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Axial flow blood pumps for cardiac assistance have proven their clinical viability and benefit in recent years. However, the clinical systems to date have no direct mechanism to decrease pump speed when adequate supply is not available. This may lead to ventricular collapse or increase the probability of hemolysis and thrombotic risks. Based on various experiences with left ventricular assist device (LVAD) patients in various states of recovery, at implant, in the intensive care unit, in the standard ward, and during physical exercise, 11 different algorithms were developed for the automatic detection of ventricular suction. These detection algorithms analyze the flow pattern for the presence of distinct suction indicators. For selection and optimization of the algorithms, 1000 records from approximately 100 patients were collected. Each record contains 5 s of pump flow, current, and arterial pressure. Three experts classified these records in terms of suction probability and other abnormalities. The optimization was developed in Matlab, capable of solving a fifth-dimensional optimization problem with 256 different algorithm combinations. The optimization resulted in a set of 6 algorithms, each with specific thresholds. The system detects 100% of the known suction events with 0.28% of false-positive interpretations. If tuned to avoid any false-positive detection, 90.7% of the certain events would be detected. A strategy for the development of a robust suction detection system for axial blood pumps was found. This system will be integrated into an automatic pump speed control system to provide adequate perfusion for the LVAD recipient, without excessive unloading of the ventricle.