ECG-based Cardiovascular Disease Diagnosis: An Ensemble Learning Approach

ECG-based Cardiovascular Disease Diagnosis: An Ensemble Learning Approach | 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 Research Article ECG-based Cardiovascular Disease Diagnosis: An Ensemble Learning Approach Amaz Gupta, Baru Sharadat, Thomas Huddle This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4747810/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The classification and identification of arrhythmias using ECG signals are of substantial practical importance in the early prevention and detection of cardiac and cardiovascular disorders. We propose an ensemble learning model to leverage the power of convolutional neural networks and transfer learning techniques to analyze heartbeat data. We transfer CNN models on 1D heartbeat signals and employ ensemble techniques to aggregate their predictions Our results underscore the improved accuracy of our approach in classifying ECG data, demonstrating its potential for early cardiovascular disease detection. Our method addresses the limitations of traditional ECG interpretation and offers a robust, automated approach to arrhythmia detection and classification, potentially revolutionizing the field of cardiac diagnostics. This research showcases the significance of transfer learning and ensemble learning techniques in advancing healthcare applications. Artificial Intelligence and Machine Learning Biomedical Engineering Cardiovascular diseases ECG classification CNN Transfer learning Ensemble Learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4747810","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327554265,"identity":"8da2c70b-5730-4f8f-a44f-fba8ce5c61ab","order_by":0,"name":"Amaz Gupta","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Amaz","middleName":"","lastName":"Gupta","suffix":""},{"id":327554775,"identity":"c009dbf3-0098-4774-95ee-7a633da9791d","order_by":1,"name":"Baru Sharadat","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Baru","middleName":"","lastName":"Sharadat","suffix":""},{"id":327554776,"identity":"0508919e-39b8-4f69-9387-d164ab42f368","order_by":2,"name":"Thomas Huddle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYJACZiBi7GNgPgBkWzAw8BBQzgPT0sbAlsDAkCBBkhYeA+K02LN3JzAXtlnLtrH3fHzM+0NCjp/nAOOHjzl4bOE5u4F5Zlu6cRvP2c3GPAkSxpK9DcySM7fh0SKRu4GZt+1wYptE7jZpoJbEDecZ2Jh5idIi/+YZqVokeNggWs42ENBy5uyGwzznQH5JMzackwb0S8/BZrx+YW/v3fiYp8xatp/98MMHb2xsgCGWfPDDRzxaQOAAGp+xAb/6UTAKRsEoGAUEAQAwrUclLF2TdgAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Huddle","suffix":""}],"badges":[],"createdAt":"2024-07-16 07:24:17","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4747810/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4747810/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60544222,"identity":"d4475fc1-9511-49c4-b04d-8e3f2ae8d4b7","added_by":"auto","created_at":"2024-07-18 03:19:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":316608,"visible":true,"origin":"","legend":"","description":"","filename":"ECGbasedCardiovascularDiseaseDiagnosisAnEnsembleLearningApproach.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4747810/v1_covered_1e6e98a8-b437-4b52-923d-ce9284e95425.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eECG-based Cardiovascular Disease Diagnosis: An Ensemble Learning Approach\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular diseases, ECG classification, CNN, Transfer learning, Ensemble Learning","lastPublishedDoi":"10.21203/rs.3.rs-4747810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4747810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe classification and identification of arrhythmias using ECG signals are of substantial practical importance in the early prevention and detection of cardiac and cardiovascular disorders. 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