Automated Detection of Epileptic EEG Signals using Recurrence Plots based Feature Extraction with Transfer Learning

Automated Detection of Epileptic EEG Signals using Recurrence Plots based Feature Extraction with Transfer 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 Automated Detection of Epileptic EEG Signals using Recurrence Plots based Feature Extraction with Transfer Learning Sachin Goel, Rajeev Agarwal, R.P. Bharti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2046441/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jun, 2023 Read the published version in Soft Computing → Version 1 posted 4 You are reading this latest preprint version Abstract “Epilepsy” is the common neurological brain disorder that is affects the human being at any stage of life. About 1-2% of the world’s population affected by this major chronic disorder. Electroencephalogram (EEG) signal is the most important tool for the early detection of the epileptic seizure in several applications of epilepsy diagnosis. Electroencephalogram (EEG) signals can be categorized in Epileptic and Non epileptic as per epilepsy seizures. Recent research has carried out various ppossibilities of predicting & analyzing epileptic seizures by mainly using two approaches: Conventional methods using signal processing and Deep learning based methods. Epilepsy Electroencephalogram Signals Recurrence plots Deep Neural Network Imaging Time Series data Full Text Cite Share Download PDF Status: Published Journal Publication published 04 Jun, 2023 Read the published version in Soft Computing → Version 1 posted Reviewers agreed at journal 24 Oct, 2022 Reviewers invited by journal 24 Oct, 2022 Editor assigned by journal 08 Sep, 2022 First submitted to journal 04 Oct, 2021 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. 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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-2046441","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":146529028,"identity":"91cc8789-3ada-443c-ba75-d8f87b709abe","order_by":0,"name":"Sachin Goel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYHACNoYEMMnA+ABI8/CRoIWZ2QCkhY0oLRDAzCaBzMUJ5NsPP3vwcAdDYh97/7HKrzl2MkDrHj66gUeLwZk0c4PEMwyJbTyH2W7LbksGOozN2DgHnxaGHDaJxDYGYzaJZLbbktuYgVp42KTxaZHvfwPVIv+YrVhyWz1hLQw3ILbIsUkwszF+3HaYsBaDG8/MgFok5Nh4ko2lGbcd52FjJuAX+f7kZ5I/22x45NsPPvz4c1u1PT9788PHeB0GAeAYYWDmAZOElSMA4w9SVI+CUTAKRsGIAQCRJzpw1AtiPAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8168-5915","institution":"Uttarakhand Technical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sachin","middleName":"","lastName":"Goel","suffix":""},{"id":146529029,"identity":"491601cb-7782-44e5-b498-fc468655a501","order_by":1,"name":"Rajeev Agarwal","email":"","orcid":"","institution":"GL Bajaj Institute of Technology and Management","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rajeev","middleName":"","lastName":"Agarwal","suffix":""},{"id":146529030,"identity":"9a298829-4da7-4868-b2c7-4f75d3a91340","order_by":2,"name":"R.P. 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