Minimally invasive estimation of systemic vascular parameters

A cardiovascular parameter estimator to identify the systemic vascular parameters was developed using an extended Kalman filter (EKF) algorithm. Measurements from a ventricular assist device (VAD) and arterial pressure were used in the estimator. The systemic vascular parameters are important indices of heart condition. However, obtaining these parameters usually requires invasive measurements, which are difficult to obtain under most clinical environments. Including a VAD model into the estimator and using the signals from a VAD to identify the cardiovascular parameters for VAD patients would minimize the need for indwelling sensors. This paper illustrates the use of a Novacor left ventricular assist system (LVAS) model with a cardiovascular model in the estimator to identify the systemic vascular parameters: characteristic resistance, blood inertance at the aorta, systemic compliance, and systemic resistance. Performance of the estimator was evaluated using data from a computer simulation and from a mock circulatory system experiment. Robustness of the estimator to the available measurements was also described. The estimation results showed that the estimates converged with reasonable accuracy in a limited time when the LVAS pump volume and arterial pressure were used as measurements. These parameter estimates can provide additional diagnostic information for patient and device monitoring and can be used for future VAD control development.