{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T17:36:33Z","timestamp":1791826593539,"version":"4.3.4"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study proposes a deep learning framework capable of reconstructing a full 12-lead ECG from a single-lead input while preserving diagnostic integrity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We developed TONet (Transformer-based Optimization Network), a multi-task framework evaluated on the PTB-XL dataset (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200921,837). The architecture integrates a Convolutional Neural Network (CNN) encoder to extract local morphological features and a Transformer encoder to capture global temporal dependencies across the cardiac cycle. To ensure clinical validity, the model was trained using a dual-objective loss function, simultaneously optimizing for signal reconstruction (MSE) and multi-label diagnostic classification (BCE) across five diagnostic super-classes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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|\u2009\u2264\u20090.0024\u2005mV 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\u20130.831), 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 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200943,559 quality-controlled recordings), where TONet, retrained from scratch using the identical protocol, achieved an overall PCC of 0.612 (95% CI: 0.552\u20130.666) and substantially outperformed the linear-regression baseline (PCC\u2009=\u20090.498), confirming that the reconstruction benefit transfers to a second, hardware- and population-distinct cohort.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>TONet demonstrates that a hybrid CNN\u2013Transformer 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1856637","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T05:43:41Z","timestamp":1780983821000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["From one to twelve: feasibility and clinical utility of deep learning-derived 12-lead ECGs for remote cardiac monitoring"],"prefix":"10.3389","volume":"13","author":[{"given":"Haoyang","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Neurology, The First Medical Centre, Chinese PLA General Hospital","place":["Beijing, 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