A Left Ventricular Assist Device (LVAD) is a mechanical pump that helps patients with Heart Failure (HF) condition. This pump works in parallel to the ailing heart and provides a continuous flow from the weak left ventricle to the ascending aorta. The current supplied to the pump motor controls the flow of blood. A new feedback control system is developed to automatically adjust the pump motor current to provide the blood flow required by the level of activity of the patient. The systemic Vascular Resistance (R S) is the only undeterministic variable parameter in a patient-specific model and also a key value that expresses the level of activity of the patient. The rest of the parameters are constants for a patientspecific model. To determine the level of activity of the patient, an inverse problem approach is followed. The output data (pump flow) is observed and using an optimized search technique, the best model to describe such output is selected. Furthermore, the estimated R S is used in another patient-specific cardiovascular model that assumes a healthy heart, to determine the blood flow demand. Once the physiological demand is established, the current supplied to the pump motor of the LVAD can be adjusted to achieve the desired blood flow through the cardiovascular system. This process can be performed automatically in a real-time basis using information that is readily available and thus rendering a high degree of applicability. Results from simulated data shows that the feedback control system is fast and very stable.

The use of a rotary Left Ventricular Assist Device (LVAD) as a bridge-to-recovery treatment is gaining considerable attention in the LVAD research community. Using a mathematical model of the cardiovascular-LVAD system, this paper intends to define the critical control parameters in terms of power and rotational speed of the LVAD to ensure normal dynamics of the aortic valve for different levels of patient's activity and severity of heart failure. The effects of permanent closure of the aortic valve on the hemodynamics of the patient and the pump flow characteristics, if the critical control values are exceeded, are also examined. Additionally, LVAD power and speed control parameters that yield a given percentage of the cardiac cycle during which the aortic valve remains open are examined indicating that the severity of the heart failure is a very important factor in deciding the appropriateness of the LVAD as a bridge-to recovery treatment.

Rotary Left Ventricular Assist Devices (LVAD) are mechanical pumps implanted in patients with congestive heart failure to assist their heart in pumping the required amount of blood in the circulatory system. Until recently, the combined mathematical model of the LVAD coupled with the left ventricle has assumed the availability of the rotational speed of the pump as the independent control variable. In reality, however, the device is controlled by the pump motor current which, in turn, produces the desired rotational speed of the pump motor. Therefore, the actual implementation of any desired speed controller for the device requires the solution of an inverse problem in order to determine the corresponding motor current that yields the desired pump speed. Recently, it has been observed from in-vivo experiments that an LVAD that is controlled by a motor current with a given profile (constant or ramp-like) has yielded a corresponding pump speed that exhibits a superposition of an oscillatory component which is synchronized with the pulsatility of the heart hemodynamic variables.Because of this, it has become evident that the solution of this inverse problem is extremely difficulty to accomplish.In this paper, we reformulate the existing combined LVAD and left ventricle model in such a way so as to introduce the pump motor current instead of the pump speed as the control variable, hence avoiding the inverse problem altogether. This new model is not only a more realistic representation of the LVAD control variable but also is much more practical in that it allows for the derivation of a controller directly in terms of the pump motor current rather than indirectly in terms of its rotational speed. Validation of this model and the challenges involved in using it when designing a feedback controller for the LVAD are also discussed.

A new suction detection algorithm for rotary Left Ventricular Assist Devices (LVAD) is presented. The algorithm is based on a Lagrangian Support Vector Machine (LSVM) model. Six suction indices are derived from the LVAD pump flow signal and form the inputs to the LSVM classifier. The LSVM classifier is trained and tested to classify pump flow patterns into three states: No Suction, Approaching Suction, and Suction. The proposed algorithm has been tested using existing in vivo data. When compared to three existing methods, the proposed algorithm produced superior performance in terms of classification accuracy, stability, and learning speed. The ability of the algorithm to detect suction provides a reliable platform in the development of a pump speed controller that has the capability of avoiding suction.

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This paper intends to define an optimal range for the pump speed of Rotary Left Ventricular Assist Devices (LVADs) that are used in bridge-to-recovery treatments. If the pump is operating within that optimal range, the aortic valve will be working properly (i.e. opening and closing) in each cardiac cycle. The proper operation of the aortic valve is a very important factor in helping the heart muscle recovers. The optimal range varies depending on the severity of the Heart Failure (HF) and the level of activity of the patient. A comparison is shown between the total flow produced as a result of operating the pump within the optimal range and the physiological demand of the patient. The comparison suggests that for cases of mild to moderate HF the flow produced is close to the physiological demand, but in severe cases the flow is significantly less than what the patient requires. Furthermore, our results suggest that data from the pump flow and the left ventricle volume signals can be used to test whether or not the aortic valve is experiencing permanent closure. Also an investigation of the aortic valve opening duration is presented for two cases: first, for mild HF case with varying Heart Rate (HR) and then for fixed HR and mild to severe HF cases. These Simulation results are obtained using a 6(th) order mathematical model of the cardiovascular-LVAD system.

Aortic valve dynamics -which implies continuous opening and closing of the aortic valve in each cardiac cycle during the feedback control of the rotary Left Ventricular Assist Devices (LVAD) support -has important clinical implications for patients with mild congestive heart failure. When the LVAD is implanted in such patients as a bridge-torecovery device, permanent closure of the aortic valve must be avoided by maintaining proper control on the power delivered to the device. In this paper, a new aortic valve dynamics detection algorithm based on a Lagrangian Support Vector Machine (LSVM) model is presented. A detection indicator is derived from the systemic vascular flow signal in the circulatory system using a nonlinear mathematical model of the combined cardiovascular-LVAD system and forms the input to the LSVM classifier. The LSVM classifier is trained and tested to classify the aortic valve dynamics into two states: aortic valve opening and closing (i.e. operating normally) and aortic valve permanently closed. Our results show that the proposed algorithm can detect the aortic valve dynamics effectively in terms of classification accuracy and stability. This classifier will be an integral part in the development of a feedback controller for the LVAD when used on patients as a bridge-to-recovery device. The output of the classifier will be used to adjust the power delivered to the LVAD to ensure that the aortic valve opens and closes normally within each cardiac cycle while at the same time making sure that the physiological demands of the patient are met.