Estimation Methods for Viscosity, Flow Rate and Pressure from Pump-Motor Assembly Parameters

Blood pumps have found applications in heart support devices, oxygenators, and dialysis systems, among others. Often, there is no room for sensors, or the sensors are simply unreliable when long-term operation is required. However, control systems rely on those hard-to-measure parameters, such as blood flow rate and pressure difference, thus their estimation takes a central role in the development process of such medical devices. The viscosity of the blood not only influences the estimation of those parameters but is often a parameter that is of great interest to both doctors and engineers. In this work, estimation methods for blood flow rate, pressure difference, and viscosity are presented using Gaussian process regression models. Different water–glycerol mixtures were used to model blood. Data was collected from a custom-built blood pump, designed for intracorporeal oxygenators in an in vitro test circuit. The estimation was performed from motor current and motor speed measurements and its accuracy was measured for: blood flow rate r(2) = 0.98, root mean squared error (RMSE) = 46 mL(.)min(−1); pressure difference r(2) = 0.98, RMSE = 8.7 mmHg; and viscosity r(2) = 0.98, RMSE = 0.049 mPa(.)s. The results suggest that the presented methods can be used to accurately predict blood flow rate, pressure, and viscosity online.