ArticleIEEE transactions on bio-medical engineering2026
AI-Driven Electrographic Seizure Classification and Seizure Onset Detection Using Image- and Time-Series-Based Approaches.
Article in IEEE transactions on bio-medical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.BioData mining · 2026Article
- Proceedings of the 13th annual deep brain stimulation think tank: the evolving landscape.Frontiers in human neuroscience · 2026Review
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11 authors.
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Abstract
objectiveManually distinguishing between seizure and non-seizure events in intracranial electroencephalography (iEEG) recordings is highly time-consuming. In this study, we explored AI-based approaches for electrographic seizure classification (ESC) and seizure onset detection (SOD) in treatment-resistant epilepsy patients. ESC involves distinguishing seizure events from non-seizure activity, while SOD focuses on pinpointing the exact moment a seizure begins.
methodsWe assessed several image-based and time-series-based model architectures for ESC and SOD, including convolutional neural networks (CNNs), vision transformers (ViTs), and time-series transformers. We used a dataset of approximately 560,000 iEEG traces from 291 focal epilepsy patients implanted with the NeuroPace RNS system. We conducted extensive experimentation, varying the number of trainable parameters, input formats, and weight initializations to assess performance.
resultsViTs showed superior performance across both ESC and SOD, outperforming other models. For ESC, ViTs achieved a mean seizure classification accuracy of 97$\%$ across five folds of data, and an accuracy of 96$\%$ on a separate clinician-annotated test dataset. In the SOD task, ViTs showed an average median absolute error (MAE) of 1.4 seconds between model-predicted and human-labeled seizure onsets across five folds, and an MAE of 0.8 s on the separate clinician-annotated test dataset.
conclusionThese findings highlight the advantage of image-based AI approaches, particularly ViTs, in capturing nuanced seizure patterns from iEEG data. SIGNIFICANCE: AI-driven ESC and SOD could support the development of more personalized treatment strategies for epilepsy patients while reducing the time required for manual review.
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