A Multifractal-Guided Machine Learning Framework for Late Post-Traumatic Seizure Prediction Following Hemorrhagic Traumatic Brain Injury

A Multifractal-Guided Machine Learning Framework for Late Post-Traumatic Seizure Prediction Following Hemorrhagic Traumatic Brain Injury | 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 A Multifractal-Guided Machine Learning Framework for Late Post-Traumatic Seizure Prediction Following Hemorrhagic Traumatic Brain Injury Daria Riabukhina, Kseniia Kriukova, Paul M. Vespa, Manuel B. Blanco, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8613721/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 Traumatic brain injury can lead to post-traumatic epilepsy, yet early, reliable biomarkers to predict its emergence remain elusive. By investigating the multifractal characteristics of electroencephalogramrecordings from the first available day post-injury, we develop for the first time a machine learning framework that distinguishes between traumatic brain injury patients who develop late post-traumatic seizures and those who do not. Statistical analysis demonstrates statistically significant differences in multifractal properties of EEG signals between patients who develop late post-traumatic seizures and patients who do not. We show that random forest classifier trained on multi-fractal properties of EEG achieve a high predictive accuracy (95%) and area under the curve (98%) for predicting late PTS. The predictive power of multifractal features was robust to sample length and electrode selection. Our findings indicate that multifractal properties of EEG offers a promising, objective approach to early risk stratification for post-traumatic epilepsy in neurocritical care settings. Full Text Additional Declarations The authors declare no competing interests. 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. 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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-8613721","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575265815,"identity":"40b64434-2a76-47d1-99ee-7a7d2627e728","order_by":0,"name":"Daria Riabukhina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACZgY2BoYDB3iATAMQkgMJHnhAihZjsJYE/PaAtTBAtDAwJDaASHxazNuZnz3mOXNHxpz98MbPFQV26fPDDj8E2mInp9uAXYvMYTZzY54bz3gse9KKJc8YJOduvJ1mANSSbGx2ALsWCWYGM2meD4d5DA7kGEg2GDDnbpydANJyIHEbTi3s3yBazr8x/tlgUJ9uODv9AwEtPEBbbgC13MgxA9pyOEFeOoeQLTzlhnPOgLQ8K7NsMDhuuEE6p+BAggEev/Af3/bgzbHD9gbnkzffbPhTLS8/O33zhw8VdnK4tGACA7BKA2KVg4B8AymqR8EoGAWjYCQAAD0FYmADprSCAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Daria","middleName":"","lastName":"Riabukhina","suffix":""},{"id":575265816,"identity":"221d442b-f67d-48f4-b4a6-2eb876cf6ecd","order_by":1,"name":"Kseniia Kriukova","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Kseniia","middleName":"","lastName":"Kriukova","suffix":""},{"id":575265817,"identity":"1a52ff87-a5f5-4600-b7ca-ad3578168990","order_by":2,"name":"Paul M. 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By investigating the multifractal characteristics of electroencephalogramrecordings from the first available day post-injury, we develop for the first time a machine learning framework that distinguishes between traumatic brain injury patients who develop late post-traumatic seizures and those who do not. Statistical analysis demonstrates statistically significant differences in multifractal properties of EEG signals between patients who develop late post-traumatic seizures and patients who do not. We show that random forest classifier trained on multi-fractal properties of EEG achieve a high predictive accuracy (95%) and area under the curve (98%) for predicting late PTS. The predictive power of multifractal features was robust to sample length and electrode selection. 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