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Giorgio Luongo

Showing results (1-10 of 9) with videos related to

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Journal of Clinical Medicine|April 30, 2021
Non-Invasive and Quantitative Estimation of Left Atrial Fibrosis Based on P Waves of the 12-Lead ECG-A Large-Scale Computational Study Covering Anatomical VariabilityClaudia Nagel, Giorgio Luongo, Luca Azzolin, et al.
IEEE Transactions on Bio-Medical Engineering|August 4, 2020
Non-Invasive Characterization of Atrial Flutter Mechanisms Using Recurrence Quantification Analysis on the ECG: A Computational StudyGiorgio Luongo, Steffen Schuler, Armin Luik, et al.
Frontiers in Physiology|July 22, 2021
Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid <i>in silico</i> and <i>in vivo</i> DatasetJorge Sánchez, Giorgio Luongo, Mark Nothstein, et al.
Frontiers in Physiology|November 12, 2021
Atrial Flutter Mechanism Detection Using Directed Network MappingMuhamed Vila, Massimo Walter Rivolta, Giorgio Luongo, et al.
Journal of Cardiovascular Electrophysiology|June 27, 2023
Spatial correlation of left atrial low voltage substrate in sinus rhythm versus atrial fibrillation: The rhythm specificity of atrial low voltage substrateDeborah Nairn, Martin Eichenlaub, Heiko Lehrmann, et al.
Frontiers in Cardiovascular Medicine|March 17, 2022
Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart FailureGiorgio Luongo, Felix Rees, Deborah Nairn, et al.
Cardiovascular Digital Health Journal|April 26, 2021
Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECGGiorgio Luongo, Luca Azzolin, Steffen Schuler, et al.
Europace : European Pacing, Arrhythmias, and Cardiac Electrophysiology : Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology|January 19, 2022
Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogramGiorgio Luongo, Gaetano Vacanti, Vincent Nitzke, et al.
Europace : European Pacing, Arrhythmias, and Cardiac Electrophysiology : Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology|September 15, 2023
Differences in atrial substrate localization using late gadolinium enhancement-magnetic resonance imaging, electrogram voltage, and conduction velocity: a cohort study using a consistent anatomical reference frame in patients with persistent atrial fibrillationDeborah Nairn, Martin Eichenlaub, Björn Müller-Edenborn, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Journal of Clinical Medicine|April 30, 2021
Non-Invasive and Quantitative Estimation of Left Atrial Fibrosis Based on P Waves of the 12-Lead ECG-A Large-Scale Computational Study Covering Anatomical VariabilityClaudia Nagel, Giorgio Luongo, Luca Azzolin, et al.
IEEE Transactions on Bio-Medical Engineering|August 4, 2020
Non-Invasive Characterization of Atrial Flutter Mechanisms Using Recurrence Quantification Analysis on the ECG: A Computational StudyGiorgio Luongo, Steffen Schuler, Armin Luik, et al.
Frontiers in Physiology|July 22, 2021
Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid <i>in silico</i> and <i>in vivo</i> DatasetJorge Sánchez, Giorgio Luongo, Mark Nothstein, et al.
Frontiers in Physiology|November 12, 2021
Atrial Flutter Mechanism Detection Using Directed Network MappingMuhamed Vila, Massimo Walter Rivolta, Giorgio Luongo, et al.
Journal of Cardiovascular Electrophysiology|June 27, 2023
Spatial correlation of left atrial low voltage substrate in sinus rhythm versus atrial fibrillation: The rhythm specificity of atrial low voltage substrateDeborah Nairn, Martin Eichenlaub, Heiko Lehrmann, et al.
Frontiers in Cardiovascular Medicine|March 17, 2022
Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart FailureGiorgio Luongo, Felix Rees, Deborah Nairn, et al.
Cardiovascular Digital Health Journal|April 26, 2021
Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECGGiorgio Luongo, Luca Azzolin, Steffen Schuler, et al.
Europace : European Pacing, Arrhythmias, and Cardiac Electrophysiology : Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology|January 19, 2022
Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogramGiorgio Luongo, Gaetano Vacanti, Vincent Nitzke, et al.
Europace : European Pacing, Arrhythmias, and Cardiac Electrophysiology : Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology|September 15, 2023
Differences in atrial substrate localization using late gadolinium enhancement-magnetic resonance imaging, electrogram voltage, and conduction velocity: a cohort study using a consistent anatomical reference frame in patients with persistent atrial fibrillationDeborah Nairn, Martin Eichenlaub, Björn Müller-Edenborn, et al.
Pageof 1