R2A-MAGNet: A Mamba-Based Attentive Gated Recurrent Network for Reconstructing Central Arterial Pressure from Radial Pressure Waveforms

R2A-MAGNet: A Mamba-Based Attentive Gated Recurrent Network for Reconstructing Central Arterial Pressure from Radial Pressure Waveforms | 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 R2A-MAGNet: A Mamba-Based Attentive Gated Recurrent Network for Reconstructing Central Arterial Pressure from Radial Pressure Waveforms Dongyi He, Kecheng Feng, Bin Jiang, He Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6311355/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 Cardiovascular diseases require precise blood pressure monitoring, with central aortic pressure (CAP) being a stronger predictor of risk than peripheral arterial pressure (PAP). However, invasive methods for measuring CAP are accurate, they are costly and carry procedural risks, while non-invasive alternatives often lack accuracy and struggle to adapt to individual physiological differences. To address these limitations, this paper introduces R2A-MAGNet, a novel deep learning model that non-invasively reconstructs CAP waveforms from radial arterial pressure (RAP) waveforms. R2A-MAGNet combines CNNs for local feature extraction with GRUs, the Selective State Spaces Model (Mamba), Self-Attention, and Cross-Attention for global feature learning and enhanced information interaction. Tested on a real-world dataset, R2A-MAGNet outperforms existing models, achieving the lowest Mean Absolute Error for CAP (1.93 mmHg), RMSE for central systolic (2.77 mmHg), and RMSE for central diastolic pressure (1.48 mmHg). Visual and statistical analyses confirm its accuracy and reliability, underscoring its potential for advanced blood pressure monitoring. Central Aortic Pressure (CAP) Radial Arterial Pressure (RAP) Non-invasive Blood Pressure Monitoring Deep Learning Waveform Reconstruction 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-6311355","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443756324,"identity":"68e49c1b-26f9-45d2-b318-7184494ee0a5","order_by":0,"name":"Dongyi He","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dongyi","middleName":"","lastName":"He","suffix":""},{"id":443756325,"identity":"1e2e0040-ba8c-4341-86de-ac0c818179fb","order_by":1,"name":"Kecheng Feng","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kecheng","middleName":"","lastName":"Feng","suffix":""},{"id":443756326,"identity":"929a9549-b803-41ff-9710-d2047aba908d","order_by":2,"name":"Bin Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACxhnMBw58MJDgkWdvPgAVSyCkhS3x4YwKGxnDnmMgpQaEtTBI8Bgb85xJs2G4kWNAnBbm2Q1mErxth3kYG3I+fnjb9oeBnx2o9+cOPA6bcyBNQhKohZ3h7GbJuW0GDJI9bwwYe8/g80vCMQlDkC2NvduYeYFaDIAuZGZsw6clsU0iEaiF4TDPM7AWe8JakpkNDpxJ42E4xsMGsUWCkJY5xxgfNlTY8Bj2sBlLzjlnzCNx5lnBwV48Wgxn9384/MdAwl5e/vHDD2/K5OT425M3PviJT0sDMo8HjBgYDuDWwMAgj8Ljwad0FIyCUTAKRiwAAIJpUjGUTT0xAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Jiang","suffix":""},{"id":443756327,"identity":"6db2a6a0-65e9-47e1-a9b2-1505c7230a65","order_by":3,"name":"He Yan","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2025-03-26 10:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6311355/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6311355/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80795755,"identity":"e82efcd1-89ae-442e-b716-eeafc8d8009d","added_by":"auto","created_at":"2025-04-17 07:37:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2708961,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6311355/v1_covered_77adcf34-b996-4ddd-9ebb-0f3e77ebc040.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"R2A-MAGNet: A Mamba-Based Attentive Gated Recurrent Network for Reconstructing Central Arterial Pressure from Radial Pressure Waveforms","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":"Central Aortic Pressure (CAP), Radial Arterial Pressure (RAP), Non-invasive Blood Pressure Monitoring, Deep Learning, Waveform Reconstruction","lastPublishedDoi":"10.21203/rs.3.rs-6311355/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6311355/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cardiovascular diseases require precise blood pressure monitoring, with central aortic pressure (CAP) being a stronger predictor of risk than peripheral arterial pressure (PAP). However, invasive methods for measuring CAP are accurate, they are costly and carry procedural risks, while non-invasive alternatives often lack accuracy and struggle to adapt to individual physiological differences. To address these limitations, this paper introduces R2A-MAGNet, a novel deep learning model that non-invasively reconstructs CAP waveforms from radial arterial pressure (RAP) waveforms. R2A-MAGNet combines CNNs for local feature extraction with GRUs, the Selective State Spaces Model (Mamba), Self-Attention, and Cross-Attention for global feature learning and enhanced information interaction. Tested on a real-world dataset, R2A-MAGNet outperforms existing models, achieving the lowest Mean Absolute Error for CAP (1.93 mmHg), RMSE for central systolic (2.77 mmHg), and RMSE for central diastolic pressure (1.48 mmHg). 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