Enhanced CNN–TCN–GTN–SE for Real-Time Heart Rate Classification in Personalized Cardiac Rehabilitation using IoT-driven Smart Wearable Device

Enhanced CNN–TCN–GTN–SE for Real-Time Heart Rate Classification in Personalized Cardiac Rehabilitation using IoT-driven Smart Wearable Device | 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 Article Enhanced CNN–TCN–GTN–SE for Real-Time Heart Rate Classification in Personalized Cardiac Rehabilitation using IoT-driven Smart Wearable Device Steven Chin Su Leong, Nor Maniha Abdul Ghani, Mohd Ridzuan Mohd Said, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7035414/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 Accurate heart rate monitoring is vital in assessing cardiovascular responses during rehabilitation, particularly for individuals with ischemic heart disease. However, conventional wearable systems often overlook inter-individual variations in heart rate dynamics and personalized cardiac performance, limiting their clinical value. This study presents an enhanced deep learning model that enables real-time classification using an IoT-driven smart wearable device and photoplethysmography (PPG) sensor. The proposed architecture integrates Convolutional Neural Networks (CNN), Temporal Convolutional Networks (TCN), Gated Transformer Networks (GTN), and Squeeze-and-Excitation (SE) blocks to extract both localized and long-range features from heart rate time-series data with [ADAM optimizer]. A total of 1,827 clinically labeled heart rate samples were collected from 38 patients undergoing a structured 480-second rehabilitation protocol. The model was trained and evaluated using 10-fold cross-validation and an independent test set, achieving an accuracy of 98.22% and a specificity of 98.53%. Ablation studies confirmed the contribution of each architectural component. This work introduces a new AI-powered solution, clinically scalable and one of the first models validated on real-world PPG data collected during structured and personalized cardiovascular rehabilitation. This work bridges wearable AI and clinical rehabilitation, offering substantial potential for real-time personalized cardiovascular monitoring and decision support in home-based settings. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Real-time heart rate classification enhanced deep learning personalized cardiac rehabilitation wearable PPG sensors IoT-enabled wearable device 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-7035414","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":521830465,"identity":"b86c62f0-90bb-44eb-af74-a688d9809a0c","order_by":0,"name":"Steven Chin Su Leong","email":"","orcid":"","institution":"Universiti Malaysia Pahang Al- Sultan Abdullah","correspondingAuthor":false,"prefix":"","firstName":"Steven","middleName":"Chin Su","lastName":"Leong","suffix":""},{"id":521830466,"identity":"02aba948-81f7-44ca-82ce-45a1213dcfd4","order_by":1,"name":"Nor Maniha Abdul 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Device","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":"Real-time heart rate classification, enhanced deep learning, personalized cardiac rehabilitation, wearable PPG sensors, IoT-enabled wearable device","lastPublishedDoi":"10.21203/rs.3.rs-7035414/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7035414/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate heart rate monitoring is vital in assessing cardiovascular responses during rehabilitation, particularly for individuals with ischemic heart disease. However, conventional wearable systems often overlook inter-individual variations in heart rate dynamics and personalized cardiac performance, limiting their clinical value. This study presents an enhanced deep learning model that enables real-time classification using an IoT-driven smart wearable device and photoplethysmography (PPG) sensor. The proposed architecture integrates Convolutional Neural Networks (CNN), Temporal Convolutional Networks (TCN), Gated Transformer Networks (GTN), and Squeeze-and-Excitation (SE) blocks to extract both localized and long-range features from heart rate time-series data with [ADAM optimizer]. A total of 1,827 clinically labeled heart rate samples were collected from 38 patients undergoing a structured 480-second rehabilitation protocol. The model was trained and evaluated using 10-fold cross-validation and an independent test set, achieving an accuracy of 98.22% and a specificity of 98.53%. Ablation studies confirmed the contribution of each architectural component. This work introduces a new AI-powered solution, clinically scalable and one of the first models validated on real-world PPG data collected during structured and personalized cardiovascular rehabilitation. This work bridges wearable AI and clinical rehabilitation, offering substantial potential for real-time personalized cardiovascular monitoring and decision support in home-based settings.\u003c/p\u003e","manuscriptTitle":"Enhanced CNN–TCN–GTN–SE for Real-Time Heart Rate Classification in Personalized Cardiac Rehabilitation using IoT-driven Smart Wearable Device","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 14:04:51","doi":"10.21203/rs.3.rs-7035414/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":"7d6eda1e-d370-4cb3-a0af-9920935a8037","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55461577,"name":"Health sciences/Cardiology"},{"id":55461578,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":55461579,"name":"Physical sciences/Engineering"},{"id":55461580,"name":"Health sciences/Health care"},{"id":55461581,"name":"Physical sciences/Mathematics and computing"},{"id":55461582,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-03-31T15:56:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 14:04:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7035414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7035414","identity":"rs-7035414","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}