ArticleCommunications medicine2025
Evaluating the feasibility of 12-lead electrocardiogram reconstruction from limited leads using deep learning.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- I-GraphECG: Observability-Based Lead Selection and Interpretable Disease Prediction from the 12-Lead ECG Using a Gray-Box Graph Electrophysiology Surrogate.Sensors (Basel, Switzerland) · 2026Article
- Wearable Electrocardiogram Technologies for the Early Detection of Acute Coronary Syndromes.JACC. Asia · 2026Review
- The Evolving Role of Artificial Intelligence and Machine Learning in the Wearable Electrocardiogram: A Primer on Wearable-Enabled Prediction of Cardiac Dysfunction.Bioengineering (Basel, Switzerland) · 2026Review
- Wearable device derived electrocardiographic age and its association with atrial fibrillation.NPJ digital medicine · 2026Article
- Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice.International journal of cardiology. Heart & vasculature · 2025Review
- Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention.Biomedicines · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWearables with integrated electrocardiogram (ECG) acquisition have made single-lead ECGs widely accessible to patients and consumers. However, the 12-lead ECG remains the gold standard for most clinical cardiac assessments. In this study, we developed a neural network to reconstruct 12-lead ECGs from single-lead and dual-lead ECGs, and evaluated the mathematical accuracy.
methodsWe used lead I or leads I and II from 9514 individuals from the Physikalisch-Technische Bundesanstalt (PTB-XL) cohort and a generative adversarial network, with the aim of recreating the missing leads from the 12-lead ECG. ECGs were divided into training, validation, and testing (10%). Original and recreated leads were measured with a commercially available algorithm. Differences in means and variances were assessed with Student's t-tests and F-tests, respectively. Calibration and bias were assessed with Bland-Altman plots. Inter-lead correlations were compared in original and recreated ECGs.
resultsThe variability of precordial ECG amplitudes is significantly reduced in recreated ECGs compared to real ECGs (all p < 0.05), indicating regression-to-the-mean. Amplitude averages are recreated with bias (p < 0.05 for most leads). Reconstruction errors depend on the real amplitudes, suggesting regression-to-the-mean (R
conclusionsAI-based 12-lead ECG reconstruction results in a regression-to-the-mean effect rather than personalized output, rendering it unsuitable for clinical use.
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Registered trials
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