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Model Predictive Control of Selective Vagal Nerve Stimulation for Regulating Cardiovascular System

Previous studies have proposed closed-loop methods to determine one of the vagal nerve stimulation (VNS) parameters based on the feedback of heart rate but never consider the blood pressure. This study develops a nonlinear model predictive controller (NMPC) for a multi-location VNS system, which controls heart rate (HR) and mean arterial pressure (MAP) by independently manipulating multiple stimulation parameters in three different locations. Pulsatile and non-pulsatile models of the integrated cardiovascular system and baroreflex regulation are developed to predict the dynamic response of the MAP and HR. The two models are compared and elicit similar dynamics. The NMPC algorithm incorporates the non-pulsatile model and is evaluated by the pulsatile model. Three set point tracking cases are presented and discussed. The performance of NMPC shows the feasibility and usefulness of the proposed control algorithm for the regulation of HR and MAP during multi-location VNS.