{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T11:18:42Z","timestamp":1788002322952,"version":"build-2784847793"},"reference-count":11,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","heartrhythmjournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Heart Rhythm"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.hrthm.2026.07.045","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T23:20:33Z","timestamp":1785885633000},"update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sampling frequency has architecture-dependent effects on electrocardiogram deep learning classification: Convolutional neural networks are robust, whereas transformers benefit modestly"],"prefix":"10.1016","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8110-9498","authenticated-orcid":false,"given":"Cole","family":"Freedman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syed Zawahir","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximo","family":"Alvarez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Elhajj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun Kyeong","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chanhyun","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sameer","family":"Hirji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua C.","family":"Grimm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saraschandra","family":"Vallabhajosyula","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Nanna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexis","family":"Okoh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ozan","family":"Unlu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Pantlin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnold","family":"Fenrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauricio","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.hrthm.2026.07.045_bib1","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41597-020-0495-6","article-title":"PTB-XL: a large publicly available ECG dataset","volume":"7","author":"Wagner","year":"2020","journal-title":"Sci Data"},{"key":"10.1016\/j.hrthm.2026.07.045_bib2","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"10.1016\/j.hrthm.2026.07.045_bib4","doi-asserted-by":"crossref","DOI":"10.2196\/44003","article-title":"Electrocardiogram devices for home use: technological and clinical scoping review","volume":"7","author":"Zepeda-Echavarria","year":"2023","journal-title":"JMIR Cardio"},{"key":"10.1016\/j.hrthm.2026.07.045_bib5","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1136\/hrt.2004.057307","article-title":"The normal ECG in childhood and adolescence","volume":"91","author":"Dickinson","year":"2005","journal-title":"Heart"},{"key":"10.1016\/j.hrthm.2026.07.045_bib6","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.3390\/s25102982","article-title":"Are wearable ECG devices ready for hospital at home application?","volume":"25","author":"Medina-Avelino","year":"2025","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.hrthm.2026.07.045_bib7","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/j.jelectrocard.2015.08.034","article-title":"Frequency content and characteristics of ventricular conduction","volume":"48","author":"Tereshchenko","year":"2015","journal-title":"J Electrocardiol"},{"key":"10.1016\/j.hrthm.2026.07.045_bib8","doi-asserted-by":"crossref","DOI":"10.2196\/38454","article-title":"State-of-the-art deep learning methods on electrocardiogram data: systematic review","volume":"10","author":"Petmezas","year":"2022","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.hrthm.2026.07.045_bib9","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/s13239-025-00797-8","article-title":"Assessing the impact of downsampled ECGs and alternative loss functions in multi-label classification of 12-lead ECGs","volume":"16","author":"Singstad","year":"2025","journal-title":"Cardiovasc Eng Technol"},{"key":"10.1016\/j.hrthm.2026.07.045_bib10","doi-asserted-by":"crossref","DOI":"10.3389\/fmed.2025.1600855","article-title":"Candelaria Chio Montero T. Transformer-based ECG classification for early detection of cardiac arrhythmias","volume":"12","author":"Ikram","year":"2025","journal-title":"Front Med"},{"key":"10.1016\/j.hrthm.2026.07.045_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106211","article-title":"CAT-Net: convolution, attention, and transformer based network for single-lead ECG arrhythmia classification","volume":"93","author":"Islam","year":"2024","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.hrthm.2026.07.045_bib12","doi-asserted-by":"crossref","first-page":"146","DOI":"10.15420\/aer.2020.26","article-title":"Big data and artificial intelligence: opportunities and threats in electrophysiology","volume":"9","author":"de Leur","year":"2020","journal-title":"Arrhythm Electrophysiol Rev"}],"container-title":["Heart Rhythm"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1547527126026573?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1547527126026573?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T11:06:40Z","timestamp":1788001600000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1547527126026573"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":11,"alternative-id":["S1547527126026573"],"URL":"https:\/\/doi.org\/10.1016\/j.hrthm.2026.07.045","relation":{},"ISSN":["1547-5271"],"issn-type":[{"value":"1547-5271","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Sampling frequency has architecture-dependent effects on electrocardiogram deep learning classification: Convolutional neural networks are robust, whereas transformers benefit modestly","name":"articletitle","label":"Article Title"},{"value":"Heart Rhythm","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.hrthm.2026.07.045","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Heart Rhythm Society. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}