Institute of Engineering Design and Product Development, TU Wien, 1060 Vienna, Austria.
Institute of Chemical, Environmental and Bioscience Engineering, TU Wien, 1060 Vienna, Austria.
Sensors (Basel). 2020 Mar 6;20(5):1451. doi: 10.3390/s20051451.
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 = 0.98, root mean squared error (RMSE) = 46 mLmin; pressure difference r = 0.98, RMSE = 8.7 mmHg; and viscosity r = 0.98, RMSE = 0. 0.049 mPas. The results suggest that the presented methods can be used to accurately predict blood flow rate, pressure, and viscosity online.
血泵已在心脏支持设备、氧合器和透析系统等中得到应用。通常，没有空间容纳传感器，或者当需要长期运行时，传感器根本不可靠。然而，控制系统依赖于这些难以测量的参数，例如血流率和压差，因此它们的估计在这些医疗设备的开发过程中起着核心作用。血液的粘度不仅会影响这些参数的估计，而且通常是医生和工程师都非常感兴趣的参数。在这项工作中，使用高斯过程回归模型提出了血流率、压差和粘度的估计方法。使用不同的水-甘油混合物来模拟血液。数据是从为体外测试回路中的体内氧合器设计的定制血泵中收集的。估计是从电机电流和电机速度测量值进行的，其准确性针对以下方面进行了测量：血流率 r = 0.98，均方根误差 (RMSE) = 46 mLmin；压差 r = 0.98，RMSE = 8.7 mmHg；粘度 r = 0.98，RMSE = 0.049 mPas。结果表明，所提出的方法可用于在线准确预测血流率、压力和粘度。
