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

Abstract

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 r2 = 0.98, root mean squared error (RMSE) = 46 mL.min−1; pressure difference r2 = 0.98, RMSE = 8.7 mmHg; and viscosity r2 = 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.

Non-parametric dynamical estimation of blood flow rate, pressure difference and viscosity for a miniaturized blood pump

“…The density of the samples influences the pressure difference created by the pump, while the pressure head stays constant. 20 The GPR modelling presented in this work was first tested in an in vitro setting with water-glycerol mixtures as blood substitutes in a previous study. 20 The accuracy of the estimator was high with RMSE = 46 ml min −1 , 8.7 mmHg and 0.049 mPa⋅s, for flow rate, pressure difference and viscosity respectively.…”

“…20 The GPR modelling presented in this work was first tested in an in vitro setting with water-glycerol mixtures as blood substitutes in a previous study. 20 The accuracy of the estimator was high with RMSE = 46 ml min −1 , 8.7 mmHg and 0.049 mPa⋅s, for flow rate, pressure difference and viscosity respectively. Water-glycerol is usually a widely accepted blood model, but it is nonetheless a Newtonian fluid while blood is not.…”

“…In this work, GPRMs are used to estimate viscosity, blood flow rate and pressure difference as a function of motor current and rotation speed. The proof of concept for the described method was presented previously 20 with water glycerol mixtures as rheological blood model and shows lower prediction error compared to state-of-the-art methods. In this work, the method is applied for blood in an in vitro setting and the estimation accuracy is further increased by including more delayed inputs in the model to describe more of the dynamics of the system.…”

Comparing of Frequency Shift and Impedance Analysis Method Based on QCM Sensor for Measuring the Blood Viscosity

“…However, although these conventional viscometers exhibit accurate and reliable measurement performance, these methods have obvious limitations in conventional use because of their expensive equipment and large sample consumption. These shortcomings increase the necessity of using microfluidic technology to measure and evaluate blood viscosity [ 10 , 11 , 12 ].…”

The 100 most cited articles on wearable technology in the area of Medical Informatics: A bibliometric analysis using Web of Science

“…The utilization of smart medical pumps J V Alamelu, A. Mythili includes drug administration, pumping of blood, administration of anaesthesia [7]. Research has been handled to estimate blood flow rate in blood pumps using the Gaussian process regression model and estimate viscosity, and a pressure difference is concentrated [8]. The drug parameters such as coefficient of viscosity and ambient temperature impact the flow rate in an infusion pump.…”