A Hybrid Model Combining 1D-CNN and BERT for Intelligent ECG Arrhythmia Classification

A Hybrid Model Combining 1D-CNN and BERT for Intelligent ECG Arrhythmia Classification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Hybrid Model Combining 1D-CNN and BERT for Intelligent ECG Arrhythmia Classification Hanqing Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7310321/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Arrhythmia is a common cardiovascular disease, whose early diagnosis is crucial to prevent severe cardiac events. Traditional electrocardiogram (ECG) interpretation methods rely on manual analysis, which often suffers from low efficiency and limited accuracy. To address these issues, intelligent algorithms are increasingly being used for automatic arrhythmia recognition. However, many existing methods still face challenges in achieving accurate classification. In this paper, we propose a novel approach that integrates a one-dimensional convolutional neural network (1D-CNN) with Bidirectional Encoder Representations from Transformers (BERT) for arrhythmia classification. The proposed model, named ECGBert, leverages the local feature extraction capability of CNN and the global context modeling strength of BERT. The model enables the precise classification of different types of arrhythmias by performing signal preprocessing, segment encoding, and sequential feature extraction. Experimental results in the MIT-BIH Arrhythmia Database demonstrate that ECGBert significantly outperforms traditional methods and existing hybrid architectures in multiple evaluation metrics. The model effectively captures long-range dependencies between abnormal heartbeats by incorporating the Transformer mechanism. It also maintains an end-to-end learning structure without the need for hand-crafted features, offering strong generalization ability and robustness. This work provides a new methodological framework for intelligent ECG analysis and promotes the innovative application of deep learning in medical signal processing. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Sep, 2025 Reviews received at journal 16 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers invited by journal 19 Aug, 2025 Editor assigned by journal 14 Aug, 2025 Editor invited by journal 14 Aug, 2025 Submission checks completed at journal 12 Aug, 2025 First submitted to journal 12 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7310321","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":505133611,"identity":"4945a08c-c5bb-4f95-8d86-803f7091ee50","order_by":0,"name":"Hanqing Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACAwbmhgOM//7LgTgHHhCnhbHhAAMbszFYSwKxWhiAWhIbQDyitJizJzYe5uFhS58fdvgh0BY7Od0GAlosex42HOaR4MndeDvNAKgl2djsACGH3UgEajGQyN04OwGk5UDiNuK0JBikG85O/0CKlgMJCfLSOcTacuZhw8G5DQcMN0jnFBxIMCDGL8eTD39423BAXn52+uYPHyrs5AhqgceFAVilAUHlSFrkG4hSPQpGwSgYBSMRAACHtUvJWAuJgwAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Hanqing","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-08-06 13:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7310321/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7310321/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-28023-4","type":"published","date":"2025-11-21T15:58:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96650141,"identity":"a2add91e-2121-4461-aabb-8facd00d2a98","added_by":"auto","created_at":"2025-11-24 16:08:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":349669,"visible":true,"origin":"","legend":"","description":"","filename":"AHybridModelCombining1DCNNandBERTforIntelligentECGArrhythmiaClassification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7310321/v1_covered_cfac7229-e611-4ce6-b946-fcf5ae8cd713.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Hybrid Model Combining 1D-CNN and BERT for Intelligent ECG Arrhythmia Classification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7310321/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7310321/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Arrhythmia is a common cardiovascular disease, whose early diagnosis is crucial to prevent severe cardiac events. Traditional electrocardiogram (ECG) interpretation methods rely on manual analysis, which often suffers from low efficiency and limited accuracy. To address these issues, intelligent algorithms are increasingly being used for automatic arrhythmia recognition. However, many existing methods still face challenges in achieving accurate classification. In this paper, we propose a novel approach that integrates a one-dimensional convolutional neural network (1D-CNN) with Bidirectional Encoder Representations from Transformers (BERT) for arrhythmia classification. The proposed model, named ECGBert, leverages the local feature extraction capability of CNN and the global context modeling strength of BERT. The model enables the precise classification of different types of arrhythmias by performing signal preprocessing, segment encoding, and sequential feature extraction. 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