Dr.-Ing. Giorgio Luongo

  • Alumnus

Award

2022

Klee Prize for Giorgio Luongo
The German Society for Biomedical Engineering (DGBMT) annually awards the Klee Prize to scientists for practical developments in the field of medical technology. Giorgio Luongo was awarded 3rd place for his dissertation "Non-Invasive Atrial Arrhythmia Diagnosis Using the 12-Lead ECG: Machine Learning Leveraging in-silico and Clinical Signals" at IBT.
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Publications


  All Publications, sorted by years

          2022  2021  2020  2019  [All]


Selected Publications

Journal Articles (10)

G. Luongo, G. Vacanti, V. Nitzke, D. Nairn, C. Nagel, D. Kabiri, T. P. Almeida, D. C. Soriano, M. W. Rivolta, G. A. Ng, O. Dössel, A. Luik, R. Sassi, C. Schmitt, and A. Loewe.
Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram.
In EP Europace, vol. 24(7) , pp. 1186-1194, 2022
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G. Luongo, F. Rees, D. Nairn, M. W. Rivolta, O. Dössel, R. Sassi, C. Ahlgrim, L. Mayer, F.-J. Neumann, T. Arentz, A. Jadidi, A. Loewe, and B. Müller-Edenborn.
Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure.
In Frontiers in Cardiovascular Medicine, vol. 9, 2022
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G. Luongo, S. Schuler, A. Luik, T. P. Almeida, D. C. Soriano, O. Dossel, and A. Loewe.
Non-Invasive Characterization of Atrial Flutter Mechanisms Using Recurrence Quantification Analysis on the ECG: A Computational Study.
In IEEE Transactions on Biomedical Engineering, vol. 68(3) , pp. 914-925, 2021
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G. Luongo, L. Azzolin, S. Schuler, M. W. Rivolta, T. P. Almeida, J. P. Martínez, D. C. Soriano, A. Luik, B. Müller-Edenborn, A. Jadidi, O. Dössel, R. Sassi, P. Laguna, and A. Loewe.
Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG.
In Cardiovascular Digital Health Journal, vol. 2(2) , pp. 126-136, 2021
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D. Nairn, M. Eichenlaub, B. Müller-Edenborn, T. Huang, H. Lehrmann, C. Nagel, L. Azzolin, G. Luongo, R. M. Figueras Ventura, B. Rubio Forcada, A. Vallès Colomer, D. Westermann, T. Arentz, O. Dössel, A. Loewe, and A. Jadidi.
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 fibrillation.
In Europace, vol. 25(9) , 2023
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D. Nairn, M. Eichenlaub, H. Lehrmann, B. Müller‐Edenborn, J. Chen, T. Huang, C. Nagel, J. Sánchez, G. Luongo, D. Westermann, T. Arentz, O. Dössel, A. Jadidi, and A. Loewe.
Spatial correlation of left atrial low voltage substrate in sinus rhythm versus atrial fibrillation: The rhythm specificity of atrial low voltage substrate.
In Journal of Cardiovascular Electrophysiology, vol. 34(8) , pp. 1613-1621, 2023
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C. Nagel, G. Luongo, L. Azzolin, S. Schuler, O. Dössel, and A. Loewe.
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 Variability.
In Journal of Clinical Medicine, vol. 10(8) , pp. 1797, 2021
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J. Sánchez, G. Luongo, M. Nothstein, L. A. Unger, J. Saiz, B. Trenor, A. Luik, O. Dössel, and A. Loewe.
Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset.
In Frontiers in Physiology, vol. 12, pp. 699291, 2021
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M. Vila, M. W. Rivolta, G. Luongo, L. A. Unger, A. Luik, L. Gigli, F. Lombardi, A. Loewe, and R. Sassi.
Atrial Flutter Mechanism Detection Using Directed Network Mapping.
In Frontiers in Physiology, vol. 12, 2021
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O. Dössel, G. Luongo, C. Nagel, and A. Loewe.
Computer Modeling of the Heart for ECG Interpretation—A Review.
In Hearts, vol. 2(3) , pp. 350-368, 2021
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Book Chapters (1)

A. Loewe, G. Luongo, and J. Sánchez.
Machine Learning for Clinical Electrophysiology.
In Innovative Treatment Strategies for Clinical Electrophysiology, Springer Nature Singapore, Singapore, pp. 93-109, 2022
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Conference Contributions (10)

G. Luongo, S. Schuler, M. W. Rivolta, O. Dössel, R. Sassi, and A. Loewe.
Semi-Supervised vs. Supervised Learning for Discriminating Atrial Flutter Mechanisms Using the 12-lead ECG.
In Computing in Cardiology Conference (CinC), vol. 48, 2021
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G. Luongo, S. Schuler, M. W. Rivolta, O. Dössel, R. Sassi, and A. Loewe.
Automatic classification of 20 different types of atrial tachycardia using 12-lead ECG signals.
In EP Europace, vol. 22(Supplement_1) , 2020
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G. Luongo, S. Schuler, M. W. Rivolta, O. Dössel, R. Sassi, and A. Loewe.
Automatic ECG-based Discrimination of 20 Atrial Flutter Mechanisms: Influence of Atrial and Torso Geometries.
In 2020 Computing in Cardiology(9344051) , pp. 1-4, 2020
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G. Luongo, L. Azzolin, M. W. Rivolta, R. Sassi, J. P. Martinez, P. Laguna, O. Doessel, and A. Loewe.
Non-invasive identification of atrial fibrillation driver location using the 12-lead ECG: pulmonary vein rotors vs. other locations.
In EMBC 20, 2020
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G. Luongo, L. Azzolin, M. W. Rivolta, T. P. Almeida, J. P. Martinez, D. C. Soriano, O. Dössel, R. Sassi, P. Laguna, and A. Loewe.
Machine Learning to Find Areas of Rotors Sustaining Atrial Fibrillation From the ECG.
In Computing in Cardiology, 2020
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G. Luongo, S. Schuler, O. Dössel, and A. Loewe.
12-Lead ECG Feature Identification to Discriminate Different Types of Atrial Flutter.
In 41 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019
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G. Luongo, S. Schuler, T. P. Almeida, D. C. Soriano, O. Dössel, and A. Loewe.
Discrimination of Atrial Flutter on Simulated 12-Lead-ECG Signals by Applying Biosignal Processing.
In Gordon Research Conference - Cardiac Arrhythmia Mechanisms, 2019
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M. Vila, M. W. Rivolta, G. Luongo, A. Loewe, and R. Sassi.
Directed Network Mapping Hints the Ablation Strategy for Atrial Flutter: a Proof of Concept.
In 4th Atrial Signals Proceedings, pp. 16, 2021
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L. Azzolin, G. Luongo, S. Rocher, J. Saiz, O. Doessel, and A. Loewe.
Influence of Gradient and Smoothness of Atrial Wall Thickness on Initiation and Maintenance of Atrial Fibrillation.
In Computing in Cardiology Conference (CinC), 2020
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A. S. Bezerra, T. Yoneyama, D. C. Soriano, G. Luongo, X. Li, F. Ravelli, M. Mase, G. S. Chu, P. J. Stafford, F. S. Schlindwein, G. A. Ng, and T. P. Almeida.
Optimazing Atrial Electrogram Classification Based on Local Ablation Outcome in Human Atrial Fibrillation.
In Computing in Cardiology Conference (CinC), 2020
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