Health Friend: Real-Time ECG Analysis Based on IoT

Health Friend: Real-Time ECG Analysis Based on IoT | 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 Health Friend: Real-Time ECG Analysis Based on IoT Arshad Shaikh, Bhagyashree Shinde, Trupti Patil, Archana Bhandare This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9085589/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Health Friend, an edge-based embedded system for ongoing cardiac monitoring and early identification of potentially lethal arrhythmias, is designed and developed in this study. To precisely record and digitize ECG signals, the system combines a Raspberry Pi 5, an AD8232 ECG sensor, and an MCP3008 ADC. A Butterworth low-pass filter is used to eliminate high-frequency noise and guarantee signal clarity. Using the MIT-BIH Arrhythmia Database as training data, a one-dimensional convolutional neural network (1D-CNN) divides heartbeats into five groups according to AAMI criteria. Real-time, cloud-independent ECG analysis is made possible by the model's local operation on the Raspberry Pi. Its usefulness in wearable, home care, and remote health monitoring applications is increased when detected anomalies cause alarms to be triggered by GPIO-driven actuators such buzzers or LEDs. This work demonstrates a scalable, cost-effective, and patient-centered approach to integrating embedded deep learning with biomedical signal processing for reliable cardiac health surveillance. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Electrocardiogram (ECG) Raspberry Pi Internet of Things (IoT) AD8232 MCP3008 Arrhythmia Detection Health Monitoring Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviewers agreed at journal 17 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 13 Apr, 2026 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. 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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-9085589","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":624005260,"identity":"64f6ca19-098c-4abb-a968-f7d976bbb80a","order_by":0,"name":"Arshad Shaikh","email":"data:image/png;base64,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","orcid":"","institution":"Padmabhooshan Vasantraodada Patil Institute of Technology, Budhgaon","correspondingAuthor":true,"prefix":"","firstName":"Arshad","middleName":"","lastName":"Shaikh","suffix":""},{"id":624005261,"identity":"8411dc9d-2879-4a89-9ba9-3b76f976e3b2","order_by":1,"name":"Bhagyashree Shinde","email":"","orcid":"","institution":"Padmabhooshan Vasantraodada Patil Institute of Technology, Budhgaon","correspondingAuthor":false,"prefix":"","firstName":"Bhagyashree","middleName":"","lastName":"Shinde","suffix":""},{"id":624005264,"identity":"0a72942e-509a-4fb2-8bb3-9af838be65a9","order_by":2,"name":"Trupti Patil","email":"","orcid":"","institution":"Padmabhooshan Vasantraodada Patil Institute of Technology, Budhgaon","correspondingAuthor":false,"prefix":"","firstName":"Trupti","middleName":"","lastName":"Patil","suffix":""},{"id":624005266,"identity":"99a5a6c4-9fbb-46b8-bb73-686cddf235b8","order_by":3,"name":"Archana Bhandare","email":"","orcid":"","institution":"Padmabhooshan Vasantraodada Patil Institute of Technology, Budhgaon","correspondingAuthor":false,"prefix":"","firstName":"Archana","middleName":"","lastName":"Bhandare","suffix":""}],"badges":[],"createdAt":"2026-03-10 15:08:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9085589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9085589/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107541701,"identity":"f851a308-30af-4b3e-bc9c-b420e1a76685","added_by":"auto","created_at":"2026-04-22 12:27:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":596229,"visible":true,"origin":"","legend":"","description":"","filename":"springer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9085589/v1_covered_6c823747-6c77-44aa-af03-7cf92df23108.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health Friend: Real-Time ECG Analysis Based on IoT","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Electrocardiogram (ECG), Raspberry Pi, Internet of Things (IoT), AD8232, MCP3008, Arrhythmia Detection, Health Monitoring","lastPublishedDoi":"10.21203/rs.3.rs-9085589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9085589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHealth Friend, an edge-based embedded system for ongoing cardiac monitoring and early identification of potentially lethal arrhythmias, is designed and developed in this study. 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