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Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG
BACKGROUND: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053175/ https://www.ncbi.nlm.nih.gov/pubmed/33899043 http://dx.doi.org/10.1016/j.cvdhj.2021.03.002 |
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author | Luongo, Giorgio Azzolin, Luca Schuler, Steffen Rivolta, Massimo W. Almeida, Tiago P. Martínez, Juan P. Soriano, Diogo C. Luik, Armin Müller-Edenborn, Björn Jadidi, Amir Dössel, Olaf Sassi, Roberto Laguna, Pablo Loewe, Axel |
author_facet | Luongo, Giorgio Azzolin, Luca Schuler, Steffen Rivolta, Massimo W. Almeida, Tiago P. Martínez, Juan P. Soriano, Diogo C. Luik, Armin Müller-Edenborn, Björn Jadidi, Amir Dössel, Olaf Sassi, Roberto Laguna, Pablo Loewe, Axel |
author_sort | Luongo, Giorgio |
collection | PubMed |
description | BACKGROUND: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. OBJECTIVES: To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. METHODS: AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). RESULTS: The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. CONCLUSION: Machine learning–based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI. |
format | Online Article Text |
id | pubmed-8053175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-80531752021-04-21 Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG Luongo, Giorgio Azzolin, Luca Schuler, Steffen Rivolta, Massimo W. Almeida, Tiago P. Martínez, Juan P. Soriano, Diogo C. Luik, Armin Müller-Edenborn, Björn Jadidi, Amir Dössel, Olaf Sassi, Roberto Laguna, Pablo Loewe, Axel Cardiovasc Digit Health J Clinical BACKGROUND: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. OBJECTIVES: To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. METHODS: AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). RESULTS: The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. CONCLUSION: Machine learning–based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI. Elsevier 2021-03-19 /pmc/articles/PMC8053175/ /pubmed/33899043 http://dx.doi.org/10.1016/j.cvdhj.2021.03.002 Text en © 2021 Heart Rhythm Society. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Clinical Luongo, Giorgio Azzolin, Luca Schuler, Steffen Rivolta, Massimo W. Almeida, Tiago P. Martínez, Juan P. Soriano, Diogo C. Luik, Armin Müller-Edenborn, Björn Jadidi, Amir Dössel, Olaf Sassi, Roberto Laguna, Pablo Loewe, Axel Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG |
title | Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG |
title_full | Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG |
title_fullStr | Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG |
title_full_unstemmed | Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG |
title_short | Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG |
title_sort | machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ecg |
topic | Clinical |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053175/ https://www.ncbi.nlm.nih.gov/pubmed/33899043 http://dx.doi.org/10.1016/j.cvdhj.2021.03.002 |
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