ArticleFrontiers in cardiovascular medicine2026
From one to twelve: feasibility and clinical utility of deep learning-derived 12-lead ECGs for remote cardiac monitoring.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Cardiovascular diseases (CVDs) remain the leading cause of global mortality. While the 12-lead electrocardiogram (ECG) is the clinical gold standard, wearable devices typically provide only single-lead monitoring (Lead I), which lacks the spatial resolution required to detect complex pathologies such as myocardial infarction or ST-segment changes. Objective: This study proposes a deep learning framework capable of reconstructing a full 12-lead ECG from a single-lead input while preserving diagnostic integrity. Methods: We developed TONet (Transformer-based Optimization Network), a multi-task framework evaluated on the PTB-XL dataset ( Results: TONet achieved an overall Pearson Correlation Coefficient (PCC) reaching 0.673 on the independent test set. Clinical agreement assessment via Bland-Altman analysis revealed a negligible per-lead bias (|Bias| ≤ 0.0024 mV across all 11 reconstructed leads), indicating no systematic voltage drift. In terms of diagnostic utility, the reconstructed 12-lead signals achieved a macro-averaged AUROC of 0.821 (95% CI: 0.810-0.831), with per-class AUROCs of 0.874 (NORM), 0.770 (MI), 0.862 (STTC), 0.782 (CD), and 0.815 (HYP). To assess generalisability beyond a single cohort, we additionally performed an independent in-distribution validation on the Chapman-Shaoxing 12-lead ECG database ( Conclusion: TONet demonstrates that a hybrid CNN-Transformer network with a multi-task reconstruction-plus-classification objective can recover morphologically faithful 12-lead surrogates from a single Lead I drawn from clinical PTB-XL recordings. We position this work as a regularized deep-learning framework for multi-lead reconstruction rather than a deployable clinical device: the reconstructed precordial leads are insufficient for fine-grained ST-elevation myocardial infarction (STEMI) screening, and external validation on true wearable recordings remains essential.
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