癫痫发作-NGCLNet：通过节点-图双对比学习学习SEEG空间病理模式以检测药物抵抗性癫痫发作
Seizure-NGCLNet: Representation Learning of SEEG Spatial Pathological Patterns for Epileptic Seizure Detection via Node-Graph Dual Contrastive Learning
AI总结：
Seizure-NGCLNet通过节点-图双对比学习学习SEEG空间病理模式，实现高精度的药物抵抗性癫痫发作检测。
摘要：
复杂的空间连接模式，如期前抑制和发作传播，使使用立体电脑电图（SEEG）和传统机器学习方法准确检测药物抵抗性癫痫（DRE）发作变得复杂。仍然存在两个关键挑战：（1）功能性连接估计中的信噪比较低，使得学习与发作相关的相互作用困难；（2）专家对空间病理连接模式的标签难以获得，同时缺乏这些模式的表示以提高发作检测。为了解决这些问题，我们提出了一种新的节点-图双对比学习框架，Seizure-NGCLNet，用于学习SEEG期前抑制和发作传播模式，以高精度检测DRE发作。首先，开发了一种受中心性度量引导的自适应图增强策略，以生成与发作相关的脑网络。其次，集成了双对比学习方法，结合全局图级对比与局部节点-图对比，以编码空间结构和语义性癫痫发作特征。第三，通过顶部K局部图注意力网络对预训练嵌入进行微调以执行最终分类。在大规模公开SEEG数据集上进行的广泛实验表明，Seizure-NGCLNet实现了最先进的性能，平均准确率为95.93%，灵敏度为96.25%，特异度为94.12%。可视化证实，所学嵌入能够清晰地区分发作期和期前期状态，反映抑制和传播模式，对应临床机制。这些结果突显了Seizure-NGCLNet学习可解释空间病理模式的能力，增强了发作检测和发作起始区定位。
英文摘要：
Complex spatial connectivity patterns, such as interictal suppression and ictal propagation, complicate accurate drug-resistant epilepsy (DRE) seizure detection using stereotactic electroencephalography (SEEG) and traditional machine learning methods. Two critical challenges remain:(1)a low signal-to-noise ratio in functional connectivity estimates, making it difficult to learn seizure-related interactions; and (2)expert labels for spatial pathological connectivity patterns are difficult to obtain, meanwhile lacking the patterns' representation to improve seizure detection. To address these issues, we propose a novel node-graph dual contrastive learning framework, Seizure-NGCLNet, to learn SEEG interictal suppression and ictal propagation patterns for detecting DRE seizures with high precision. First, an adaptive graph augmentation strategy guided by centrality metrics is developed to generate seizure-related brain networks. Second, a dual-contrastive learning approach is integrated, combining global graph-level contrast with local node-graph contrast, to encode both spatial structural and semantic epileptogenic features. Third, the pretrained embeddings are fine-tuned via a top-k localized graph attention network to perform the final classification. Extensive experiments on a large-scale public SEEG dataset from 33 DRE patients demonstrate that Seizure-NGCLNet achieves state-of-the-art performance, with an average accuracy of 95.93%, sensitivity of 96.25%, and specificity of 94.12%. Visualizations confirm that the learned embeddings clearly separate ictal from interictal states, reflecting suppression and propagation patterns that correspond to the clinical mechanisms. These results highlight Seizure-NGCLNet's ability to learn interpretable spatial pathological patterns, enhancing both seizure detection and seizure onset zone localization.
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