Improved R-Peak Detection in Long-Term ECGs: Leveraging Hybrid Linearization and LSTM with Grey Wolf Optimization

Improved R-Peak Detection in Long-Term ECGs: Leveraging Hybrid Linearization and LSTM with Grey Wolf Optimization | 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 Improved R-Peak Detection in Long-Term ECGs: Leveraging Hybrid Linearization and LSTM with Grey Wolf Optimization SARAVANAN VELUSAMY, PALLIKONDA RAJASEKARAN MURUGAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5409435/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 precise interpretation of the ECG signal can reveal the condition of the heart. ECG signal analysis can assist in identifying any abnormalities or arrhythmias in the heart. Premature Ventricular Contractions (PVCs) are irregular heartbeats that may signal the presence of a heart ailment. Long-term ECGs are commonly utilized in clinical practice to diagnose PVCs. However, analyzing these long-term ECGs is time-consuming for cardiologists and requires human involvement. This research proposes a robust approach for detecting R peaks in QRS complexes using a recurrent neural network. Our proposed methodology was applied to the well-known MIT-BIH Arrhythmia Database (MIT-DB) dataset and the China Physiological Signal Challenge (2020) database, which contains over a million beats. The hybrid linearization technique uses an adaptive filter and discrete wavelet transform (DWT) to remove noise from the ECG signal. The next step is to use principle component analysis (PCA) to extract characteristics from the ECG data. Lastly, the R peak signals are classified using long short-term memory (LSTM) to improve accuracy through optimization techniques like Grey Wolf optimization (GWO). The algorithm's performance was also evaluated using the MIT-BIH Arrhythmia database and the China Physiological Signal Challenge (2020). The suggested formal technique yields the best results for R-peak detection on CPSC-DB, with F1-score of 95.3%, recall of 96.8%, accuracy of 99.5%, and precision of 95.3%. The F1-score, recall, and precision of the algorithms on MIT-DB are all equivalent to, or better than, those of the competing methods. ECG signal MIT-DB CPSC-DB QRS R wave LSTM adaptive filter precision recall accuracy GWO. Full Text Additional Declarations No competing interests reported. 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-5409435","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377256117,"identity":"91eff6bb-0731-485e-97a8-4e461d7adb45","order_by":0,"name":"SARAVANAN VELUSAMY","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYLACxgYGBn4QI6GABC0Skg0gLQakaDE4AGIRo0W+/ezDz4U77OqMz69O/PDAgEGeX+wAfi0GZ9KNpWeeSZYwu/F2swTQYYYzZycQ0MKQxiDN23YAqOXsBpCWBIPbBLTI9z9j/g3SYjzj7OYfRGlhuJHGBrbFgL93G3G2GNx4xmbN25YsOeMG7zaLBAMJwn6R709jvs3bZsfP3392880fFTby/NKEHAYHEmCVEsQqBwH+A6SoHgWjYBSMgpEEAFxCQM3xi96hAAAAAElFTkSuQmCC","orcid":"","institution":"Kalasalingam Academy of Research and Education","correspondingAuthor":true,"prefix":"","firstName":"SARAVANAN","middleName":"","lastName":"VELUSAMY","suffix":""},{"id":377256118,"identity":"5751cd97-34c9-4c6f-b95f-4248f411d5f3","order_by":1,"name":"PALLIKONDA RAJASEKARAN MURUGAN","email":"","orcid":"","institution":"Kalasalingam Academy of Research and Education","correspondingAuthor":false,"prefix":"","firstName":"PALLIKONDA","middleName":"RAJASEKARAN","lastName":"MURUGAN","suffix":""}],"badges":[],"createdAt":"2024-11-07 11:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5409435/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5409435/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71685569,"identity":"d41d6e23-3679-4606-ad4a-4a4336f252e2","added_by":"auto","created_at":"2024-12-17 17:16:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":597584,"visible":true,"origin":"","legend":"","description":"","filename":"ImprovedRPeakDetectioninLongTermECGsLeveragingHybridLinearizationandLSTMwithGreyWolfOptimization.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5409435/v1_covered_c684c603-3a7a-4149-b2fe-cf08b1cab904.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improved R-Peak Detection in Long-Term ECGs: Leveraging Hybrid Linearization and LSTM with Grey Wolf Optimization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"ECG signal, MIT-DB, CPSC-DB, QRS, R wave, LSTM, adaptive filter, precision, recall, accuracy, GWO.","lastPublishedDoi":"10.21203/rs.3.rs-5409435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5409435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe precise interpretation of the ECG signal can reveal the condition of the heart. ECG signal analysis can assist in identifying any abnormalities or arrhythmias in the heart. Premature Ventricular Contractions (PVCs) are irregular heartbeats that may signal the presence of a heart ailment. Long-term ECGs are commonly utilized in clinical practice to diagnose PVCs. However, analyzing these long-term ECGs is time-consuming for cardiologists and requires human involvement. This research proposes a robust approach for detecting R peaks in QRS complexes using a recurrent neural network. Our proposed methodology was applied to the well-known MIT-BIH Arrhythmia Database (MIT-DB) dataset and the China Physiological Signal Challenge (2020) database, which contains over a million beats. The hybrid linearization technique uses an adaptive filter and discrete wavelet transform (DWT) to remove noise from the ECG signal. The next step is to use principle component analysis (PCA) to extract characteristics from the ECG data. 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