Detection & Prediction of Epileptic Seizures Using Machine Learning Model on 3600s Long EEG data

Detection & Prediction of Epileptic Seizures Using Machine Learning Model on 3600s Long EEG data | 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 Detection & Prediction of Epileptic Seizures Using Machine Learning Model on 3600s Long EEG data Noore Zahra, Hanan AlJuaid, Shatha Saqer Almutairi, Nouf Khalid Alfouzan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7798462/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 Analysis of epileptic seizure detection and prediction may significantly improve the lives of people living with epilepsy. As the analysis helps to understand the underlying causes of seizures, detection and prediction may help to prevent mishaps and injuries by providing more effective treatment. This paper presents detection and prediction in three stages using the CHB-MIT scalp EEG database. The first stage explores the spectral features of delta, theta, alpha, beta, and gamma bands using wavelet transform and identifies the frequency range of seizure occurrences. The second stage comprises feature extraction, which helps to identify specific patterns and changes in brain activity. One hour EEG recordings of 45 subjects were considered to gain more information about the temporal dynamics of epileptic seizures. In the third stage, machine learning models (SVM, RF, and KNN) were used to classify the data into seizure and non-seizure. Performance was evaluated for training and testing in terms of TPR, NPR, PPV, f-score, and accuracy. Finally, the results and comparison with other recent published work demonstrate the efficacy of the proposed technique for predicting seizures. EEG Seizure prediction SVM KNN RF 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. 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-7798462","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550090453,"identity":"99fc25d8-b7ca-4a55-b738-31323ab8dc64","order_by":0,"name":"Noore 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data","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":"EEG, Seizure prediction, SVM, KNN, RF","lastPublishedDoi":"10.21203/rs.3.rs-7798462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7798462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnalysis of epileptic seizure detection and prediction may significantly improve the lives of people living with epilepsy. As the analysis helps to understand the underlying causes of seizures, detection and prediction may help to prevent mishaps and injuries by providing more effective treatment. This paper presents detection and prediction in three stages using the CHB-MIT scalp EEG database. The first stage explores the spectral features of delta, theta, alpha, beta, and gamma bands using wavelet transform and identifies the frequency range of seizure occurrences. The second stage comprises feature extraction, which helps to identify specific patterns and changes in brain activity. One hour EEG recordings of 45 subjects were considered to gain more information about the temporal dynamics of epileptic seizures. In the third stage, machine learning models (SVM, RF, and KNN) were used to classify the data into seizure and non-seizure. Performance was evaluated for training and testing in terms of TPR, NPR, PPV, f-score, and accuracy. Finally, the results and comparison with other recent published work demonstrate the efficacy of the proposed technique for predicting seizures.\u003c/p\u003e","manuscriptTitle":"Detection \u0026amp; Prediction of Epileptic Seizures Using Machine Learning Model on 3600s Long EEG data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 17:28:40","doi":"10.21203/rs.3.rs-7798462/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"297533ef-536f-4742-a06e-c4a60e637341","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T08:55:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 17:28:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7798462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7798462","identity":"rs-7798462","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}