A computational model has been developed to study the effects of left ventricular assist devices (LVADs) on the cardiovascular system during a ventricular collapse. The model consists of a toroidal pulsatile blood pump and a closed loop circulatory system. Together, they predict the pump's motor current traces that reflect ventricular suck-down and provide insights into torque magnitudes that the pump experiences. In addition, the model investigates likeliness of a suction event and predicts reasonable outcomes for a few test cases. Ventricular collapse was modeled with the help of a mock circulatory loop consisting of a artificial left ventricle and centrifugal continuous flow pump. This study also investigates different suction detection schemes and proposes the most suitable suction detection algorithm for the TORVAD pump, toroidal left ventricular assist device. Model predictions were further compared against the data sampled during in vivo animal trials with the TORVAD system. The two sets of results are in good accordance. / text

The Left Ventricular Assist Device (LVAD) is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. The rotary type pumps are controlled by varying the pump motor current to adjust the amount of blood flowing through the LVAD. One important challenge in using such a device is the desire to provide the patient with as close to a normal lifestyle as possible until a donor heart becomes available. The development of an appropriate feedback controller that is capable of automatically adjusting the pump current is therefore a crucial step in meeting this challenge. In addition to being able to adapt to changes in the patient's daily activities, the controller must be able to prevent the occurrence of excessive pumping of blood from the left ventricle (a phenomenon known as ventricular suction) that may cause collapse of the left ventricle and damage to the heart muscle and tissues. In this dissertation, we present a new suction detection system that can precisely classify pump flow patterns, based on a Lagrangian Support Vector Machine (LSVM) model that combines six suction indices extracted from the pump flow signal to make a decision about whether the pump is not in suction, approaching suction, or in suction. The proposed method has been tested using in vivo experimental data based on two different LVAD pumps. The results show that the system can produce superior performance in terms of classification accuracy, stability, learning speed, iv and good robustness compared to three other existing suction detection methods and the original SVM-based algorithm. The ability of the proposed algorithm to detect suction provides a reliable platform for the development of a feedback control system to control the current of the pump (input variable) while at the same time ensuring that suction is avoided. Based on the proposed suction detector, a new control system for the rotary LVAD was developed to automatically regulate the pump current of the device to avoid ventricular suction. The control system consists of an LSVM suction detector and a feedback controller. The LSVM suction detector is activated first so as to correctly classify the pump status as No Suction (NS) or Suction (S). When the detection is “No Suction”, the feedback controller is activated so as to automatically adjust the pump current in order that the blood flow requirements of the patient’s body at different physiological states are met according to the patient’s activity level. When the detection is “Suction”, the pump current is immediately decreased in order to drive the pump back to a normal No Suction operating condition. The performance of the control system was tested in simulations over a wide range of physiological conditions.