Detection of Ectopic Beat from Electrocardiography with Deep Convolutional Neural Network

Detection of Ectopic Beat from Electrocardiography with Deep Convolutional Neural Network | 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 Detection of Ectopic Beat from Electrocardiography with Deep Convolutional Neural Network Bilal Shaikh, Ana Zafar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4837328/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 Electrocardiogram (ECG) is a crucial tool for identifying cardiovascular diseases. However, manual evaluation of ECG signals can be tedious and time-consuming, especially when dealing with a large number of cardiac patients. To address this challenge, this study presents a model that categorizes ECG signals into three distinct classes based on their morphological characteristics. Our approach utilizes non-linear features extracted through a convolutional neural network (CNN). The proposed 1D-CNN model architecture comprises three convolutional layers, max pooling layers, and dense layers. This structure automatically extracts distinctive non-linear features from ECG signals and classifies them into five categories: Normal (Normal Beat), Supraventricular ectopic beats and Ventricular ectopic beats. We evaluated our algorithm using the open-source MIT-BIH database and 5-fold cross-validation. The model achieved an accuracy of 97% and an F1 score of 99%. Artificial Intelligence and Machine Learning Biomedical Engineering Electrocardiogram classification ectopic beats 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-4837328","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334386662,"identity":"873fa9e2-0a27-4639-bbd9-64f761de7125","order_by":0,"name":"Bilal Shaikh","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bilal","middleName":"","lastName":"Shaikh","suffix":""},{"id":334386663,"identity":"22b703c4-a03f-4cd0-ae0b-50ecd3a44130","order_by":1,"name":"Ana Zafar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIie3PsWrDMBCA4ROGeAlkVZf4Fc50yFBCXuVEoF4ydPQQSotBU+gsSMgzuEugm41AXfwAgnaQ36BQKBpKqcdCcZxsheqbDnE/4gCC4O+aAkSRdpQD4InJJUAsr9E1ZyXjBi9aeUIy2RaO+TUmM0WIYq+TWVxU4PNDb8JfDQI3mO4skRMHnT5tDLFN89L/jSUEHH0yxanCLmGlXWHEZH+S2OwN6AsXios7LnZ6MZhgtwC1RKHGGrpKi8Ektaub+v4BlyqWIySTLcvGUH3slqnNHlv/gXMVTd5bv76al89F7Xx+5PxO9WMufr0Muj1nOQiC4J/4BgWBX9SGBXEUAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Zafar","suffix":""}],"badges":[],"createdAt":"2024-07-31 17:27:35","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-4837328/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4837328/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61615441,"identity":"116034ed-2832-4d40-912d-7d1c0acc02e2","added_by":"auto","created_at":"2024-08-02 03:06:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":196187,"visible":true,"origin":"","legend":"","description":"","filename":"DetectionofEctopicBeatfromElectrocardiographywithDeepConvolutionalNeuralNetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4837328/v1_covered_477a06a2-abec-4914-8645-fc53f4eebb9d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDetection of Ectopic Beat from Electrocardiography with Deep Convolutional Neural Network\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Indian Institutes of Technology","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":"Electrocardiogram, classification, ectopic beats","lastPublishedDoi":"10.21203/rs.3.rs-4837328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4837328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectrocardiogram (ECG) is a crucial tool for identifying cardiovascular diseases. 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