Abstract
We present a model-based framework for imaging 3D cardiac transmembrane potential (TMP) distributions from body surface potential (BSP) measurements. Based on physiologically motivated modeling of the spatiotemporal evolution of TMPs and their projection to body surface, the cardiac electrophysiology is modeled as a stochastic system with TMPs as the latent dynamics and BSPs as external measurements. Given the patient-specific data from BSP measurements and tomographic medical images, the inverse problem of electrocardiography (IECG) is solved via state estimation of the underlying system, using the unscented Kalman filtering (UKF) for data assimilation. By incorporating comprehensive a priori physiological information, the framework enables direct recovery of intracardiac electrophysiological events free from commonly used physical equivalent cardiac sources, and delivers accurate, robust, and fast converging results under different noise levels and types. Experiments concerning individual variances and pathologies are also conducted to verify its feasibility in patient-specific applications.
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