{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T15:13:27Z","timestamp":1766416407974,"version":"3.48.0"},"posted":{"date-parts":[[2025,12,19]]},"group-title":"Cardiovascular Medicine","reference-count":35,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T00:00:00Z","timestamp":1766102400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,12,19]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Despite its broadening indications, the implantable cardiac monitor (ICM) records a narrow, nonstandard electrocardiogram (ECG) signal which precludes morphological and functional assessments or the application of 12-lead ECG models. We hypothesize that deep learning can be used to reconstruct 12-lead ECG from a single ICM lead for continuously assessing clinical endpoints outside of rhythm detection alone.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>To reconstruct 12-lead ECG from a single ICM lead to detect conduction, repolarization, rhythm, and cardiac functional changes in a large, diverse patient population.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We annotated 75,450 echocardiogram-ECG pairs with five disease labels a) right bundle branch block, b) left bundle branch block, c) atrial fibrillation, d) QT-prolongation and e) low left ventricular ejection fraction (LVEF) using regex-based parsing of clinician interpretations. We used perceptual loss to train a deep U-Net (ECG12-PerceptNet) to reconstruct 12-lead ECG from a simulated ICM signal. We compared the classification performance of the reconstructed 12-lead ECG against the original 12-lead and single lead ECG in an internal and external test set. Furthermore, we trained a regression model to predict the absolute LVEF using original and reconstructed 12-lead ECGs.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The reconstructed ECG approached the original 12-lead ECG in classification performance across all endpoints while significantly outperforming the single lead ECG. We show two case studies where sequential LVEF measurements were tracked using LVEF predicted with the original and reconstructed 12-lead ECG.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>In this paper, we report the ECG12-PerceptNet which reconstructs 12-lead ECG from a simulated ICM signal. This can enable continuous in-home or ambulatory monitoring of cardiac functional changes, potentially reducing hospitalizations and out-of-hospital cardiac arrest.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2025.12.17.25342224","type":"posted-content","created":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T02:10:17Z","timestamp":1766196617000},"source":"Crossref","is-referenced-by-count":0,"title":["Long-term, ambulatory 12-lead ECG from a single non-standard lead using perceptual reconstruction"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4825-646X","authenticated-orcid":false,"given":"Sabyasachi","family":"Bandyopadhyay","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"I-Min","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rayan","family":"Ansari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. Weston","family":"Hughes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander C.","family":"Perino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neal K.","family":"Bhatia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3813-7518","authenticated-orcid":false,"given":"David","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Euan A.","family":"Ashley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco V.","family":"Perez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjiv M.","family":"Narayan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Albert J.","family":"Rogers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025122207100580000_2025.12.17.25342224v1.1","first-page":"530352","article-title":"ECG-Based Detection of Early Myocardial Ischemia in a Computational Model: Impact of Additional Electrodes, Optimal Placement, and a New Feature for ST Deviation","volume":"2015","year":"2015","journal-title":"Biomed Res Int"},{"key":"2025122207100580000_2025.12.17.25342224v1.2","doi-asserted-by":"crossref","unstructured":"Sun, Q. , et al. Early detection of myocardial ischemia in 12-lead ECG using deterministic learning and ensemble learning. 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