[Submitted on 15 Oct 2026]
Title:Seeing Seizures: A Large-Scale Empirical Study of Video and EEG Deep Learning for Automated Rodent Seizure Detection and Severity Grading
View a PDF of the paper titled Seeing Seizures: A Large-Scale Empirical Study of Video and EEG Deep Learning for Automated Rodent Seizure Detection and Severity Grading, by Alloy Das and 6 other authors
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Abstract:Manual annotation of synchronized video and EEG recordings is a bottleneck in preclinical seizure research. We present an empirical study of seizure detection and behavioral severity grading using 24,131 paired clips from 601 sessions across 20 rats in a soman-induced epilepsy model. Ten video architectures and seven single-channel EEG models are evaluated independently on binary detection and three- and five-class severity tasks. In the session-exclusive reference comparison, R(2+1)D-18 and GRU achieve detection macro-F1 scores of 0.968 and 0.974, respectively; for five-class grading, the corresponding scores are 0.657 and 0.560. Subject-exclusive evaluation reduces five-class macro-F1 to 0.475 for video and 0.404 for EEG, highlighting the difficulty of grading unseen animals. Temporal-sampling experiments demonstrate the value of multiple frames, while detection performance varies relatively little across the evaluated video architectures. A separate video-only binary-detection evaluation on RodEpil achieves a mean seizure-class F1 of 0.979 after uniform re-encoding to reduce identified class-correlated encoding cues. The study characterizes existing models for behavior-defined severity labels under the evaluated protocol, motivating further investigation of expert-assisted annotation workflows. Our code and github page is available here.
Comments: Under Review
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.22566 [cs.CV]
(or arXiv:2610.22566v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.22566
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Alloy Das [view email] [v1] Thu, 15 Oct 2026 19:11:05 UTC (9,820 KB)
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