EEG-based seizure detection models face challenges in terms of inference speed and memory efficiency, limiting their real-time implementation in clinical devices. This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis in applications such as epileptic seizure detection. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks. Notably, REST achieves a remarkable 9-fold acceleration in inference speed compared to state-of-the-art models, while simultaneously demanding substantially less memory than the smallest model employed for this task. These attributes position REST as a promising candidate for real-time implementation in clinical devices, such as Responsive Neurostimulation or seizure alert systems.

本研究纳入了一种新颖的基于图的残差状态更新机制（REST），用于实时脑电信号分析，如癫痫发作检测，通过利用图神经网络和循环结构，REST能够高效地捕捉脑电数据中的非欧几里得几何和时间依赖关系，显示出在发作检测和分类任务中高准确性，并与现有模型相比，推断速度提高了9倍，同时所需的内存大大减少，使REST成为用于临床设备实时应用（如响应性脑刺激或癫痫警报系统）的有前景的候选方法。

REST: 基于残余状态更新的高效加速的脑电图癫痫分析