Neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-25942-5_5
Reference13 articles.
1. Aras, K., et al.: Experimental data and geometric analysis repository - EDGAR. J. Electrocardiol. 48(6), 975–981 (2015). https://doi.org/10.1016/j.jelectrocard.2015.08.008
2. Bear, L.R., et al.: How accurate is inverse electrocardiographic mapping? Circ. Arrhythm. Electrophysiol. 11(5), 6108 (2018). https://doi.org/10.1161/CIRCEP.117.006108. https://www.ahajournals.org/doi/10.1161/CIRCEP.117.006108
3. Cluitmans, M., et al.: Validation and opportunities of electrocardiographic imaging: from technical achievements to clinical applications. Front. Physiol. 9, 1305 (2018). https://doi.org/10.3389/fphys.2018.01305. https://www.frontiersin.org/article/10.3389/fphys.2018.01305/full
4. Cluitmans, M.J.M., Clerx, M., Vandersickel, N., Peeters, R.L.M., Volders, P.G.A., Westra, R.L.: Physiology-based regularization of the electrocardiographic inverse problem. Med. Biol. Eng. Comput. 55(8), 1353–1365 (2017). https://doi.org/10.1007/s11517-016-1595-5, https://drive.google.com/_le/d/199s6HPwSYF1WCWHiYBkJdt3ZAD99EeW/view?usp=sharing
5. Cluitmans, M., et al.: In vivo validation of electrocardiographic imaging. JACC: Clin. Electrophysiol. 3(3), 232–242 (2017). https://doi.org/10.1016/j.jacep.2016.11.012
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