{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T01:16:16Z","timestamp":1788052576295,"version":"build-2784847793"},"reference-count":13,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004470","name":"Daewoong Pharmaceutical Company","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004470","id-type":"DOI","asserted-by":"publisher"}]}],"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>Persistent atrial fibrillation (AF) frequently coexists with heart failure (HF), yet HF monitoring remains limited by the need for repeated blood-based biomarkers such as N-terminal pro-brain natriuretic peptide (NT-proBNP). Advances in wearable electrocardiography (ECG) and artificial intelligence (AI) now allow continuous extraction of digital physiologic signatures that may reflect hemodynamic stress.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the feasibility of predicting HF progression using wearable ECG\u2013derived features in patients with persistent AF.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Fifty patients with persistent AF underwent 3\u20137 days of single-lead ECG monitoring. Heart rate variability (HRV) and RR-interval features from 30\u2005min windows were combined with baseline clinical metrics. A context-aware deep learning model using long short-term memory (LSTM) and attention mechanisms was trained to predict 6\u201312-month NT-proBNP changes. Model performance was assessed using root mean squared error (RMSE), mean absolute error (MAE), and the accuracy of directional NT-proBNP change.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The best performance was achieved when clinical metrics, RR features, and long-term HRV summaries were combined (RMSE 1,667.04; MAE 950.52). Directional classification of NT-proBNP trajectories achieved an accuracy of 0.82. ECG-only models performed comparably to multimodal models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Wearable ECG\u2013based AI modeling is feasible for predicting trends in HF biomarkers in persistent AF. These results provide early evidence that ECG-derived digital biomarkers may offer a scalable, non-invasive approach for longitudinal HF monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2025.1748673","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T13:11:56Z","timestamp":1770297116000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-based prediction of heart failure progression in persistent atrial fibrillation using wearable electrocardiography: a brief research report"],"prefix":"10.3389","volume":"12","author":[{"given":"Chae-Bin","family":"Song","sequence":"first","affiliation":[{"name":"Department of Technology Development, Seers Technology Company Ltd.","place":["Pyeongtaek, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun Kwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Technology Development, Seers Technology Company Ltd.","place":["Pyeongtaek, Republic of Korea"]},{"name":"Department of Brain and Cognitive Engineering, Korea University","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YouMi","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Cardiology, College of Medicine , St. Vincent\u2019s Hospital, The Catholic University of Korea","place":["Suwon, Republic of Korea"]},{"name":"College of Medicine, Catholic Research Institute for Intractable Cardiovascular Disease (CRID), The Catholic University of Korea","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2920","DOI":"10.1161\/01.CIR.0000072767.89944.6E","article-title":"Temporal relations of atrial fibrillation and congestive heart failure and 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