NeuroGRIP：用于基于知识的脑电图癫痫诊断的检索增强图细化
NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis
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中文总结 AI 辅助
研究针对脑电图癫痫诊断难题，提出NeuroGRIP框架，结合外部医学知识校准脑电图图。通过构建知识库、利用大语言模型提取知识图，经对齐感知查询和相似性搜索检索关系证据，提升诊断准确性与可解释性，为临床诊断提供新框架。
中文摘要 AI 辅助
从脑电图信号中进行癫痫诊断是一项关键但持续具有挑战性的任务，因为神经动力学复杂且通道间建模存在虚假连接。虽然时空图神经网络（STGNNs）推进了脑电图脑网络表示学习，但由于其纯数据驱动性质，生成的图结构临床合理性低且可解释性有限。为此，我们引入了NeuroGRIP，一个检索增强图细化框架，它结合外部医学知识来校准有噪声的脑电图图。我们首先构建一个从权威临床指南派生的大规模、特定领域知识库。利用大语言模型，提取结构化生物医学实体和关系以形成文本知识图（KG），作为临床先验的外部知识源。我们的框架通过将STGNN生成的脑电图节点嵌入投影到KG的语义空间来执行对齐感知查询构建。然后通过基于FAISS的知识三元组相似性搜索执行语义查询以检索关系证据。根据检索到的相似性、关系类型和源可靠性为每个预测边分配置信度分数，使我们能够从原始预测图中修剪医学上不合理的边。在TUSZ和CHB - MIT上的大量实验表明，NeuroGRIP不仅提高了癫痫检测准确性，还通过将每个预测基于临床验证的知识增强了可解释性。这项工作提供了第一个通过检索增强推理将脑动力学与外部医学专业知识紧密结合的统一框架，为知识增强、可解释的临床诊断铺平了道路。代码可在：这个https URL获取。
英文摘要
Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling. While spatial-temporal graph neural networks (STGNNs) have advanced EEG brain network representation learning, the resulting graph structures suffer from low clinical plausibility and limited interpretability due to their purely data-driven nature. To this end, we introduce NeuroGRIP, a retrieval-augmented graph refinement framework that incorporates external medical knowledge to calibrate noisy EEG graphs. We first construct a large-scale, domain-specific knowledge base derived from authoritative clinical guidelines. Leveraging large language models, we extract structured biomedical entities and relations to form a textual knowledge graph (KG), which serves as external knowledge source of clinical priors. Our framework performs alignment-aware query construction by projecting STGNN-generated EEG node embeddings into the semantic space of KG. Semantic queries are then executed via FAISS-based similarity search over knowledge triplets to retrieve relation evidence. Each predicted edge is assigned a confidence score based on retrieved similarity, relation type, and source reliability, enabling us to prune medically implausible edges from the originally predicted graph. Extensive experiments on TUSZ and CHB-MIT demonstrate that NeuroGRIP not only improves seizure detection accuracy but also enhances interpretability by grounding each prediction in clinically validated knowledge. This work provides the first unified framework that tightly couples brain dynamics with external medical expertise via retrieval-augmented reasoning, paving the way for knowledge-enhanced, explainable clinical diagnosis. The code is available at: https://github.com/LincanLi-X/NeuroGRIP.
