A lightweight 1D-CNN-GRU model for epileptic seizure prediction

A lightweight 1D-CNN-GRU model for epileptic seizure prediction | 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 lightweight 1D-CNN-GRU model for epileptic seizure prediction Chunlei He, Peijun Ma, Jiangyi Shi, Chenxin Qu, Qingrong Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4681232/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 Epilepsy is one of the most common neurological disorders. Seizure prediction for patients with refractory epilepsy can alert patients to interventions and prevent many serious consequences. Aiming at the problem that most of the current epilepsy prediction algorithms are not suitable for hardware implementation into low-latency and low-power wearable or portable medical devices because of their high complexity and large number of parameters, this paper proposes a lightweight and hardware-friendly deep learning network, 1D-CNN-GRU model. The raw EEG data can be fed into the network for automatic feature extraction and classification after simple filtering and normalization. After fixed-point quantization and compression, the overall size of the model is only 6.955 KB. The proposed method has been evaluated on 23 samples from the scalp-EEG based CHB-MIT dataset provided by the Boston Children's Hospital-MIT. Experimental results demonstrate that the proposed model can achieve an average sensitivity of 94.63% and accuracy of 93.45% in the binary classification task of the pre-seizure 30 min signal and inter-seizure signal, and its lightweight feature fulfills the requirements for hardware implementation as a low-power, wearable epilepsy prediction medical device. Epilepsy prediction Hardware-friendly algorithm Convolutional neural network Gated Recirculation Unit Scalp EEG 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-4681232","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326159873,"identity":"bced7682-a140-4671-8d30-ce167ffb5fc2","order_by":0,"name":"Chunlei He","email":"","orcid":"","institution":"Xidian University","correspondingAuthor":false,"prefix":"","firstName":"Chunlei","middleName":"","lastName":"He","suffix":""},{"id":326159874,"identity":"e2aa6ced-efe2-454c-9100-bd19bf48ae7f","order_by":1,"name":"Peijun Ma","email":"","orcid":"","institution":"Xidian 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