Model-based supervision of a blood pump

Abstract In this paper, we present a novel method to supervise several discrete events and continuous processes causing failures in a blood pump. These are potential hazards which regularly cause problems in intensive care routine. We propose an indicator that considers the nonlinear shear thinning flow properties of blood. Based on a threefold of physiological motivated measures, we calculate an indicator which is not only able to detect ongoing events like gas in the blood phase but also to predict upcoming events like the suction of the withdrawing cannula to the surrounding vessel's wall. We present an algorithm that is embedded in a distributed 32 bit microcontroller network and holding hard real-time constraints. We were able to evaluate out algorithms in-vivo. For this algorithm we analyzed online data of more than 140 hours of animal experiments.