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

Ting Yu Tsai, An Yu, Lucy Lee, Felix X. -F. Ye, Damian S. Shin, Tzu-Jen Kao, Xin Li, Ming-Ching Chang

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.

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

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.

Paper Structure

This paper contains 6 sections, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. The RatSeizure Benchmark Dataset
  4. RaSeformer: Processing and Architecture
  5. Experimental Validation and Results
  6. Conclusion

Figures (2)

  • Figure 1: RatSeizure dataset composition and split distribution: (a) Concentric donut chart showing behavioral class frequencies before (inner ring) and after (outer ring) data augmentation, with example frames from four action categories annotated with temporal boundaries. (b) Label distribution across training and test splits after augmentation, showing the relative frequency (%) of each behavioral category and a consistent distribution with $\sim 65:35$ split ratio across categories.
  • Figure 2: The RaSeformer Pipeline: In phase 1 (a-c), raw rat videos are first processed through temporal segmentation, mirror artifact masking, and YOLO-based ROI cropping to produce standardized input tensors. These tensors are encoded by an I3D backbone into spatiotemporal feature sequences (phase 2). In the final phase, the sequences pass through the Salient Context Transformer Encoder, which uses per-head Top-$K$ pruning to focus on salient behavioral cues. A decoder then fuses the encoded features, with parallel heads for temporal boundary regression and behavior classification.