{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T09:09:48Z","timestamp":1784538588500,"version":"3.55.0"},"reference-count":27,"publisher":"Elsevier BV","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007497","name":"Biosense Webster","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007497","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["R 01HL174056-01"],"award-info":[{"award-number":["R 01HL174056-01"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["1R03NS145583"],"award-info":[{"award-number":["1R03NS145583"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000738","name":"US Department of Veterans Affairs","doi-asserted-by":"publisher","award":["5I01CX002758-02"],"award-info":[{"award-number":["5I01CX002758-02"]}],"id":[{"id":"10.13039\/100000738","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","heartrhythmopen.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Heart Rhythm O2"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.hroo.2026.02.009","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T08:08:46Z","timestamp":1775030926000},"page":"1361-1373","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting ventricular arrhythmia in myocardial ischemia using deep learning"],"prefix":"10.1016","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3577-460X","authenticated-orcid":false,"given":"Anna","family":"Busatto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jake A.","family":"Bergquist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tolga","family":"Tasdizen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin A.","family":"Steinberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravi","family":"Ranjan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rob S.","family":"MacLeod","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.hroo.2026.02.009_bib1","first-page":"39","article-title":"Management of premature ventricular complexes","volume":"107","author":"Chan","year":"2010","journal-title":"Mo Med"},{"key":"10.1016\/j.hroo.2026.02.009_bib2","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1161\/CIRCULATIONAHA.119.040015","article-title":"Premature ventricular contractions","volume":"140","author":"Gerstenfeld","year":"2019","journal-title":"Circulation"},{"key":"10.1016\/j.hroo.2026.02.009_bib3","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1093\/europace\/euac184","article-title":"Prognostic implication of premature ventricular contractions in patients without structural heart disease","volume":"25","author":"Scorza","year":"2023","journal-title":"Europace"},{"key":"10.1016\/j.hroo.2026.02.009_bib4","doi-asserted-by":"crossref","first-page":"i23","DOI":"10.1093\/eurheartjsupp\/suae006","article-title":"Unlocking the enigma: decoding premature ventricular complexes for effective clinical assessment and risk management","volume":"26","author":"Calo","year":"2024","journal-title":"Eur Heart J Suppl"},{"key":"10.1016\/j.hroo.2026.02.009_bib5","article-title":"Enhancing premature ventricular contraction localization through electrocardiographic imaging and cardiac digital twins","volume":"190","author":"S\u2019anchez","year":"2025","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.hroo.2026.02.009_bib6","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1038\/nrcardio.2010.52","article-title":"Early identification of risk factors for sudden cardiac death","volume":"7","author":"Chugh","year":"2010","journal-title":"Nat Rev Cardiol"},{"key":"10.1016\/j.hroo.2026.02.009_bib7","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1016\/S0140-6736(12)61413-5","article-title":"Ventricular arrhythmias and sudden cardiac death","volume":"380","author":"John","year":"2012","journal-title":"Lancet"},{"key":"10.1016\/j.hroo.2026.02.009_bib8","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.ccep.2017.08.004","article-title":"Ventricular arrhythmias and sudden cardiac death","volume":"9","author":"Tang","year":"2017","journal-title":"Card Electrophysiol Clin"},{"key":"10.1016\/j.hroo.2026.02.009_bib9","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1161\/CIRCRESAHA.120.317872","article-title":"Machine learning in arrhythmia and electrophysiology","volume":"128","author":"Trayanova","year":"2021","journal-title":"Circ Res"},{"key":"10.1016\/j.hroo.2026.02.009_bib10","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.hroo.2024.07.009","article-title":"Performance of off-the-shelf machine learning architectures and biases in low left ventricular ejection fraction detection","volume":"5","author":"Bergquist","year":"2024","journal-title":"Heart Rhythm O2"},{"key":"10.1016\/j.hroo.2026.02.009_bib11","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","article-title":"An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction","volume":"394","author":"Attia","year":"2019","journal-title":"Lancet"},{"key":"10.1016\/j.hroo.2026.02.009_bib12","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCEP.119.007988","article-title":"Assessing and mitigating bias in medical artificial intelligence: the effects of race and ethnicity on a deep learning model for ECG analysis","volume":"13","author":"Noseworthy","year":"2020","journal-title":"Circ Arrhythm Electrophysiol"},{"key":"10.1016\/j.hroo.2026.02.009_bib13","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.cvdhj.2022.09.001","article-title":"Emerging role of artificial intelligence in cardiac electrophysiology","volume":"3","author":"Kabra","year":"2022","journal-title":"Cardiovasc Digit Health J"},{"key":"10.1016\/j.hroo.2026.02.009_bib14","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1161\/CIRCULATIONAHA.121.057480","article-title":"ECG-based deep learning and clinical risk factors to predict atrial fibrillation","volume":"145","author":"Khurshid","year":"2022","journal-title":"Circulation"},{"key":"10.1016\/j.hroo.2026.02.009_bib15","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"10.1016\/j.hroo.2026.02.009_bib16","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.compbiomed.2018.03.016","article-title":"A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification","volume":"96","author":"Yildirim","year":"2018","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.hroo.2026.02.009_bib17","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.3390\/s19071502","article-title":"Detection of congestive heart failure based on LSTM-based deep network via short-term RR intervals","volume":"19","author":"Wang","year":"2019","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.hroo.2026.02.009_bib18","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.3390\/bioengineering11121290","article-title":"An intelligent approach for early and accurate predication of cardiac disease using hybrid artificial intelligence techniques","volume":"11","author":"Bilal","year":"2024","journal-title":"Bioengineering (Basel)"},{"key":"10.1016\/j.hroo.2026.02.009_bib19","doi-asserted-by":"crossref","first-page":"239","DOI":"10.3390\/diagnostics14030239","article-title":"An improved long short-term memory algorithm for cardiovascular disease prediction","volume":"14","author":"Revathi","year":"2024","journal-title":"Diagnostics"},{"key":"10.1016\/j.hroo.2026.02.009_bib20","article-title":"Prediction of ventricular tachycardia one hour before occurrence using artificial neural networks","volume":"6","author":"Lee","year":"2016","journal-title":"Sci Rep"},{"key":"10.1016\/j.hroo.2026.02.009_bib21","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-65357-x","article-title":"Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias","volume":"14","author":"Kolk","year":"2024","journal-title":"Sci Rep"},{"key":"10.1016\/j.hroo.2026.02.009_bib22","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1093\/eurheartj\/ehaf073","article-title":"Near-term prediction of sustained ventricular arrhythmias applying artificial intelligence to single-lead ambulatory electrocardiogram","volume":"46","author":"Fiorina","year":"2025","journal-title":"Eur Heart J"},{"key":"10.1016\/j.hroo.2026.02.009_bib23","article-title":"Novel experimental model for studying the spatiotemporal electrical signature of acute myocardial ischemia: a translational platform","volume":"41","author":"Zenger","year":"2020","journal-title":"J Physiol Meas"},{"key":"10.1016\/j.hroo.2026.02.009_bib24","series-title":"Comput Cardiol","first-page":"47","article-title":"High-capacity cardiac signal acquisition system for flexible, simultaneous, multidomain acquisition","volume":"2020","author":"Zenger","year":"2010"},{"key":"10.1016\/j.hroo.2026.02.009_bib25","doi-asserted-by":"crossref","first-page":"472","DOI":"10.21105\/joss.00472","article-title":"PFEIFER: preprocessing framework for electrograms intermittently fiducialized from experimental recordings","volume":"3","author":"Rodenhauser","year":"2018","journal-title":"J Open Source Softw"},{"key":"10.1016\/j.hroo.2026.02.009_bib26","unstructured":"Loshchilov I, Hutter F. Decoupled weight decay regularization. arXiv doi: 10.48550\/arXiv.1711.05101."},{"key":"10.1016\/j.hroo.2026.02.009_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.111178","article-title":"Reconstructing ventricular activation sequences from epicardial data: insights from Geodesic Back-Propagation optimization in porcine models","volume":"198","author":"Tanner","year":"2025","journal-title":"Comput Biol Med"}],"container-title":["Heart Rhythm O2"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666501826000644?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666501826000644?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T08:58:44Z","timestamp":1784537924000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666501826000644"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":27,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["S2666501826000644"],"URL":"https:\/\/doi.org\/10.1016\/j.hroo.2026.02.009","relation":{},"ISSN":["2666-5018"],"issn-type":[{"value":"2666-5018","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting ventricular arrhythmia in myocardial ischemia using deep learning","name":"articletitle","label":"Article Title"},{"value":"Heart Rhythm O2","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.hroo.2026.02.009","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Heart Rhythm Society. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}