The rotary left ventricular assist device (LVAD) has been an effective option for end-stage heart failure. However, while clinically using the LVAD, patients are often at significant risk for ventricular collapse, called suction, mainly due to higher LVAD speeds required for adequate cardiac output. Some proposed suction detection algorithms required the external implantation of sensors, which were not reliable in long-term use due to baseline drift and short lifespan. Therefore, this study presents a new suction detection system only using the LVAD intrinsic blood pump parameter (pump speed) without using any external sensor. Three feature indices are derived from the pump speed and considered as the inputs to four different classifiers to classify the pumping states as no suction or suction. The in-silico results using a combined human circulatory system and LVAD model show that the proposed method can detect ventricular suction effectively, demonstrating that it has high classification accuracy, stability, and robustness. The proposed suction detection system could be an important part in the LVAD for detecting and avoiding suction, while at the same time making the LVAD meet the cardiac output demand for the patients. It could also provide theoretical basis and technology support for designing and optimizing the control system of the LVAD.
旋转式左心室辅助装置（LVAD）是治疗终末期心衰的一种有效方法。然而当临床上使用 LVAD 时，患者通常会经历心室塌陷的严重危险，这种现象叫做抽吸，主要是由于为了满足心输出量要求所需的过高 LVAD 转速导致的。某些抽吸检测算法又因传感器位置偏移和使用寿命短等原因无法长期应用于临床。因此，本研究基于 LVAD 的内在血泵参数（泵转速）提出了一种新颖的抽吸检测方法，避免了额外传感器的使用。从泵转速提取三种特征指标后作为四种分类器的输入，采用这些分类器对无抽吸和抽吸状态进行分类。基于人体循环系统和 LVAD 耦合模型的仿真结果表明，该方法可以有效地检测出抽吸现象，具有较高的分类精度、稳定性和鲁棒性。此抽吸检测系统可作为 LVAD 的重要组成部分，检测并避免抽吸现象的发生，同时使 LVAD 保证患者的心输出量要求，并为 LVAD 控制系统的设计和优化提供理论依据和技术支持。.
Keywords: intrinsic blood pump parameter; left ventricular assist device; suction detection.