RatSeizure: A Benchmark and Saliency-Context Transformer for Rat Seizure Localization

TL;DR

RatSeizure introduces a new dataset and a Transformer-based model for precise rat seizure behavior analysis, enabling better localization and classification of seizure events.

Contribution

The paper provides the first publicly available benchmark dataset with detailed annotations and proposes RaSeformer, a novel saliency-context Transformer for seizure localization.

Findings

RaSeformer achieves strong performance on RatSeizure.

The dataset enables behavior classification and temporal localization.

Standardized protocols support reproducible benchmarking.

Abstract

Animal models, particularly rats, play a critical role in seizure research for studying epileptogenesis and treatment response. However, progress is limited by the lack of datasets with precise temporal annotations and standardized evaluation protocols. Existing animal behavior datasets often have limited accessibility, coarse labeling, and insufficient temporal localization of clinically meaningful events. To address these limitations, we introduce RatSeizure, the first publicly benchmark for fine-grained seizure behavior analysis. The dataset consists of recorded clips annotated with seizure-related action units and temporal boundaries, enabling both behavior classification and temporal localization. We further propose RaSeformer, a saliency-context Transformer for temporal action localization that highlights behavior-relevant context while suppressing redundant cues. Experiments on RatSeizure show that RaSeformer achieves strong performance and provides a competitive reference model for this challenging task. We also establish standardized dataset splits and evaluation protocols to support reproducible benchmarking.