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

Sulaiman Somani, Adam J. Russak, Felix Richter, Shan Zhao, Akhil Vaid, Fayzan Chaudhry, Jessica K. De Freitas, Nidhi Naik, Riccardo Miotto, Girish N. Nadkarni, Jagat Narula, Edgar Argulian, Benjamin S. Glicksberg

Research output: Contribution to journalReview articlepeer-review

120 Scopus citations


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.

Original languageEnglish
Pages (from-to)1179-1191
Number of pages13
Issue number8
StatePublished - 1 Aug 2021


  • Artificial intelligence
  • Big data
  • Cardiovascular medicine
  • Deep learning
  • Electrocardiogram


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