Parameter Identification of Cardiovascular System Model Used for Left Ventricular Assist Device Algorithms.

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 (SC) with a time-varying elastance for the left ventricle (LV). A parameter identifiability stage is followed by identification using an unscented Kalman filter (UKF), which uses measurements of LV pressure (Plv), aortic pressure (Pao), aortic flow (Qa), and known input measurement of LVAD flowrate (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 3.4% 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.

Hybrid Mock Circulatory Loop Simulation of Extreme Cardiac Events.

OBJECTIVE: This paper presents preliminary methods of incorporating the pathological conditions of cardiac arrhythmias and valvular stenosis in hybrid mock circulation loop (hMCL) operation for the enhanced verification and validation of mechanical circulatory support devices such as VADs. METHODS: The MGH/MF Waveform datasets from PhysioNet database (including both nominal and clinically diagnosed arrhythmic ECG measurements) as well as cardiovascular system model updates are used to recreate arrhythmic events and valvular stenosis in vitro. RESULTS: Preliminary results show the hMCL can recreate each tested cardiac event within 2% and 4% mean error for reference pressure tracking in the aortic and left ventricular pressure chambers, respectively. Further, frequency spectrum analysis comparisons using the magnitude-squared coherence analysis shows close alignment between measured arrhythmic and hMCL realized pressure frequency content. CONCLUSION: The generation of cardiac arrhythmias and valvular stenosis around a VAD via both model and acute measurement based methods was achieved. SIGNIFICANCE: Pathological conditions such as cardiac arrhythmias and valvular stenosis are limited in documentation despite the large percentage of patients who experience these events. This paper provides a means to begin incorporating these events into hardware-in-the-loop mock circulatory systems for next generation VAD validation and verification.