通过时空图学习分析脑电数据，精准定位癫痫发作起始区并解释传播过程。
SpaTeoGL: Spatiotemporal Graph Learning for Interpretable Seizure Onset Zone Analysis from Intracranial EEG
- 构建时空联合图模型，捕捉电极间互动与时间窗口关联。
- 在多中心数据上表现优于基线方法，非发作区识别准确率更高。
- 结果可解释，适合临床医生和神经科学研究者使用。
从颅内脑电（iEEG）中精确定位癫痫发作起始区（SOZ）对癫痫手术至关重要，但复杂的时空发作动态带来挑战。本文提出 SpaTeoGL，一种用于可解释发作网络分析的时空图学习框架。该框架联合学习窗级空间图（捕捉电极间相互作用）和时序图（基于空间结构相似性连接时间窗）。方法基于平滑图信号处理框架，采用交替块坐标下降算法求解，并具有收敛性保证。在包含成功手术结果的多中心iEEG数据集上的实验表明，SpaTeoGL性能媲美基于水平可视图与逻辑回归的基线方法，同时提升了非SOZ区域的识别能力，并提供了关于发作起始与传播动态的可解释洞察。
原文摘要 · Abstract (English)
Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics. We propose SpaTeoGL, a spatiotemporal graph learning framework for interpretable seizure network analysis. SpaTeoGL jointly learns window-level spatial graphs capturing interactions among iEEG electrodes and a temporal graph linking time windows based on similarity of their spatial structure. The method is formulated within a smooth graph signal processing framework and solved via an alternating block coordinate descent algorithm with convergence guarantees. Experiments on a multicenter iEEG dataset with successful surgical outcomes show that SpaTeoGL is competitive with a baseline based on horizontal visibility graphs and logistic regression, while improving non-SOZ identification and providing interpretable insights into seizure onset and propagation dynamics.
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