Abstract
Advancement of implanted Left Ventricular Assist Device (LVAD) technology includes modern sensing and control methods to enable online diagnostics and monitoring of patients using on-board sensors. These methods often rely on a cardiovascular system (CVS) model, the parameters of which must be identified for the specific patient. Some of these, such as the Systemic Vascular Resistance (SVR), can be estimated online while others must be identified separately. This paper describes a three-staged approach for designing a parameter identification algorithm (PIA) for this problem. The approach is demonstrated using a two-element Windkessel model of the systemic circulation with a time-varying elastance for the left ventricle. A parameter identifiability stage is followed by identification using an unscented Kalman filter (UKF) which uses measurements of left ventricle pressure (Plv), aortic pressure (Pao), aortic flow (Qa), and known input measurement of LVAD flow rate (Qvad). Both simulation and experimental data from animal experiments were used to evaluate the presented methods. By bounding the initial guess for left ventricular volume, the identified CVS model is able to reproduce signals of Plv, Pao and Qa within a normalized root mean squared error (nRMSE) of 5.1 %, 19 %, and 11 %, respectively during simulations. Experimentally, the identified model is able to estimate SVR with an accuracy of 0.56 % compared with values from invasive measurements. Diagnostics and physiological control algorithms on-board modern LVADs could use CVS models other than those shown here, and the presented approach is easily adaptable to them. The methods also demonstrate how to test the robustness and accuracy of the identification algorithm.
Evaluating the Hemodynamical Response of a Cardiovascular System under Support of a Continuous Flow Left Ventricular Assist Device via Numerical Modeling and Simulations
