MCI detection using transformers for EEG-HRV fusion

MCI detection using transformers for EEG-HRV fusion | 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 MCI detection using transformers for EEG-HRV fusion Amal Boudaya, Siwar Chaabene, Bassem Bouaziz, Lotfi Chaari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6893576/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 The need for multi-modal data in detecting complex relationships between physiological processes to improve anomaly characterization becomes evident with the growing of medical records modalities. Robust fusion techniques are not often used for biomedical data, and current multi-modal approaches need to be employed more effectively. This makes it possible to early identify a variety of anomalies as Alzheimer's disease (AD). To possibly prevent AD, it is essential to identify mild cognitive impairment (MCI) patients who are more likely to develop this disease. Our study makes use of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD). This protocol has become a prevalent clinical method for a variety of dementias, including MCI. Numerous modalities including electroencephalography (EEG) and heart rate variability (HRV) can be used as helpful tools for MCI diagnosis. In this research, we developed a new deep learning (DL) method using the fusion of EEG and HRV signals acquired during the CERAD task. Using our data, a Transformer architecture was adapted to categorize participants into MCI and healthy control (HC). Our experimental results show that the proposed method has a benefit over the state-of-the-art in terms of classification accuracy. An accuracy test of 95.02%, a sensitivity of 95.47%, an F1 score of 95.03%, and a precision of 94.57% were achieved due to the proposed method. MCI detection EEG HRV Transformer Cognitive CERAD task 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-6893576","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476011175,"identity":"3d36572a-0d86-4d60-8c70-e94c08ab7441","order_by":0,"name":"Amal Boudaya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYHACxgOMDUDqABB/AGI2diL0wLUwzgBpYSZFCzMPiEtIC/+0ww8OMO6wSew7fsbws82vbfJ8zAyMHz7m4NYicTvN4ADjmbTEmWdyjKVz+24btjEzMEvO3IbHmtsJQC1thxM3HMgxkM7tuc0I1MLGzItHi/zt9A8QLeffGP+27LltT1CLwe0cqC03csykGX7cTiSoxfB2TsGBxDNpxjNvPCuz7G24ndzGzNiM1y9yt9M3Pvi4w0a273zy5hs//ty2nd/efPDDR3zeB4EEMMlhwMDYBmKAo4kowP6AgeEPsYpHwSgYBaNgJAEAi1lcTC4yxKsAAAAASUVORK5CYII=","orcid":"","institution":"University of Sfax","correspondingAuthor":true,"prefix":"","firstName":"Amal","middleName":"","lastName":"Boudaya","suffix":""},{"id":476011176,"identity":"3fdc38be-d74f-4bf7-8a94-11a45ceb0031","order_by":1,"name":"Siwar Chaabene","email":"","orcid":"","institution":"University of Sfax","correspondingAuthor":false,"prefix":"","firstName":"Siwar","middleName":"","lastName":"Chaabene","suffix":""},{"id":476011179,"identity":"b959643a-14d1-4a31-9b1d-d803196f834b","order_by":2,"name":"Bassem Bouaziz","email":"","orcid":"","institution":"University of Sfax","correspondingAuthor":false,"prefix":"","firstName":"Bassem","middleName":"","lastName":"Bouaziz","suffix":""},{"id":476011182,"identity":"11cff14c-95c5-4961-919c-bac4fde56b51","order_by":3,"name":"Lotfi Chaari","email":"","orcid":"","institution":"National Polytechnic Institute of Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Lotfi","middleName":"","lastName":"Chaari","suffix":""}],"badges":[],"createdAt":"2025-06-14 11:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6893576/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6893576/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88058255,"identity":"27e0c361-ee6d-46f8-9449-96a1fc419f05","added_by":"auto","created_at":"2025-08-01 00:31:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":476111,"visible":true,"origin":"","legend":"","description":"","filename":"SubmitMCIdetectionusingTransformerSpringer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6893576/v1_covered_f0727780-a716-44f7-935f-476742ea4bf5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MCI detection using transformers for EEG-HRV fusion","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":"MCI detection, EEG, HRV, Transformer, Cognitive CERAD task","lastPublishedDoi":"10.21203/rs.3.rs-6893576/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6893576/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe need for multi-modal data in detecting complex relationships between physiological processes to improve anomaly characterization becomes evident with the growing of medical records modalities. 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