Estimation of Subject-specific Hemodynamic Parameters Using Differential Evolution Algorithm and Parallel Computing

Cardiovascular system modeling involves a great number of parameters (resistances, compliances, network geometries, etc.) which are unknown a priori and need to be identified. The present work aims at developing an identification procedure for estimating subject-specific hemodynamic parameters based on a one-dimensional (1-D) tree-like vascular flow model. For the present identification method, the arterial stiffness and stress-free lumen radius as well as the terminal lumped resistances and compliance were estimated. This work presented a preliminary validation using in vivo data to assess the feasibility and accuracy of the proposed method. The results show that the identification method is accurate and feasible. An excellent fit was achieved with root-mean-square error (RMSE) of 2.18% between the in vivo measured and the model produced pressure waveforms. Keywords— Windkessel models, reflection coefficient, hemodynamic inverse problem, parameter identification, least-squares estimation.