Analysis of ECG5000 Electrocardiogram Signals Using Improved DTW Algorithm

Analysis of ECG5000 Electrocardiogram Signals Using Improved DTW Algorithm | 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 Analysis of ECG5000 Electrocardiogram Signals Using Improved DTW Algorithm Yu Han, ChunYu Miao, Yu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7002634/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The research introduces an ECG classification framework that integrates dynamic temporal window and multi-dimensional attention mechanism, improving classification performance through adaptive feature fusion and robust temporal matching. By constructing a time-frequency domain feature system, key features such as RR interval variability and spectral energy distribution are extracted, and high-discriminative features are screened using analysis of variance. Based on the temporal perception attention model, a dynamic similarity matrix is generated by integrating signal amplitude, gradient and curvature information, and the matching window width is adaptively adjusted to address the sensitivity of traditional Dynamic Time Warping (DTW) algorithm to waveform distortion. A hybrid classification model is constructed using the joint representation of multi-dimensional features and original signals, and the path backtracking strategy is optimized by try-catch mechanism. Experiments on the ECG5000 dataset show that the proposed method achieves a classification accuracy of 94.2%, and demonstrates lower error rates than comparative algorithms under noises with different frequencies and specifications. dynamic time warping (DTW) temporal - aware attention matrix adaptive dynamic window multi - dimensional data support robustness against diverse noises Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor assigned by journal 01 Jul, 2025 Submission checks completed at journal 01 Jul, 2025 First submitted to journal 29 Jun, 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. 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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-7002634","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478989919,"identity":"6dd708db-8c36-4cf9-bc4a-202f09f090e5","order_by":0,"name":"Yu Han","email":"","orcid":"","institution":"Northeast Electric Power Uni- versity","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Han","suffix":""},{"id":478989920,"identity":"12c9e07e-54d0-424e-8de3-8aa1c3e83140","order_by":1,"name":"ChunYu Miao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACgwM8DAwfKtjk+JmZDz8gSoslUAvjjDN8xpLtbGkGRGmxB2ph5myTS9xwnkdBgigtZsfPHpNmYDNL3HyYh8GAocYmmrCWM3lp0gU8acbbDvMeeMBwLC23gaCWGzxm0jMkjsluO8yXYMDYcJiwFgOQFh6D/4ybm3kMJEjQksCmuIGZaC1ncowtZxxgM5Y4DAzkBGL8YnD8jOGNj/+AUdl/+PCDDzU2hLUAAQsiOhKIUA4CzB+IVDgKRsEoGAUjFQAACAhAZ4MkqMIAAAAASUVORK5CYII=","orcid":"","institution":"Northeast Electric Power Uni- versity","correspondingAuthor":true,"prefix":"","firstName":"ChunYu","middleName":"","lastName":"Miao","suffix":""},{"id":478989921,"identity":"552ec1ff-aabb-44d2-88dd-4b51552017a2","order_by":2,"name":"Yu Zhang","email":"","orcid":"","institution":"Northeast Electric Power Uni- versity","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-29 12:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7002634/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7002634/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86164576,"identity":"09489ef6-787c-48b8-8763-0a18c51bc8b5","added_by":"auto","created_at":"2025-07-07 13:17:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":537566,"visible":true,"origin":"","legend":"","description":"","filename":"AnalysisofECG5000ElectrocardiogramSignalsUsingImprovedDTWAlgorithm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7002634/v1_covered_c3d4a76e-2b31-4e1e-a97f-788a3c6199dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of ECG5000 Electrocardiogram Signals Using Improved DTW Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"dynamic time warping (DTW), temporal - aware attention matrix, adaptive dynamic window, multi - dimensional data support, robustness against diverse noises","lastPublishedDoi":"10.21203/rs.3.rs-7002634/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7002634/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe research introduces an ECG classification framework that integrates dynamic temporal window and multi-dimensional attention mechanism, improving classification performance through adaptive feature fusion and robust temporal matching. 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