R-Wave Detection From ECG Signal with K-Mean Clustering

R-Wave Detection From ECG Signal with K-Mean Clustering | 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 R-Wave Detection From ECG Signal with K-Mean Clustering Mogu Shrakana, Miki Yuuki, Shao Zhang, Felong Li, Edward Ben This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4848578/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 Electrocardiograph (ECG) R-wave detection from abdominal ECGs presents significant challenges due to signal distortion and interference. This study introduces a novel approach using K-Mean Clustering technique to detect ECG R waves from ECG signal. Leveraging the quad periodicity of R waves, we developed an algorithm to identify and correct misaligned and missed R wave detection in the RR time series. Our detection process comprises two stages. Initially, we detect ECG R waves that do not overlap with QRS complexes. The algorithm then corrects misaligned and missed R waves while predicting approximate regions where ECG R waves overlap with QRS. In the second stage, we detect overlapping ECG R waves within these predicted regions. We construct QRS complex from non-overlapping detected complexes using K-Mean Clustering. The algorithm then precisely locates overlapping ECG R waves by finding the optimal correlation between the actual QRS and the superposition of QRS within the predicted regions. The results suggest that our method significantly enhances the accurate extraction of ECG R waves, making it potentially valuable for both clinical and commercial applications in monitoring. Computational Biology Artificial Intelligence and Machine Learning electrocardiogram machine learning decision support system 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. 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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-4848578","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335234653,"identity":"f098d6d1-999f-4d5d-8a77-81dcdda17628","order_by":0,"name":"Mogu Shrakana","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mogu","middleName":"","lastName":"Shrakana","suffix":""},{"id":335234654,"identity":"a9191c3d-9c32-4d63-91d3-a0d93c435c15","order_by":1,"name":"Miki Yuuki","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Miki","middleName":"","lastName":"Yuuki","suffix":""},{"id":335234655,"identity":"7ee1dc4c-4072-4a28-b703-33933102e0a7","order_by":2,"name":"Shao Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shao","middleName":"","lastName":"Zhang","suffix":""},{"id":335234656,"identity":"6d7dfaf3-85b0-47f9-8909-446a1bde658c","order_by":3,"name":"Felong Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Felong","middleName":"","lastName":"Li","suffix":""},{"id":335234657,"identity":"590812b8-b6f5-4af6-a193-5b9caacd70f4","order_by":4,"name":"Edward Ben","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie3QMQrCMBSA4RcK6dKS9ZWCZ0jp4lDwKpGCkwdw0oIQFz1AwUMIgjgqQqcU125WBCcHu+kgGHG21s0h/xDyAt/wAmAy/WWC6gNbFHYbfWkDOM1IO2QkE3rAxmTQTceKNyPMFrSs1kgWmapO7g1bzNmQa9X/TLxpaQepQourfBk6AkNvllheuvpMeCGo70qkvMhXvibdxR6o5daQzos8JDr8cDk3Ixw1IRLRSxR9kzypJ6iOk2AmkTPIwmDe07tMt+PaXdgkzsq7HI4k7I7lJYr0j8Xba1VDAEjy/cVkMplMP/YEDodN+XoXsswAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Edward","middleName":"","lastName":"Ben","suffix":""}],"badges":[],"createdAt":"2024-08-02 12:54:18","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-4848578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4848578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61736486,"identity":"213e63da-8d87-49a3-a6be-2d6f7d753ffc","added_by":"auto","created_at":"2024-08-05 03:26:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":311235,"visible":true,"origin":"","legend":"","description":"","filename":"RWaveDetectionFromECGSignalwithKMeanClustering.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4848578/v1_covered_eb89f2d7-1806-4090-a18e-ef79d85bf0c8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eR-Wave Detection From ECG Signal with K-Mean Clustering\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":"electrocardiogram, machine learning, decision support system","lastPublishedDoi":"10.21203/rs.3.rs-4848578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4848578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectrocardiograph (ECG) R-wave detection from \u0026nbsp;abdominal ECGs presents significant challenges due to signal distortion and interference. This study introduces a novel approach using K-Mean Clustering technique to detect ECG R waves from ECG signal. Leveraging the quad periodicity of R waves, we developed an algorithm to identify and correct misaligned and missed R wave detection in the RR time series.\u003c/p\u003e\n\u003cp\u003eOur detection process comprises two stages. Initially, we detect ECG R waves that do not overlap with \u0026nbsp;QRS complexes. The algorithm then corrects misaligned and missed R waves while predicting approximate regions where ECG R waves overlap with QRS. In the second stage, we detect overlapping ECG R waves within these predicted regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe construct QRS complex from non-overlapping detected complexes using K-Mean Clustering.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe algorithm then precisely locates overlapping ECG R waves by finding the optimal correlation between the actual QRS and the superposition of QRS within the predicted regions. 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