ArticleScientific reports2020
Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 36 citations in OpenAlex.
- Diagnostic Accuracy of Artificial Intelligence for Arrhythmia Detection Using the 12-Lead Electrocardiogram: A Systematic Review and Meta-Analysis.medRxiv : the preprint server for health sciences · 2026Article
- Deep learning for electrocardiogram interpretation: Bench to bedside.European journal of clinical investigation · 2025Review
- Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads.BMC medical research methodology · 2024Article
- Using domain adaptation for classification of healthy and disease conditions from mobile-captured images of standard 12-lead electrocardiograms.Scientific reports · 2023Article
- Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms.IEEE journal of translational engineering in health and medicine · 2023Article
- Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals.International journal of environmental research and public health · 2022Article
- Automated multilabel diagnosis on electrocardiographic images and signals.Nature communications · 2022Article
- Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing.Journal of healthcare engineering · 2022Article
- Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Standard 12-lead electrocardiography (ECG) is used as the primary clinical tool to diagnose changes in heart function. The value of automated 12-lead ECG diagnostic approaches lies in their ability to screen the general population and to provide a second opinion for doctors. Yet, the clinical utility of automated ECG interpretations remains limited. We introduce a two-way approach to an automated cardiac disease identification system using standard digital or image 12-lead ECG recordings. Two different network architectures, one trained using digital signals (CNN-dig) and one trained using images (CNN-ima), were generated. An open-source dataset of 41,830 classified standard ECG recordings from patients and volunteers was generated. CNN-ima was trained to identify atrial fibrillation (AF) using 12-lead ECG digital signals and images that were also transformed to mimic mobile device camera-acquired ECG plot snapshots. CNN-dig accurately (92.9-100%) identified every possible combination of the eight most-common cardiac conditions. Both CNN-dig and CNN-ima accurately (98%) detected AF from standard 12-lead ECG digital signals and images, respectively. Similar classification accuracy was achieved with images containing smartphone camera acquisition artifacts. Automated detection of cardiac conditions in standard digital or image 12-lead ECG signals is feasible and may improve current diagnostic methods.
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Registered trials
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