Epilepsy is a common neurological disorder that affects around 65 million people worldwide. Detecting seizures quickly and accurately is vital, given the prevalence and severity of the associated complications. Recently, deep learning-based automated seizure detection methods have emerged as solutions; however, most existing methods require extensive post-processing and do not effectively handle the crucial long-range patterns in EEG data. In this work, we propose SeizureTransformer, a simple model comprised of (i) a deep encoder comprising 1D convolutions (ii) a residual CNN stack and a transformer encoder to embed previous output into high-level representation with contextual information, and (iii) streamlined decoder which converts these features into a sequence of probabilities, directly indicating the presence or absence of seizures at every time step. Extensive experiments on public and private EEG seizure detection datasets demonstrate that our model significantly outperforms existing approaches (ranked in the first place in the 2025 "seizure detection challenge" organized in the International Conference on Artificial Intelligence in Epilepsy and Other Neurological Disorders), underscoring its potential for real-time, precise seizure detection.

翻译：癫痫是一种常见的神经系统疾病，全球约影响6500万人。鉴于其高发病率及相关并发症的严重性，快速准确地检测癫痫发作至关重要。近年来，基于深度学习的自动化癫痫检测方法已成为解决方案；然而，现有方法大多需要大量后处理，且未能有效处理脑电数据中关键的长程模式。本研究提出SeizureTransformer，该简洁模型包含：（i）由一维卷积构成的深度编码器；（ii）残差CNN堆栈与Transformer编码器，用于将先前输出嵌入至包含上下文信息的高层表示；（iii）流线型解码器，将这些特征转换为概率序列，直接指示每个时间步是否存在癫痫发作。在公开及私有脑电癫痫检测数据集上的大量实验表明，本模型显著优于现有方法（在2025年“国际癫痫与其他神经系统疾病人工智能会议”组织的“癫痫检测挑战赛”中位列第一），凸显了其在实时精准癫痫检测中的应用潜力。