GeronGnosis: A Database for Diagnosing Dementias Using EEG and Machine Learning

GeronGnosis: A Database for Diagnosing Dementias Using EEG and Machine Learning | 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 GeronGnosis: A Database for Diagnosing Dementias Using EEG and Machine Learning Ana Paula Silva de Oliveira, Camila Tiodista de Lima, Gabriel Miranda de Souza, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6792998/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Research on Biomedical Engineering → Version 1 posted 14 You are reading this latest preprint version Abstract Proposal:The aging population has led to an increase in chronic degenerative diseases, such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). Future projections indicate that this increase will be more pronounced, with cases tripling by 2025. Therefore, it is necessary to identify patterns of brain activity related to dementia to assist in earlier diagnosis. Objective: In this study, we propose to compare the performance of machine learning (ML) models across different databases in patients with dementia and elderly individuals using EEG data. Methods: We investigated various machine learning models using two distinct EEG databases: OpenNeuro (AD, FTD, and healthy controls) and GeronGnosis (mild cognitive impairment - MCI, AD, FTD, and healthy controls), the latter developed by the researchers and presented here for the first time. After extracting different signal attributes, we applied the Bayes Network, Random Tree, Random Forest, and SVM models and compared their best performances. Results: The Random Forest model with 500 trees demonstrated the best classification performance, achieving an accuracy of 98% on the GeronGnosis database, while the OpenNeuro database reached an accuracy of 95%. Conclusion: The GeronGnosis database was able to correctly classify patients with dementia and healthy controls with excellent performance across the studied metrics. Additionally, as a national and unprecedented database, it allows for better representation of the local population, helping to improve the clinical applicability of classification models. dementia machine learning EEG Computer-aided diagnosis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Research on Biomedical Engineering → Version 1 posted Editorial decision: Revision requested 09 Jan, 2026 Reviews received at journal 31 Dec, 2025 Reviewers agreed at journal 31 Dec, 2025 Reviews received at journal 30 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviewers invited by journal 15 Aug, 2025 Editor assigned by journal 04 Jul, 2025 Submission checks completed at journal 04 Jun, 2025 First submitted to journal 31 May, 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. 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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-6792998","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500975957,"identity":"3cee73e2-c103-44bb-ae03-94ce8f193850","order_by":0,"name":"Ana Paula Silva de Oliveira","email":"","orcid":"","institution":"Federal University of Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Paula Silva","lastName":"de Oliveira","suffix":""},{"id":500975958,"identity":"dd695cec-4dc6-4684-be5f-59ce645f26a5","order_by":1,"name":"Camila Tiodista de Lima","email":"","orcid":"","institution":"Federal University of 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Future projections indicate that this increase will be more pronounced, with cases tripling by 2025. Therefore, it is necessary to identify patterns of brain activity related to dementia to assist in earlier diagnosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObjective: In this study, we propose to compare the performance of machine learning (ML) models across different databases in patients with dementia and elderly individuals using EEG data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: We investigated various machine learning models using two distinct EEG databases: OpenNeuro (AD, FTD, and healthy controls) and GeronGnosis (mild cognitive impairment - MCI, AD, FTD, and healthy controls), the latter developed by the researchers and presented here for the first time. 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