提出新模型sATAE-HFGCN，提升脑电图癫痫灶定位精度。
Shared Attention-based Autoencoder with Hierarchical Fusion-based Graph Convolution Network for sEEG SOZ Identification
- 通过共享注意力自编码器捕捉跨患者共性特征与电极间依赖关系。
- 在17例颞叶癫痫数据上，定位准确率显著优于现有方法。
- 适合神经外科与深度学习结合的研究者参考。
癫痫发作起始区（SOZ）的精确定位是神经外科的关键挑战，立体脑电图（sEEG）是重要手段。现有研究仅关注单个患者的癫痫特征表示，忽视了跨患者共性及电极点间的特征依赖关系。为此，本文提出共享注意力自编码器（sATAE），利用所有患者的数据训练，引入注意力模块增强特征元素间的依赖建模。考虑到sEEG在不同患者间的空间差异，采用图神经网络进行定位。但现有图方法依赖静态图建模癫痫网络。受神经科学启发，癫痫网络存在动态与稳定状态的复杂平衡，因此设计分层融合图卷积网络（HFGCN），通过多层级加权融合动态与静态特征，实现更全面的癫痫特征学习并丰富节点信息。将sATAE与HFGCN结合，在自建的17例颞叶癫痫sEEG数据集上进行实验，结果表明sATAE-HFGCN在识别每位患者的SOZ方面表现优异，有效解决了上述问题，为sEEG-based SOZ识别提供了高效方案。
原文摘要 · Abstract (English)
Diagnosing seizure onset zone (SOZ) is a challenge in neurosurgery, where stereoelectroencephalography (sEEG) serves as a critical technique. In sEEG SOZ identification, the existing studies focus solely on the intra-patient representation of epileptic information, overlooking the general features of epilepsy across patients and feature interdependencies between feature elements in each contact site. In order to address the aforementioned challenges, we propose the shared attention-based autoencoder (sATAE). sATAE is trained by sEEG data across all patients, with attention blocks introduced to enhance the representation of interdependencies between feature elements. Considering the spatial diversity of sEEG across patients, we introduce graph-based method for identification SOZ of each patient. However, the current graph-based methods for sEEG SOZ identification rely exclusively on static graphs to model epileptic networks. Inspired by the finding of neuroscience that epileptic network is intricately characterized by the interplay of sophisticated equilibrium between fluctuating and stable states, we design the hierarchical fusion-based graph convolution network (HFGCN) to identify the SOZ. HFGCN integrates the dynamic and static characteristics of epileptic networks through hierarchical weighting across different hierarchies, facilitating a more comprehensive learning of epileptic features and enriching node information for sEEG SOZ identification. Combining sATAE and HFGCN, we perform comprehensive experiments with sATAE-HFGCN on the self-build sEEG dataset, which includes sEEG data from 17 patients with temporal lobe epilepsy. The results show that our method, sATAE-HFGCN, achieves superior performance for identifying the SOZ of each patient, effectively addressing the aforementioned challenges, providing an efficient solution for sEEG-based SOZ identification.
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