用图神经网络分析脑电波，预测中风严重程度。
Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals
- 构建五频段脑区连接图，用注意力机制学习关键神经通路。
- 在71例患者数据上，模型预测中风量表得分准确率显著提升。
- 可视化注意力权重，揭示中风后大脑重组规律，助力临床决策。
急性中风后，准确评估中风严重程度对治疗管理至关重要。已有研究发现，中风后脑功能连接会随频率变化发生重构。传统方法依赖手工特征，难以捕捉临床复杂性。本研究提出一种基于图神经网络（GNN）的新方法，利用71名住院患者脑电图（EEG）数据，按δ（2-4 Hz）、θ（4-8 Hz）、α₁（8-10.5 Hz）、α₂（10.5-13 Hz）、β₁（13-20 Hz）五个频段，以滞后线性相干性（LLC）加权生成脑区间连接图。为突出关键连接并保持稀疏性，采用基于脑网络结构与功能特性的稀疏化处理。训练图注意力模型预测美国国立卫生研究院中风量表（NIHSS）评分。通过分析注意力系数，模型揭示了大脑重配置机制，为临床诊断、个性化治疗及神经康复早期干预提供可解释工具。
原文摘要 · Abstract (English)
After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Graph theory methods have shown that brain connectivity undergoes frequency-dependent reorganization post-stroke, adapting to new conditions. Traditional methods often rely on handcrafted features that may not capture the complexities of clinical phenomena. In this study, we propose a novel approach using Graph Neural Networks (GNNs) to predict stroke severity, as measured by the NIH Stroke Scale (NIHSS). We analyzed electroencephalography (EEG) recordings from 71 patients at the time of hospitalization. For each patient, we generated five graphs weighted by Lagged Linear Coherence (LLC) between signals from distinct Brodmann Areas, covering $δ$ (2-4 Hz), $θ$ (4-8 Hz), $α_1$ (8-10.5 Hz), $α_2$ (10.5-13 Hz), and $β_1$ (13-20 Hz) frequency bands. To emphasize key neurological connections and maintain sparsity, we applied a sparsification process based on structural and functional brain network properties. We then trained a graph attention model to predict the NIHSS. By examining its attention coefficients, our model reveals insights into brain reconfiguration, providing clinicians with a valuable tool for diagnosis, personalized treatment, and early intervention in neurorehabilitation.
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