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Peer-Reviewed Publication
Eur Heart J2025;46(21):1998-2008.June 2, 2025Journal Article

Near-term prediction of sustained ventricular arrhythmias applying artificial intelligence to single-lead ambulatory electrocardiogram.

Laurent Fiorina1,2, Tanner Carbonati3, Kumar Narayanan2,4, Jia Li3, Christine Henry3, Jagmeet P Singh5, Eloi Marijon2,6
1Ramsay Santé, Institut Cardiovasculaire Paris Sud, Hôpital privé Jacques Cartier, Massy 91300, France.
2Université Paris Cité, PARCC, INSERM U970, 56 Rue Leblanc, Paris 75015, France.
3Cardiologs, 136 rue Saint Denis, Paris 75002, France.
4Department of Cardiology, Medicover Hospitals, Hyderabad, India.
5Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
6Division of Cardiology, European Georges Pompidou Hospital, 20-40 Rue Leblanc, Paris 75908, France.
PubMed PMC DOI PDF

Abstract

BACKGROUND AND AIMS: Accurate near-term prediction of life-threatening ventricular arrhythmias would enable pre-emptive actions to prevent sudden cardiac arrest/death. A deep learning-enabled single-lead ambulatory electrocardiogram (ECG) may identify an ECG profile of individuals at imminent risk of sustained ventricular tachycardia (VT). METHODS: This retrospective study included 247 254, 14 da…

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