Robust Epileptic Diagnosis Using EMD Extractor and Voting-Hybrid with Optimization algorithms

Robust Epileptic Diagnosis Using EMD Extractor and Voting-Hybrid with Optimization algorithms | 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 Article Robust Epileptic Diagnosis Using EMD Extractor and Voting-Hybrid with Optimization algorithms Abdullah S Almalaise Alghamdi, Rana Alrawashdeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8894706/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The neurological disorder epilepsy affects millions of people worldwide, so early, accurate diagnosis remains essential for effective treatment and improved quality of life. The diagnosis of epileptic seizures through EEG signals faces significant challenges because brain activity demonstrates non-stationary characteristics and high dimensionality. Current diagnostic approaches rely on fixed feature extraction techniques and single optimization methods, yet these methods do not perform well across different patients. In our work, we will present an automated epileptic diagnosis system that starts by extracting statistical and nonlinear features from different combinations of the Bonn EEG signals dataset through Empirical Mode Decomposition (EMD). Then we will test four optimization methods, like Snake Optimization (SOA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), and conduct independent feature selection for the most relevant features from these three methods. Additionally, a voting-based decision system counts feature frequencies, identifying features that appear in two or more of the previous three algorithms. Finally, we test three classifiers, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), to classify the signals and give the final accuracy. The proposed system achieved high performance across all classifiers. Extensive experiments conducted on multiple binary and multiclass combinations of the Bonn EEG dataset demonstrate the effectiveness of the proposed framework. When combined with PSO, the KNN classifier achieves the highest overall performance, reaching an average accuracy of 96.15%, and a specificity of 97.61%. The Random Forest classifier also exhibits strong, stable performance, particularly when optimized using the GA, achieving average accuracies of 92.94%, 93.18%, and 96.98% for sensitivity, specificity, and overall accuracy, respectively. SVM shows comparatively lower performance, with PSO yielding average accuracies of 87.56% and 93.56% for specificity. Although the SOA delivers competitive results across several binary and moderately complex cases, its performance declines in higher-dimensional multiclass scenarios. The proposed voting-based hybrid metaheuristic selector improves feature selection robustness in certain configurations but demonstrates inconsistent performance in complex classification tasks. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Supplementary Files RanaThesisdesertationV31.zip DrAbdullahEEGPaper12.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 May, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 24 Feb, 2026 Editor invited by journal 24 Feb, 2026 Submission checks completed at journal 19 Feb, 2026 First submitted to journal 19 Feb, 2026 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-8894706","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":596347506,"identity":"36439c37-bc5e-42ff-b82a-15a1cf583f42","order_by":0,"name":"Abdullah S Almalaise Alghamdi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYFACHiBmk5BjOABmkKDFmGQtDIkNRGvh71978NONMov0vuNnDzB8KDvMIN9+AL8WiRvvkqVzzknkzjyTl8A449xhBoMzCQSsuXHGQDq3TSJ3w4EcA2beNqAWBgJa5G+cMf4N1JJucP6NAfNfoBb5/gf4tRic7zED2ZJgcANoCyNQC8MNArYY3uAxswb6xXDmjTcGB3vOpfMY3CBgi9z5M8a3c8rq5PnO5xg++FFmLSffT8AWBgkkBQcYINFEAPAfIKxmFIyCUTAKRjgAAFLUR/QuTfUoAAAAAElFTkSuQmCC","orcid":"","institution":"Dar Alhekma University","correspondingAuthor":true,"prefix":"","firstName":"Abdullah","middleName":"S Almalaise","lastName":"Alghamdi","suffix":""},{"id":596347507,"identity":"316e297d-b585-4642-a653-c5e067ca0985","order_by":1,"name":"Rana Alrawashdeh","email":"","orcid":"","institution":"King Fahd University of Petroleum and Minerals","correspondingAuthor":false,"prefix":"","firstName":"Rana","middleName":"","lastName":"Alrawashdeh","suffix":""}],"badges":[],"createdAt":"2026-02-16 16:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8894706/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8894706/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103618618,"identity":"9e0501da-f73c-4126-8304-9febf089d5f0","added_by":"auto","created_at":"2026-02-27 17:29:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":573628,"visible":true,"origin":"","legend":"","description":"","filename":"DrAbdullahEEGPaper118.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8894706/v1_covered_ef54dc8d-e1d6-4f22-a1a1-e00a9ff7bfd9.pdf"},{"id":103618617,"identity":"440b29d9-cc4b-427a-874d-8ee45128f8d2","added_by":"auto","created_at":"2026-02-27 17:29:42","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12032518,"visible":true,"origin":"","legend":"","description":"","filename":"RanaThesisdesertationV31.zip","url":"https://assets-eu.researchsquare.com/files/rs-8894706/v1/92e300495480483f32b2d0c3.zip"},{"id":103618616,"identity":"f6b00305-6d61-46a0-ae8e-9c78d795a640","added_by":"auto","created_at":"2026-02-27 17:29:41","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1338650,"visible":true,"origin":"","legend":"","description":"","filename":"DrAbdullahEEGPaper12.zip","url":"https://assets-eu.researchsquare.com/files/rs-8894706/v1/f91821bcfaca4c1de94a6186.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Epileptic Diagnosis Using EMD Extractor and Voting-Hybrid with Optimization algorithms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8894706/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8894706/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The neurological disorder epilepsy affects millions of people worldwide, so early, accurate diagnosis remains essential for effective treatment and improved quality of life. The diagnosis of epileptic seizures through EEG signals faces significant challenges because brain activity demonstrates non-stationary characteristics and high dimensionality. Current diagnostic approaches rely on fixed feature extraction techniques and single optimization methods, yet these methods do not perform well across different patients. In our work, we will present an automated epileptic diagnosis system that starts by extracting statistical and nonlinear features from different combinations of the Bonn EEG signals dataset through Empirical Mode Decomposition (EMD). Then we will test four optimization methods, like Snake Optimization (SOA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), and conduct independent feature selection for the most relevant features from these three methods. Additionally, a voting-based decision system counts feature frequencies, identifying features that appear in two or more of the previous three algorithms. 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