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Body Surface Potential Propagation Maps During Macroreentrant Atrial Arrhythmias. A Simulation Study.

, , , , , and . CinC, page 915-918. www.cinc.org, (2013)

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Phase singularity point tracking for the identification of typical and atypical flutter patients: A clinical-computational study., , , , , , , and . Comput. Biol. Medicine, (2019)Simplified Electrophysiology Modeling Framework to Assess Ventricular Arrhythmia Risk in Infarcted Patients., , , , , , , , and . FIMH, volume 12738 of Lecture Notes in Computer Science, page 531-539. Springer, (2021)Intra-cardiac Signatures of Atrial Arrhythmias Identified by Machine Learning and Traditional Features., , , , , and . FIMH, volume 12738 of Lecture Notes in Computer Science, page 671-678. Springer, (2021)Assessment of Risk for Ventricular Tachycardia based on Extensive Electrophysiology Simulations., , , , , , , , , and 2 other author(s). EMBC, page 1-4. IEEE, (2023)Accuracy of Inverse Solution Computation of Dominant Frequencies and Phases during Atrial Fibrillation., , , , , , , , , and . CinC, page 537-540. www.cinc.org, (2014)Temporal Stability of Dominant Frequency as Predictor of Atrial Fibrillation Recurrence., , , , , , , , and . CinC, page 1-4. IEEE, (2019)Non-Invasive Characterization of Atrial Arrhythmic Driving Mechanisms in Computer Models., , , and . CinC, page 1-4. IEEE, (2019)Effects of Geometry in Atrial Fibrillation Markers Obtained With Electrocardiographic Imaging., , , , , , , and . CinC, page 1-4. IEEE, (2019)Evaluation of Inverse Problem with Slow-Conducting Channel in Scar Area in a Post-Infarction Model., , , , , , and . CinC, www.cinc.org, (2017)Atrial Fibrillation Driver Localization From Body Surface Potentials Using Deep Learning., , , , , , , and . CinC, page 1-4. IEEE, (2020)