Deep Learning-Based EEG Seizure Classification Using Comparative Time–Frequency Spectral Representations

Deep Learning-Based EEG Seizure Classification Using Comparative Time–Frequency Spectral Representations | 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 Deep Learning-Based EEG Seizure Classification Using Comparative Time–Frequency Spectral Representations Siddartha K M, Sriram Kumar P, Rajamanickam YUVARAJ, John THOMAS, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9156412/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 22 You are reading this latest preprint version Abstract The automated prediction and classification of seizures using electroencephalography (EEG) remains a challenging task due to the inherent complexity and significant inter-subject variability observed in seizure patterns. To address these challenges, we explored the use of two-dimensional time-frequency representations of EEG signals to enhance the effectiveness of seizure type classification. In this study, we employed EEG data corresponding to background, focal, and generalized seizures, sourced from the Temple University Hospital (TUH v2.0.1) dataset. The EEG signals were first preprocessed and segmented. Each segment was then transformed into a two-dimensional time-frequency image using three techniques: Short-Time Fourier Transform (STFT), Mel-Frequency Cepstrum (MFC), and Continuous Wavelet Transform (CWT). We developed and evaluated five deep learning architectures for seizure classification: AlexNet, VGGNet, configurable Convolutional Neural Network (cCNN), configurable Recurrent Neural Network (cRNN), and configurable Long Short-Term Memory (cLSTM). All models were assessed using 10-fold cross-validation and evaluated across standard performance metrics such as accuracy, precision, recall, and F1-score. Experimental results showed that STFT-based representations with cCNN achieved an accuracy of 83.33%, while MFC spectrograms with cCNN yielded 76.31%. The highest classification performance, an accuracy of 85.17% was obtained using CWT scalograms with the cRNN model. In conclusion, the combination of CWT-based time-frequency features with the cRNN model proved to be most effective for the classification of seizures type. This study underscores the potential of deep learning and time-frequency analysis in developing reliable EEG-based diagnostic tools for improved clinical decision-making in epilepsy care. Epilepsy Seizure type classification Electroencephalography Time-Frequency representation Deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 13 May, 2026 Reviews received at journal 05 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 02 May, 2026 Reviews received at journal 02 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 18 Mar, 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. 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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-9156412","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633770232,"identity":"e2a9968a-6fd0-4674-ad84-9a313cec1d13","order_by":0,"name":"Siddartha K M","email":"","orcid":"","institution":"Indian Institute of Technology (BHU) Varanasi","correspondingAuthor":false,"prefix":"","firstName":"Siddartha","middleName":"K","lastName":"M","suffix":""},{"id":633770233,"identity":"6c768345-9675-4722-a958-2ea9beffb672","order_by":1,"name":"Sriram Kumar 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