A Lightweight Method for Non-Invasive Blood Pressure Estimation Using PPG and Hjorth Parameters for Wearable Devices

A Lightweight Method for Non-Invasive Blood Pressure Estimation Using PPG and Hjorth Parameters for Wearable Devices | 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 A Lightweight Method for Non-Invasive Blood Pressure Estimation Using PPG and Hjorth Parameters for Wearable Devices Sarah Pereira, João Paulo Leite This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7124098/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 This work proposes a lightweight, non-invasive approach for estimating systolic and diastolic blood pressure (BP) from photoplethysmography (PPG) signals, leveraging Hjorth parameters as core features. By combining Hjorth descriptors—activity, mobility, and complexity—with conventional time-domain features, the method achieves high predictive accuracy while maintaining low computational complexity, making it suitable for integration into wearable health monitoring systems. The model was trained and evaluated on data from 85 subjects extracted from the publicly available University of California Irvine (UCI) repository, derived from the MIMIC-II database. The proposed approach achieved mean absolute errors (MAE) of 3.53 mmHg for systolic and 2.15 mmHg for diastolic BP. These results not only meet the performance requirements of the Association for the Advancement of Medical Instrumentation (AAMI), but also achieve Grade A classification under British Hypertension Society (BHS) standards. Offering performance comparable to more complex state-of-the-art models, this method stands out for its efficiency and ease of deployment on resource-constrained embedded platforms, representing a promising solution for accessible, continuous BP monitoring in real-world, low-resource healthcare environments. Photoplethysmography Machine Learning Blood Pressure Hjorth Parameters 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. 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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-7124098","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549211656,"identity":"1b58c3d6-4ede-4987-b5da-471188bd9b81","order_by":0,"name":"Sarah Pereira","email":"","orcid":"","institution":"Federal University of Itajubá","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Pereira","suffix":""},{"id":549211657,"identity":"3e0de436-b3ea-416f-a1e4-2fca36dd2bec","order_by":1,"name":"João Paulo Leite","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACxgYILcPADmQl2DDwADkGcGF8WngYeA4AtaQRoQUGeBgkEoBUGpiDXwtze/PDxwU1h3n4Jd+YPXiQYAN0YfM2CcYd93A7rOeYsfGMY4d5JGfnmBskJKQBXXisTILxTDFuLTMSzKR52A7zGNzOMZNI/HEY6EIgg7EtAY+W9G/SPP+AWm6eMZNISPjPwyD/hpCWHDNp3jaglhs8IC0HgLbwENDSc6bYeGZfOo9kT1oZUEsyDxtPWrFF4hncWgzb2zc+LvhmLcfPfnib5I8EO3sgY+ONjzvwaGkABjSKCBuIwK2BgUGeAV3LKBgFo2AUjAJ0AAB8IUsg6o6MOQAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University of Itajubá","correspondingAuthor":true,"prefix":"","firstName":"João","middleName":"Paulo","lastName":"Leite","suffix":""}],"badges":[],"createdAt":"2025-07-14 19:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7124098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7124098/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96840653,"identity":"fd27924f-2dfe-4f9e-a5a4-a61c5f9f658f","added_by":"auto","created_at":"2025-11-26 15:48:16","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4330,"visible":true,"origin":"","legend":"","description":"","filename":"c43f8108cc664936b73467062254092e.json","url":"https://assets-eu.researchsquare.com/files/rs-7124098/v1/2798a41612abb2d24b34d311.json"},{"id":97894317,"identity":"28eb4f77-313d-4469-82e6-0d1e4a9eb1e4","added_by":"auto","created_at":"2025-12-10 15:32:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":832799,"visible":true,"origin":"","legend":"","description":"","filename":"SarahSpringer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7124098/v1_covered_6e8ef9ff-cae0-4069-a5cb-970bfc567a56.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Lightweight Method for Non-Invasive Blood Pressure Estimation Using PPG and Hjorth Parameters for Wearable Devices\n","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":"Photoplethysmography, Machine Learning, Blood Pressure, Hjorth Parameters","lastPublishedDoi":"10.21203/rs.3.rs-7124098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7124098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This work proposes a lightweight, non-invasive approach for estimating systolic and diastolic blood pressure (BP) from photoplethysmography (PPG) signals, leveraging Hjorth parameters as core features. 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