Beat-by-Beat ECG Monitoring from PPG Using Spatio-Temporal Information with WaveNet

Beat-by-Beat ECG Monitoring from PPG Using Spatio-Temporal Information with WaveNet | 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 Beat-by-Beat ECG Monitoring from PPG Using Spatio-Temporal Information with WaveNet Osama Omer, Mohammed Alammar, Hamada Esmaiel, Mostafa Salah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8043333/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The synthesis of the Electrocardiogram (ECG) from the Photoplethysmogram (PPG) is a hot research topic in the last decade. Also, PPG can be measured using PPG sensor connected with fingertips or even measured from RGB signals. The successful reconstruction of the ECG waveform from a PPG signal promises to enable continuous, comfortable, and diagnostically rich cardiac monitoring outside of traditional clinical settings. The main limitations of the current ECG reconstruction techniques are the use of invalid training datasets, unsuitable networks, and/or insufficient information. Therefore, in this paper, we propose to emphasize the cleanness of the datasets including spatial and temporal information with adapted WaveNet as an efficient network which is deep neural network model for generating raw audio waveforms. The adaptation includes the use of the network for regression rather than audio generation. Also, the architecture of the network is optimized for signal-to-signal regression. Two scenarios are designed and tested in this paper, namely, beat-to-beat regression fixed number of beats (three beats is tested) to beat. The effect of using the time interval information is evaluated as well, The experimental results demonstrated that using fixed number of beats is better than using single beat and better that using signal to signal ECG estimation. Also, it confirmed that the time interval information is essential for ECG monitoring. The proposed system can achieve a Pearson‘s correlation coefficient (r), Fréchet distance (FD), root mean square error (RMSE), percentage root mean square difference (PRD), mean absolute error (MAE) and standard deviation (SD) of 0.9831, 0.1174, 0.0373, 12.3207, 0.0262 and 0.0371, respectively which outperforms the existing state-of-the-arts ECG reconstruction algorithms. This research uses both of spatial and temporal information in beat-to-beat ECG reconstruction. ECG PPG Single Lead Deep Learning WaveNet Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Editor assigned by journal 06 Nov, 2025 Submission checks completed at journal 06 Nov, 2025 First submitted to journal 05 Nov, 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. 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-8043333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541109634,"identity":"03c72329-58cc-41dd-8f2c-9ba7fc9179e2","order_by":0,"name":"Osama Omer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYHAD5gNQRgLRWthgSonXwmNAnBZ+scPHPjBU1Mnrtp/5+PFn22EGfvYcA4YPv3BrkZydljyD4cxhw21ncjdL8wK1SPa8MWCc2Ydbi8HtHGMGxrYDjNsO5G6QZtx2mMHgRo4BM28PPi35n4Fa6uy3nX/z+OdPoBZ7kJa/eLXkMAO1MCduu5HDJsELskUCqIXhB16/GDMknDmcvO3GMzNr3n/pPBJnnhUc7G3ArYVfOvkxw4eKOttt55Mf3/xxxlqOvz1544Mff3BrAYMEJDYPiDjA2EZACxZAyJZRMApGwSgYSQAAnspUwD5dKssAAAAASUVORK5CYII=","orcid":"","institution":"aswan university","correspondingAuthor":true,"prefix":"","firstName":"Osama","middleName":"","lastName":"Omer","suffix":""},{"id":541109635,"identity":"adffbab6-c8e8-4795-8001-b4234deb8012","order_by":1,"name":"Mohammed Alammar","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Alammar","suffix":""},{"id":541109636,"identity":"370ba289-e2ec-4e50-ae75-61117c9ad274","order_by":2,"name":"Hamada Esmaiel","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Hamada","middleName":"","lastName":"Esmaiel","suffix":""},{"id":541109637,"identity":"6ecb19fa-9eb1-41e1-ae9e-a533a2035211","order_by":3,"name":"Mostafa Salah","email":"","orcid":"","institution":"Sohag University","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Salah","suffix":""}],"badges":[],"createdAt":"2025-11-06 03:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8043333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8043333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95341681,"identity":"6645b5b5-600f-4949-b1ad-9879d09d28e8","added_by":"auto","created_at":"2025-11-07 02:08:01","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6251,"visible":true,"origin":"","legend":"","description":"","filename":"cf23f3d5741441fc85bd6d341101158e.json","url":"https://assets-eu.researchsquare.com/files/rs-8043333/v1/a3fbd18341d990eb8ab0bb47.json"},{"id":95525185,"identity":"4b42fd5c-6496-466c-a967-bf6d2a237260","added_by":"auto","created_at":"2025-11-10 10:04:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":953350,"visible":true,"origin":"","legend":"","description":"","filename":"PPG2ECGWaveNet.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8043333/v1_covered_f9a09943-32d8-4b5d-adb0-4acedf37536e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beat-by-Beat ECG Monitoring from PPG Using Spatio-Temporal Information with WaveNet","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":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ECG, PPG, Single Lead, Deep Learning, WaveNet","lastPublishedDoi":"10.21203/rs.3.rs-8043333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8043333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The synthesis of the Electrocardiogram (ECG) from the Photoplethysmogram (PPG) is a hot research topic in the last decade. 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