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

Journal: European heart journal
Published Date: Jun 2, 2025

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).

Authors

  • Laurent Fiorina
    Institut Cardiovasculaire Paris-Sud, Hôpital Privé Jacques Cartier, Ramsay, Massy, France.
  • Tanner Carbonati
    Tempus Labs, Inc., Chicago, IL, USA.
  • Kumar Narayanan
    Université de Paris, PARCC, INSERM, F-75015 Paris, France.
  • Jia Li
    Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan Tsuihang New District, Guangdong, 528400, PR China; School of Pharmacy, Zunyi Medical University, Zunyi, 563000, PR China; National Center for Drug Screening, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, PR China.
  • Christine Henry
    Cardiologs, 136 rue Saint Denis, Paris 75002, France.
  • Jagmeet P Singh
    Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Eloi Marijon
    Université de Paris, PARCC, INSERM, F-75015 Paris, France.

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