• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1061-1065
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• 2002

Sensors need to be implanted to obtain necessary information for LVAS (Left Ventricular Assist System) operations. Size of the sensors can prevent them from being implanted in a patient and reliabilities of the sensors are questionable for a long term use. In this wort we utilize a developed pump model to estimate flow and pressure difference across the pump without implanted sensors and present a method to obtain the physiological variables as aorta pressure and left ventricle pressure from the pump model and pulsatility of flow estimate or pressure difference estimate. These estimated variables can be used for LVAS control as an index or indices.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.368-375
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• 2003

Data fusion approach is investigated to avoid suction in the left ventricular assist system (LVAS) using a nonpulsatile pump. LVAS requires careful control of pump speed to support the heart while preventing suction in the left ventricle and providing proper cardiac output at adequate perfusion pressure to the body. Since the implanted sensors are usually unreliable for long-term use, a sensorless approach is adopted to detect suction. The pump model is developed to provide the load coefficient as a necessary signal to the data fusion system without the implanted sensors. The load coefficient of the pump mimics the pulsatility property of the actual pump flow and provides more comparable information than the pump flow after suction occurs. Four signals are generated from the load coefficient as inputs to the data fusion system for suction detection and a neural fuzzy method is implemented to construct the data fusion system. The data fusion approach has a good ability to classify suction status and it can also be used to design a controller for LVAS.