Near-term prediction of sustained ventricular arrhythmias applying artificial intelligence to single-lead ambulatory electrocardiogram
A novel deep learning model utilizing dynamic single-lead ambulatory ECGs accurately identifies patients at near-term risk of ventricular arrhythmias. It also uncovers an early depolarization pattern as a potential determinant of ventricular arrhythmias events.
Authors
- Éloi Marijon (ORCID: https://orcid.org/0000-0001-7227-3428)
- Kumar Narayanan (ORCID: https://orcid.org/0000-0003-4897-240X)
- Tanner Carbonati (ORCID: https://orcid.org/0000-0003-1727-1723)
- Jia Li (ORCID: https://orcid.org/0000-0003-4286-5953)
- Laurent Fiorina (ORCID: https://orcid.org/0000-0003-2502-3463)
- Jagmeet Singh (ORCID: https://orcid.org/0000-0001-9522-1188)
- Christine Henry
Institutions
- Inserm (FR)
- Université Paris Cité (FR)
- Massachusetts General Hospital (US)
- Hôpital Européen Georges-Pompidou (FR)
- Paris Cardiovascular Research Center (FR)
- Medicover (DE)
- Institut Cardiovasculaire Paris Sud (FR)
- Hôpital Privé Jacques Cartier (FR)
Publication Details
- Journal
- European Heart Journal
- Published
- 2025-03-30
- DOI
- https://doi.org/10.1093/eurheartj/ehaf073
- Citations
- 18
- Primary Topic
- Cardiac electrophysiology and arrhythmias
- Type
- article
- Field-Weighted Citation Impact
- 24.99