Poster

Seizure-Semiology-Suite($S^3$): A Clinically Multimodal Dataset, Benchmark, and Models for Seizure Semiology Understanding

Lina Zhang ⋅ Jiarui Cui ⋅ Tonmoy Monsoor ⋅ Peizheng Li ⋅ Xinyi Peng ⋅ Chong Han ⋅ Prateik Sinha ⋅ Siyuan Dai ⋅ Jessica Pasqua ⋅ Colin McCrimmon ⋅ Weiting Liu ⋅ Hailey Miranda ⋅ Bing Hu ⋅ Xiangting Wu ⋅ Tengyou Xu ⋅ Chunhan Li ⋅ Jiaye Tian ⋅ Jiarui Tang ⋅ Detao Ma ⋅ Lingye Kong ⋅ Junnan Lyu ⋅ Jungang Li ⋅ Yan Zan ⋅ Junhua Huang ⋅ Rajarshi Mazumder ⋅ Vwani Roychowdhury

Abstract

While Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in general video understanding, their capacity to interpret involuntary, and spatio-temporally evolving pathologic motor behaviors such as seizure semiology remains largely untested. To address this gap, we introduce Seizure-Semiology-Suite (S³), a clinically grounded dataset and benchmark for fine-grained, structured seizure semiology understanding. The dataset includes 438 seizure videos annotated with over 35,000 dense labels covering 20 ILAE-defined semiological features. Building on this dataset, we propose a seven-task hierarchical benchmark that systematically evaluates MLLMs from low-level visual perception to temporal sequencing, narrative report generation, and seizure diagnosis. To enable clinically meaningful evaluation of generated reports, we further introduce the Report Quality Index for Seizure Semiology (Seizure-RQI). Extensive baselines across 11 open-weight MLLMs reveal systematic weaknesses in laterality reasoning, temporal localization, symptom sequencing, and clinically faithful reporting. We show that seizure-specific fine-tuning substantially improves performance across tasks, and that a two-stage neuro-symbolic framework achieves an F1 score of 0.96 on epileptic versus non-epileptic seizure classification. Seizure-Semiology-Suite establishes a rigorous benchmark for evaluating multimodal models in safety-critical medical video understanding and guides the development of clinically reliable, domain-adaptive multimodal intelligence.