Scanned ECG-Based Deep Learning for Localization of Premature Ventricular Complexes Origins

Scanned ECG-Based Deep Learning for Localization of Premature Ventricular Complexes Origins | 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 Scanned ECG-Based Deep Learning for Localization of Premature Ventricular Complexes Origins Fatemeh Davarinia, Afrooz Arzehgar, Elahe Heidari, Davood Ramezaninezhad, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7027627/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 Background: Premature ventricular complexes (PVCs) frequently originate from the right or left ventricular outflow tract (RVOT or LVOT). Accurate localization is essential for guiding catheter ablation but remains challenging due to morphological overlap on surface Electrocardiogram(ECG). Objective: To develop and evaluate an image-based deep learning framework for automated localization of PVC origins using scanned 12-lead ECGs. Methods: Scanned ECGs from 59 patients with confirmed LVOT or RVOT PVCs were preprocessed and augmented to improve quality and generalizability. Two models, a custom convolutional neural network (CNN) and a transfer learning-based MobileNetV2, were trained to classify PVC origin. Performance was assessed using fivefold cross-validation and metrics including accuracy, sensitivity, specificity, and area under the ROC curve (AUC). Grad-CAM was applied for interpretability. Results: MobileNetV2 outperformed CNN across all metrics, achieving an accuracy of 0.74, sensitivity of 0.78, specificity of 0.70, and AUC of 0.84. Grad-CAM visualizations confirmed the model's attention to clinically relevant features of QRS morphology, particularly precordial leads. Conclusion: This study demonstrates the feasibility of image-based deep learning for PVC localization using paper-based ECGs. MobileNetV2 offers a promising, interpretable solution for real-world deployment, especially in resource-limited settings where digital ECG signals are unavailable. Premature Ventricular Cntraction Machine Learning Electrocardiogarm Artificial intelligence 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. 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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-7027627","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498903099,"identity":"277fae11-361b-49fd-a334-72b79430498e","order_by":0,"name":"Fatemeh Davarinia","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Davarinia","suffix":""},{"id":498903100,"identity":"3f719172-12ac-428c-9a17-50ae273e3c1d","order_by":1,"name":"Afrooz Arzehgar","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Afrooz","middleName":"","lastName":"Arzehgar","suffix":""},{"id":498903103,"identity":"3904b247-2520-4a01-815b-24f93b4907f6","order_by":2,"name":"Elahe Heidari","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Elahe","middleName":"","lastName":"Heidari","suffix":""},{"id":498903104,"identity":"e6fcd108-175d-48e1-870b-36f2f367aa13","order_by":3,"name":"Davood Ramezaninezhad","email":"","orcid":"","institution":"Isfahan university of medical sciences","correspondingAuthor":false,"prefix":"","firstName":"Davood","middleName":"","lastName":"Ramezaninezhad","suffix":""},{"id":498903105,"identity":"f101c90d-dba4-48bf-a641-5756f6a28d02","order_by":4,"name":"Feisal Rahimpour","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYFACHoYDYJr5YOMDuGACUVrYkpsNiNYCAWzpbRJEOcu8/ezBQzd32CRub2Nsq7rZts2egf3wA4aHe3BrkTmTl3A490xa4pxjjG23c9tuJzbwpBkwJDzDrUWCIcfgcG7b4cQZ8o1gLUBf5AD9cgCPFv43UC1sjG3FQC32DPxvCGiRyEFoYQZqYWyQIGSLBMiWM2nGQC3N0jnnbie2STwzOIDfYTnGn3N32MjOYGN/+Dmn7LY9P3/yw4c/8GgBA8YGJA4bEBPSgKZlFIyCUTAKRgE6AABo1lSw3TwlLQAAAABJRU5ErkJggg==","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Feisal","middleName":"","lastName":"Rahimpour","suffix":""}],"badges":[],"createdAt":"2025-07-02 09:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7027627/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7027627/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96801581,"identity":"6ccf0258-f40e-4d8d-abf4-81908ea445fc","added_by":"auto","created_at":"2025-11-26 08:39:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":896328,"visible":true,"origin":"","legend":"","description":"","filename":"Revised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7027627/v1_covered_680c3e6c-ce3f-4591-86d3-a89e6f8c9d25.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Scanned ECG-Based Deep Learning for Localization of Premature Ventricular Complexes Origins","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":"Premature Ventricular Cntraction, Machine Learning, Electrocardiogarm, Artificial intelligence","lastPublishedDoi":"10.21203/rs.3.rs-7027627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7027627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePremature ventricular complexes (PVCs) frequently originate from the right or left ventricular outflow tract (RVOT or LVOT). Accurate localization is essential for guiding catheter ablation but remains challenging due to morphological overlap on surface Electrocardiogram(ECG).\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo develop and evaluate an image-based deep learning framework for automated localization of PVC origins using scanned 12-lead ECGs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eScanned ECGs from 59 patients with confirmed LVOT or RVOT PVCs were preprocessed and augmented to improve quality and generalizability. Two models, a custom convolutional neural network (CNN) and a transfer learning-based MobileNetV2, were trained to classify PVC origin. Performance was assessed using fivefold cross-validation and metrics including accuracy, sensitivity, specificity, and area under the ROC curve (AUC). Grad-CAM was applied for interpretability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMobileNetV2 outperformed CNN across all metrics, achieving an accuracy of 0.74, sensitivity of 0.78, specificity of 0.70, and AUC of 0.84. Grad-CAM visualizations confirmed the model's attention to clinically relevant features of QRS morphology, particularly precordial leads.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study demonstrates the feasibility of image-based deep learning for PVC localization using paper-based ECGs. MobileNetV2 offers a promising, interpretable solution for real-world deployment, especially in resource-limited settings where digital ECG signals are unavailable.\u003c/p\u003e","manuscriptTitle":"Scanned ECG-Based Deep Learning for Localization of Premature Ventricular Complexes Origins","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 03:50:57","doi":"10.21203/rs.3.rs-7027627/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"52a057fa-f35c-4ad5-bb22-07b9e1842ab3","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T08:39:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 03:50:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7027627","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7027627","identity":"rs-7027627","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}