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

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

Blood viscosity measurements are crucial for the diagnosis of cardiovascular and hematological diseases. Traditional blood viscosity measurements have obvious limitations because of their expensive equipment usage and large sample consumption. In this study, blood viscosity was measured by the oscillating circuit method and impedance analysis method based on single QCM. In addition, the effectiveness of two methods with high precision and less sample is proved by the experiments. Moreover, compared to the result from a standard rotational viscometer, the maximum relative errors of the proposed oscillating circuit method and impedance analysis method are ±5.2% and ±1.8%, respectively. A reliability test is performed by repeated measurement (N = 5), and the result shows that the standard deviation about 0.9% of impedance analysis is smaller than that of oscillating circuit method. Therefore, the impedance analysis method is superior. Further, the repeatability of impedance analysis method was evaluated by regression analysis method, and the correlation coefficient R2 > 0.965 demonstrated that it had excellent reproducibility.

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

Blood pumps are becoming increasingly important for medical devices. They are used to assist and control the blood flow and blood pressure in the patient's body. To accurately control blood pumps, information about important hydrodynamic parameters such as blood flow rate, pressure difference and viscosity is needed. These parameters are difficult to measure online. Therefore, an accurate estimation of these parameters is crucial for the effective operation of implantable blood pumps. In this study, in vitro tests with bovine blood were conducted to collect data about the non-linear dependency of blood flow rate, flow resistance (pressure difference) and whole blood viscosity on motor current and rotation speed of a prototype blood pump. Gaussian process regression models are then used to model the non-linear mappings from motor current and rotation speed to the hydrodynamic variables of interest. The performance of the estimation is evaluated for all three variables and shows very high accuracy. For blood flow rate - correlation coefficient (r2) = 1, root mean squared error (RMSE) = 0.31 ml min^-1, maximal error (ERRmax) = 9.31 ml min^-1; for pressure r2 = 1, RMSE = 0.09 mmHg, ERRmax = 8.34 mmHg; and for viscosity r2 = 1,RMSE = 0.09 mPa.s, ERRmax = 0.31 mPa⋅s. The current findings suggest that this method can be employed for highly accurate online estimation of essential hydrodynamic parameters for implantable blood pumps.