A Trust-Aware Hybrid Unsupervised Framework for Robust ECG Anomaly Detection in IoMT Monitoring Networks

A Trust-Aware Hybrid Unsupervised Framework for Robust ECG Anomaly Detection in IoMT Monitoring Networks | 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 Trust-Aware Hybrid Unsupervised Framework for Robust ECG Anomaly Detection in IoMT Monitoring Networks Venus Mohammadi, Bahram Sadeghi Bigham This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8917980/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 A reliable anomaly detection mechanism is paramount for ensuring trustworthy electrocardiogram (ECG) monitoring within the Internet of Medical Things (IoMT) ecosystems, where diverse noise sources and acquisition variability frequently compromise signals. This study introduces a hybrid unsupervised framework that integrates a Long Short-Term Memory (LSTM) autoencoder with an Isolation Forest to enable robust signal-trust assessment under unlabeled and noisy conditions. The proposed methodology synergizes temporal reconstruction learning with distribution-based outlier isolation through a trust-driven decision logic. This integration enables the explicit differentiation of true pathological abnormalities from noise-induced distortions, addressing a critical gap in conventional diagnostic systems. The framework was rigorously evaluated using the MIT-BIH Arrhythmia benchmark dataset in a single-modal, label-free learning environment. Experimental results demonstrate superior performance over both standalone architectures and contemporary state-of-the-art methods, achieving an accuracy of 99.71%, sensitivity of 99.92%, specificity of 99.46%, and an ROC-AUC of 99.96%. Ablation studies confirm that the hybrid synergy significantly enhances detection reliability compared to individual LSTM-AE or Isolation Forest implementations. Furthermore, the model exhibits a substantial reduction in both false-negative risk (FNR: 0.08) and false-alarm rate (FPR: 0.54%), thereby bolstering clinical confidence in continuous monitoring scenarios. The findings suggest that the proposed trust-based framework provides a robust and operationally efficient solution for ECG anomaly detection without requiring extensive labeled datasets, supporting sustainable long-term monitoring in real-world IoMT healthcare applications. Artificial Intelligence and Machine Learning Health Policy Electrocardiogram (ECG) anomaly detection Unsupervised learning LSTM autoencoder Isolation Forest Trust assessment Internet of Medical Things (IoMT) Full Text Additional Declarations The authors declare no competing interests. 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-8917980","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593986905,"identity":"77412d16-4b72-41ac-a5d6-0c49d46808a0","order_by":0,"name":"Venus Mohammadi","email":"","orcid":"https://orcid.org/0000-0001-7923-7331","institution":"Alzahra University","correspondingAuthor":false,"prefix":"","firstName":"Venus","middleName":"","lastName":"Mohammadi","suffix":""},{"id":593986906,"identity":"0bf77e44-4615-40a9-bc11-7c1d292a6902","order_by":1,"name":"Bahram Sadeghi Bigham","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYPCCA2BSIqECSDIzN5Ci5QxICyMpWhjbQBQBLQbHew8w3ai4I6877fDDGw/n1UbztwO1/KjYhlvLmXMJzDlnnhluu51mbJG47XjujMOMDYw9Z27j1CI5I8eAObftMOO22wlmEonbjuU2ALUwM7bh0TL/DViL/bbb6d8kEuccy51PSAu/BA9YS+K22zlAWxpqcjcQ1MKTY3A458zhZKCWYouEYwdyNwK1HMTnFzb2M4aPcyoO2wIdtvHmj5q63HnnDx988KMCtxYQOIDEPowhQhDUkaJ4FIyCUTAKRggAADfEYnULIkMFAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3387-3193","institution":"Alzahra University","correspondingAuthor":true,"prefix":"","firstName":"Bahram","middleName":"Sadeghi","lastName":"Bigham","suffix":""}],"badges":[],"createdAt":"2026-02-19 15:04:00","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8917980/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8917980/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104397183,"identity":"8eae7b45-3b73-470b-8736-2561ea5deeaa","added_by":"auto","created_at":"2026-03-11 11:41:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1200863,"visible":true,"origin":"","legend":"","description":"","filename":"HeartFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8917980/v1_covered_d409a26b-5f50-49f3-99d2-ac6099ce3b10.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Trust-Aware Hybrid Unsupervised Framework for Robust ECG Anomaly Detection in IoMT Monitoring Networks\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[{"identity":"c50c4f94-1ced-41b8-8900-3d47646e31a7","identifier":"10.13039/501100003968","name":"Iran National Science Foundation","awardNumber":"4047209","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Alzahra University","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":"Electrocardiogram (ECG) anomaly detection, Unsupervised learning, LSTM autoencoder, Isolation Forest, Trust assessment, Internet of Medical Things (IoMT)","lastPublishedDoi":"10.21203/rs.3.rs-8917980/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8917980/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA reliable anomaly detection mechanism is paramount for ensuring trustworthy electrocardiogram (ECG) monitoring within the Internet of Medical Things (IoMT) ecosystems, where diverse noise sources and acquisition variability frequently compromise signals. 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