9 March 2018 Towards non-invasive electrocardiographic imaging using regularized neural networks
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Abstract
We present a new data-driven technique for non-invasive electronic imaging of cardiovascular tissues using routinely-measured body-surface electrocardiogram (ECG) signals. While traditional ECG imaging and 3D reconstruction algorithms typically rely on a combination of linear Fourier theory, geometric and parametric modeling, and invasive measurements via catheters, we show in this work that it is possible to learn the complicated inverse map, from body-surface potentials to epicardial or endocardial potentials, by exploiting the powerful approximation properties of neural networks. The key contribution here is a formulation of the inverse problem that allows historical data to be leveraged as ground-truth for training the inverse operator. We provide some initial experiments, and outline a path for extending this technique for real-time diagnostic applications.
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Abhejit Rajagopal, Vincent Radzicki, Hua Lee, Shivkumar Chandrasekaran, "Towards non-invasive electrocardiographic imaging using regularized neural networks", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105732O (9 March 2018); doi: 10.1117/12.2294474; https://doi.org/10.1117/12.2294474
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