Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

Hannun, Awni Y.; Rajpurkar, Pranav; Haghpanahi, Masoumeh; Tison, Geoffrey H.; Bourn, Codie; Turakhia, Mintu P.; Ng, Andrew Y.

doi:10.1038/s41591-018-0268-3

Cited by 3,148ECG Monitoring and AnalysisCardiac electrophysiology and arrhythmiasNon-Invasive Vital Sign Monitoring

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

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Hannun, Awni Y., Rajpurkar, Pranav, Haghpanahi, Masoumeh, Tison, Geoffrey H., Bourn, Codie, Turakhia, Mintu P., Ng, Andrew Y.·2019·Nature Medicine

DOI 10.1038/s41591-018-0268-3PMC PMC6784839

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Abstract

Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow 1. Widely available digital ECG data and the algorithmic paradigm of deep learning 2 present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F 1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions.

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Federated learning has tremendous potential in healthcare, particularly towards addressing health disparities, under-served populations, and rare diseases, by enabling machine learning models to gain knowledge from ample and diverse data that would otherwise not be available.
“FL has tremendous potential in healthcare22,23, particularly towards addressing health disparities, under-served populations, and “rare” diseases24, by enabling ML models to gain knowledge from ample and diverse data that would otherwise not be available.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [23]
A DNN using single-lead ambulatory ECG data classified 12 rhythm classes with high diagnostic performance similar to, and perhaps exceeding, practicing cardiologists.
“A DNN that used single-lead, ambulatory ECG data was able to classify 12 rhythm classes with a high diagnostic performance similar to, and perhaps exceeding, practicing cardiologists57.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [57]

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Authors: Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y.
Affiliations: Stanford University (US)
iRhythm (United States) (US)
University of California, San Francisco (US)
VA Palo Alto Health Care System (US)
Corresponding author: Hannun, Awni Y.
ORCID: Hannun, Awni Y.
Rajpurkar, Pranav
Tison, Geoffrey H.
Turakhia, Mintu P.
Ng, Andrew Y.
Venue: Nature Medicine
Publisher: Springer Science and Business Media LLC
Published: 2019-01
Volume: 25
Issue: 1
Pages: 65-69
ISSN: 1078-8956 (ISSN-L)
1546-170X
DOI: 10.1038/s41591-018-0268-3
PMC: PMC6784839
OpenAlex: W2902644322
Cited by: 3,148
References: 49
Citations by year: 2026: 546 · 2025: 488 · 2024: 367 · 2023: 396 · 2022: 432 · 2021: 410 · 2020: 351 · 2019: 153 · 2018: 2
FWCI: 173.01
Type: journal article
Language: en
Update notices: correction (2019-01-24)
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Medicine (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: iRhythm Technologies
National Institutes of Health
National Heart, Lung, and Blood Institute (K23HL135274)
Topics: Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Keywords: arrhythmia detection, arrhythmia classification, Holter monitoring, single-lead ECG, deep neural network, automated ECG analysis, automated diagnosis
MeSH terms: Cardiologists, Algorithms, Arrhythmias, Cardiac, Electrocardiography, Humans, ROC Curve, Neural Networks, Computer, Monitoring, Ambulatory

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Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

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