Noninvasive inference of patient-specific intramural electrical activity from surface electrocardiograms (ECG) lacks a unique solution in the absence of prior assumptions. While 3D cardiac electrophysiological models emerged to be a viable vehicle for constraining this inference with knowledge about the spatiotemporal dynamics of cardiac excitation, it is important for the inference to be robust to errors in these highdimensional model predictions given the limited ECG data. We present an innovative solution to this problem by exploiting the low-dimensional structure of the solution space – a powerful regularizer in overcoming the lack of measurements – within the dynamic inference guided by physiological models. We present the first Bayesian inference framework that allows the exploration of both the spatial sparsity of cardiac excitation and its complex nonlinear spatiotemporal dynamics for an improved inference of patient-specific intramural electrical activity. The benefit of this integration is verified in both synthetic and real-data experiments, where we present one of the first detailed, point-by-point comparison of the reconstructed electrical activity to in-vivo catheter mapping data.
CITATION STYLE
Xu, J., Sapp, J. L., Dehaghani, A. R., Gao, F., Horacek, M., & Wang, L. (2015). Robust transmural electrophysiological imaging: Integrating sparse and dynamic physiological models into ECG-based inference. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9350, pp. 519–527). Springer Verlag. https://doi.org/10.1007/978-3-319-24571-3_62
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