Deep learning and the electrocardiogram: review of the current state-of-the-art

DOI PDF 3 Citations
  • Sulaiman Somani
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
  • Adam J Russak
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
  • Felix Richter
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
  • Shan Zhao
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
  • Akhil Vaid
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
  • Fayzan Chaudhry
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
  • Jessica K De Freitas
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
  • Nidhi Naik
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
  • Riccardo Miotto
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
  • Girish N Nadkarni
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
  • Jagat Narula
    Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Edgar Argulian
    Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Benjamin S Glicksberg
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA

Description

<jats:title>Abstract</jats:title> <jats:p>In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.</jats:p>

Journal

  • EP Europace

    EP Europace 23 (8), 1179-1191, 2021-02-10

    Oxford University Press (OUP)

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