Abstract
As models of cardiac electrophysiology (EP) are maturing, an increasing effort is being put in their translation to the bed side, in particular for abnormal cardiac rhythm diagnosis and therapy planning. However, the parameters that govern these models need to be estimated from noisy and sparse clinical data in an efficient and precise way, which is still an unsolved challenge. Invasive cardiac mapping provides the richest EP information available today. This paper proposes a new method to estimate a local map of electrical conductivities of the bi-ventricular heart by applying the back-propagation error concept, widely used in neural networks. The method works when either endocardial or epicardial activation time maps are available, and can cope with heterogeneous cardiac tissue. The method was evaluated on synthetic data, showing significantly increased performance in goodness of fit compared to a global parameter estimation approach. The resulting predictive power of the personalized model for cardiac resynchronization therapy was then assessed on 16 swine models of left bundle branch block with rich imaging and EP data before and after CRT. With the proposed personalization, the average error in activation time post CRT was \(10 \pm 4.5\) ms, lower than the observed pre/post-CRT difference of \(26.3 \pm 16.8\) ms.
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Pheiffer, T. et al. (2017). Estimation of Local Conduction Velocity from Myocardium Activation Time: Application to Cardiac Resynchronization Therapy. In: Pop, M., Wright, G. (eds) Functional Imaging and Modelling of the Heart. FIMH 2017. Lecture Notes in Computer Science(), vol 10263. Springer, Cham. https://doi.org/10.1007/978-3-319-59448-4_23
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DOI: https://doi.org/10.1007/978-3-319-59448-4_23
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