Personalized ECG Arrhythmia Classification Using Single Normal Beat Guidance: A Minimal Supervision Approach for Atypical Morphology Cases

Personalized ECG Arrhythmia Classification Using Single Normal Beat Guidance: A Minimal Supervision Approach for Atypical Morphology Cases | 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 Personalized ECG Arrhythmia Classification Using Single Normal Beat Guidance: A Minimal Supervision Approach for Atypical Morphology Cases Junho An, Kwanglo Lee, Sunghoon Jung, Jinkook Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7966938/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Electrocardiogram (ECG) exhibits diverse morphologies, allowing for individual distinction but often leading to varying interpretations of similar patterns. This complexity, especially with atypical morphologies, poses challenges for deep learning (DL) algorithms, potentially causing hidden stratification issues. While patient-specific studies show promise, their performance on unseen data remains uncertain, requiring labor-intensive corrections. The proposed model uses the morphology of the target beat and its intervals with surrounding beats. A single normal beat selected by an expert serves as a guide to influence the classification of all beats. Morphological similarity and RR intervals are utilized to classify beats into normal (N), supraventricular ectopic (S), and ventricular ectopic (V) types. ECGs were collected from 1,366 patients using patch-type devices, categorized into typical (91%; QRS duration (QRSd) \((<)\) 120 ms for N, S, and \((\geq)\) 120 ms for V) and atypical (9%; QRSd \((\geq)\) 120 ms for N, S, or \((<)\) 120 ms for V) groups. Inter-patient 5-fold cross-validation showed improved F1-score for the unseen atypical group, averaging 87.49 ( \((+)\) 7.66) with a standard deviation of 14.57 ( \((-)\) 5.91). The proposed method enhances ECG analysis for atypical cases by leveraging minimal patient-specific guide data. It demonstrates potential for improving productivity in clinical ECG analysis. Personalized ECG Analysis Arrhythmia Detection Deep Learning Wearable ECG Minimal Supervision ECG Morphology Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Reviews received at journal 26 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor invited by journal 30 Oct, 2025 Editor assigned by journal 29 Oct, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 28 Oct, 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-7966938","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537727009,"identity":"6847bee6-766f-4fc7-a3c9-50df1664e875","order_by":0,"name":"Junho An","email":"","orcid":"","institution":"HUINNO Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Junho","middleName":"","lastName":"An","suffix":""},{"id":537727011,"identity":"94ed571e-410c-43b0-bb54-d9cc8517f36a","order_by":1,"name":"Kwanglo Lee","email":"","orcid":"","institution":"HUINNO Co., 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Cases","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":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision 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This complexity, especially with atypical morphologies, poses challenges for deep learning (DL) algorithms, potentially causing hidden stratification issues. While patient-specific studies show promise, their performance on unseen data remains uncertain, requiring labor-intensive corrections.\u003c/p\u003e\u003cp\u003eThe proposed model uses the morphology of the target beat and its intervals with surrounding beats. A single normal beat selected by an expert serves as a guide to influence the classification of all beats. Morphological similarity and RR intervals are utilized to classify beats into normal (N), supraventricular ectopic (S), and ventricular ectopic (V) types.\u003c/p\u003e\u003cp\u003eECGs were collected from 1,366 patients using patch-type devices, categorized into typical (91%; QRS duration (QRSd) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\u0026lt;)\\)\u003c/span\u003e\u003c/span\u003e 120 ms for N, S, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\geq)\\)\u003c/span\u003e\u003c/span\u003e 120 ms for V) and atypical (9%; QRSd \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\geq)\\)\u003c/span\u003e\u003c/span\u003e 120 ms for N, S, or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\u0026lt;)\\)\u003c/span\u003e\u003c/span\u003e 120 ms for V) groups. Inter-patient 5-fold cross-validation showed improved F1-score for the unseen atypical group, averaging 87.49 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((+)\\)\u003c/span\u003e\u003c/span\u003e7.66) with a standard deviation of 14.57 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((-)\\)\u003c/span\u003e\u003c/span\u003e5.91).\u003c/p\u003e\u003cp\u003eThe proposed method enhances ECG analysis for atypical cases by leveraging minimal patient-specific guide data. It demonstrates potential for improving productivity in clinical ECG analysis.\u003c/p\u003e","manuscriptTitle":"Personalized ECG Arrhythmia Classification Using Single Normal Beat Guidance: A Minimal Supervision Approach for Atypical Morphology Cases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 19:34:12","doi":"10.21203/rs.3.rs-7966938/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-02T16:16:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T11:15:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T18:00:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191012060356082310098129867316029449215","date":"2026-02-16T11:11:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245537947643891035028523011873050935176","date":"2026-02-13T17:58:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76892699042882188917964442581646046443","date":"2026-02-13T15:34:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T08:25:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187088130126961569187995109441325668023","date":"2026-02-09T08:07:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296795205906484046054111616377978475159","date":"2026-02-06T14:57:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T13:33:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-30T11:32:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-29T11:33:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-29T11:32:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2025-10-28T08:12:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"786c961d-3622-48e2-822d-5821157f34f8","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 19:34:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7966938","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7966938","identity":"rs-7966938","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}