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arXiv · 2603.23757

Learning Cross-Joint Attention for Generalizable Video-Based Seizure Detection

Omar Zamzam·Takfarinas Medani·Chinmay Chinara·Richard Leahy
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Abstract

Automated seizure detection from long-term clinical videos can substantially reduce manual review time and enable real-time monitoring. However, existing video-based methods often struggle to generalize to unseen subjects due to background bias and reliance on subject-specific appearance cues. We propose a joint-centric attention model that focuses exclusively on body dynamics to improve cross-subject generalization. For each video segment, body joints are detected and joint-centered clips are extracted, suppressing background context. These joint-centered clips are tokenized using a Video Vision Transformer (ViViT), and cross-joint attention is learned to model spatial and temporal interactions between body parts, capturing coordinated movement patterns characteristic of seizure semiology. Extensive cross-subject experiments show that the proposed method consistently outperforms state-of-the-art CNN-, graph-, and transformer-based approaches on unseen subjects.

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Omar Zamzam, Takfarinas Medani, Chinmay Chinara, Richard Leahy. 2026-03-24. Learning Cross-Joint Attention for Generalizable Video-Based Seizure Detection. https://arxiv.org/abs/2603.23757

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