Automatic diagnosis of the 12-lead ECG using a deep neural network
Open accessAbstract
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.
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Deep learning models analyze the entire ECG waveform and can detect subtle patterns distributed across multiple leads.
“Rather than focusing solely on established ECG features such as intervals, amplitudes, and segment morphology, deep learning models analyze the entire waveform and can detect subtle patterns distributed across multiple leads [9].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [9]
The Discussion says the end-to-end classifier takes the raw ECG signal and learns features rather than relying on features extracted by traditional methods. Methods > Neural network architecture and training says the input contains the sampled signal from each lead. The paper does not establish that the detected patterns are subtle or distributed across multiple leads. (LLM, conf=0.85)
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- Authors
- Ribeiro, A. H., Ribeiro, M. H., Paixão, G. M. M., Oliveira, D. M., Gomes, P. R., Canazart, J. A., Ferreira, M. P. S., Andersson, C. R., Macfarlane, P. W., Meira, W., Schön, T. B., & Ribeiro, A. L. P.
- Affiliations
- Universidade Federal de Minas Gerais (BR)
- Uppsala University (SE)
- University of Glasgow (GB)
- Corresponding author
- Ribeiro, Antônio H.
- Schön, Thomas B.
- Venue
- Nature Communications
- Published
- 2020-04-09
- Volume
- 11
- Issue
- 1
- Pages
- 1760-1760
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7145824
- OpenAlex
- W3015226328
- Cited by
- 894
- References
- 50
- Citations by year
- 2026: 235 · 2025: 207 · 2024: 148 · 2023: 113 · 2022: 98 · 2021: 78 · 2020: 14
- FWCI
- 46.87
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-05-01)
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- arXiv (Cornell University) (submittedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Ministry of Science, Technology and Innovation | Conselho Nacional de Desenvolvimento Científico e Tecnológico (465518/2014-1)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- 12-lead ECG, automated ECG analysis, deep neural network, telemedicine
- MeSH terms
- Deep Learning, Adolescent, Adult, Aged, Aged, 80 and over, Atrial Fibrillation, Cardiology, Electrocardiography, Humans, Middle Aged, Sensitivity and Specificity, Reproducibility of Results, Neural Networks, Computer, Young Adult
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