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Deep Learning and the Electrocardiogram: Review of the Current State-of-the-art by 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 is a Medicine article available to read on EtoBox.

What is Deep Learning and the Electrocardiogram: Review of the Current State-of-the-art about?

## Abstract 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.

Who reads Deep Learning and the Electrocardiogram: Review of the Current State-of-the-art?

Author

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

Publisher

Oxford University Press (OUP)

Published

2021

Language

EN

Field

Medicine (Health Sciences)