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Peer-Reviewed Publication
Heart Rhythm2025;22(7):1667-1674.July 1, 2025Journal Article

Enhanced detection of atrial fibrillation in single-lead electrocardiograms using a Cloud-based artificial intelligence platform.

François De Guio1, Michiel Rienstra2, José María Lillo-Castellano3, Raquel Toribio-Fernández4, Carlos Lizcano4, Daniel Corrochano-Diego5, David Jimenez-Virumbrales5, Manuel Marina-Breysse6
1IDOVEN Research, Madrid, Spain. Electronic address: francois.deguio@idoven.ai.
2Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
3IDOVEN Research, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Myocardial Pathophysiology Area, Madrid, Spain.
4IDOVEN Research, Madrid, Spain.
5IDOVEN Research, Madrid, Spain; Hospital Universitario del Henares, Spain.
6IDOVEN Research, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Myocardial Pathophysiology Area, Madrid, Spain; Centro de Investigación Biomédica en Red. Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain. Electronic address: manuel.marina@idoven.ai.
PubMed PMC DOI PDF

Abstract

BACKGROUND: Although smartphone-based devices have been developed to record 1-lead electrocardiogram (ECG), existing solutions for automatic detection of atrial fibrillation (AF) often has poor positive predictive value. OBJECTIVE: This study aimed to validate a Cloud-based deep-learning platform for automatic AF detection in a large cohort of patients using 1-lead ECG records. METHODS: We analy…

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