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.
Abstract (translated)
动物模型，尤其是大鼠，在癫痫发生及治疗反应的发作研究中发挥着关键作用。然而，由于缺乏具有精确时序标注和标准化评估协议的数据集，相关研究进展受限。现有动物行为数据集通常存在可访问性有限、标注粗糙以及临床有意义事件的时序定位不足等问题。为应对这些局限，我们推出了RatSeizure——首个用于细粒度发作行为分析的公开基准数据集。该数据集包含经过标注的视频片段，标注内容涉及发作相关动作单元及时序边界，从而支持行为分类与时序定位双重任务。此外，我们提出了RaSeformer，一种用于时序动作定位的显著性上下文Transformer模型，该模型能够突出与行为相关的上下文信息，同时抑制冗余线索。在RatSeizure上的实验表明，RaSeformer取得了优异性能，并为这一具有挑战性的任务提供了有竞争力的参考模型。我们还建立了标准化的数据集划分方案与评估协议，以支持可复现的基准测试。
URL
https://arxiv.org/abs/2603.26780
