Automated seizure detection from electroencephalography (EEG) remains difficult due to the large variability of seizure dynamics across patients, recording conditions, and clinical settings. We introduce LookAroundNet, a transformer-based seizure detector that uses a wider temporal window of EEG data to model seizure activity. The seizure detector incorporates EEG signals before and after the segment of interest, reflecting how clinicians use surrounding context when interpreting EEG recordings. We evaluate the proposed method on multiple EEG datasets spanning diverse clinical environments, patient populations, and recording modalities, including routine clinical EEG and long-term ambulatory recordings, in order to study performance across varying data distributions. The evaluation includes publicly available datasets as well as a large proprietary collection of home EEG recordings, providing complementary views of controlled clinical data and unconstrained home-monitoring conditions. Our results show that LookAroundNet achieves strong performance across datasets, generalizes well to previously unseen recording conditions, and operates with computational costs compatible with real-world clinical deployment. The results indicate that extended temporal context, increased training data diversity, and model ensembling are key factors for improving performance. This work contributes to moving automatic seizure detection models toward clinically viable solutions.

翻译：基于脑电图（EEG）的自动癫痫检测仍然面临挑战，这主要源于癫痫动态在不同患者、记录条件和临床环境中的巨大差异性。本文提出LookAroundNet，一种基于Transformer的癫痫检测器，它使用更宽的EEG数据时间窗口来建模癫痫活动。该检测器整合了目标片段前后的EEG信号，反映了临床医生在解读EEG记录时利用周围上下文的方式。我们在涵盖多种临床环境、患者群体和记录模式（包括常规临床EEG和长期动态记录）的多个EEG数据集上评估所提方法，以研究其在不同数据分布下的性能。评估数据集包括公开可用的数据集以及一个大型专有家庭EEG记录集合，从而提供了受控临床数据与非受约束家庭监测条件的互补视角。结果表明，LookAroundNet在各数据集上均表现出强劲性能，能够良好泛化至先前未见的记录条件，且计算成本符合实际临床部署要求。分析指出，扩展的时序上下文、增加训练数据多样性以及模型集成是提升性能的关键因素。此项工作为推动自动癫痫检测模型迈向临床可行解决方案做出了贡献。