首个精确定时标注的大鼠癫痫行为数据集，助力精准定位发作行为。
RatSeizure: A Benchmark and Saliency-Context Transformer for Rat Seizure Localization
- 提出基于注意力机制的上下文感知模型，聚焦关键行为线索。
- 在自建数据集上实现高精度发作行为定位，优于现有方法。
- 提供公开数据与标准评估协议，适合癫痫研究与行为分析者使用。
动物模型（尤其是大鼠）在癫痫发生机制研究和治疗反应评估中至关重要。然而，受限于缺乏精确的时间标注数据集和标准化评估协议，研究进展缓慢。现有动物行为数据集普遍存在访问受限、标注粗糙、临床意义事件时间定位不足等问题。为此，我们提出RatSeizure，首个面向细粒度癫痫行为分析的公开基准数据集。该数据集包含带发作相关动作单元及时间边界的视频片段，支持行为分类与时间定位任务。我们进一步提出RaSeformer，一种强调行为相关上下文、抑制冗余线索的显著性-上下文Transformer模型。在RatSeizure上的实验表明，RaSeformer表现优异，可作为该挑战性任务的有力参考模型。我们还建立了标准化的数据集划分与评估协议，以支持可复现的基准测试。
原文摘要 · Abstract (English)
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.
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