Deep Learning Can Unmask Conduction Tissue Disease From an Ambulatory ECG
A deep learning-enabled ambulatory ECG is capable of unmasking underlying conduction tissue system disease. This tool may help identify patients with significant intermittent bradyarrhythmia, potentially improving timely diagnosis and management.
Authors
- Éloi Marijon (ORCID: https://orcid.org/0000-0001-7227-3428)
- Jagmeet P. Singh (ORCID: https://orcid.org/0000-0002-2724-529X)
- Kumar Narayanan (ORCID: https://orcid.org/0000-0003-4897-240X)
- Jean‐Claude Deharo (ORCID: https://orcid.org/0000-0002-1534-3845)
- Tanner Carbonati (ORCID: https://orcid.org/0000-0003-1727-1723)
- Baptiste Maille (ORCID: https://orcid.org/0000-0003-2639-9593)
- Laurent Fiorina (ORCID: https://orcid.org/0000-0003-2502-3463)
- Pauline Porquet
- Christine Henry (ORCID: https://orcid.org/0000-0002-9544-8021)
Institutions
- Université Paris-Sud (FR)
- Inserm (FR)
- Université Paris Cité (FR)
- Aix-Marseille Université (FR)
- CARE Hospitals (IN)
- Assistance Publique Hôpitaux de Marseille (FR)
- Massachusetts General Hospital (US)
- Sorbonne Paris Cité (FR)
- Hôpital Européen Georges-Pompidou (FR)
- Paris Cardiovascular Research Center (FR)
- MaxCure Hospitals (IN)
- Hôpital Privé Jacques Cartier (FR)
- Hôpital de la Timone (FR)
- GGG (France) (FR)
Publication Details
- Journal
- Circulation Arrhythmia and Electrophysiology
- Published
- 2025-08-26
- DOI
- https://doi.org/10.1161/circep.124.013695
- Citations
- 1
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.39