to good reference tracking results even in the presence of noise.