分频段分析脑电图，用图神经网络提升癫痫发作检测精度与可解释性。
Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals
- 将脑电信号按五频段分解，提取11个特征并建模电极间空间关系。
- 在CHB-MIT数据集上达到99.01%总体准确率，中频段表现最优。
- 揭示了不同频段的癫痫特征差异，适合临床辅助诊断研究者参考。
癫痫发作是大脑异常过度电活动导致的神经疾病，表现为反复发作。脑电图（EEG）因其能捕捉神经活动的时间与空间动态，被广泛用于诊断。尽管近期深度学习方法已实现高检测准确率，但普遍存在可解释性差和神经生理相关性不足的问题。本研究提出一种基于发作期脑电信号的频率感知框架。原始EEG信号被分解为五个频段（delta、theta、alpha、下beta、上beta），每个频段提取11个判别性特征。采用图卷积神经网络（GCN）建模电极间的空间依赖关系，电极作为图节点。在CHB-MIT头皮EEG数据集上的实验表明，各频段准确率分别为97.1%、97.13%、99.5%、99.7%和51.4%，总体宽带准确率达99.01%。结果凸显中频段的强大判别能力，并揭示了频段特异性的发作模式。相比传统宽带方法，该方法提升了可解释性与诊断精度。
原文摘要 · Abstract (English)
Epileptic seizures are neurological disorders characterized by abnormal and excessive electrical activity in the brain, resulting in recurrent seizure events. Electroencephalogram (EEG) signals are widely used for seizure diagnosis due to their ability to capture temporal and spatial neural dynamics. While recent deep learning methods have achieved high detection accuracy, they often lack interpretability and neurophysiological relevance. This study presents a frequency-aware framework for epileptic seizure detection based on ictal-phase EEG analysis. The raw EEG signals are decomposed into five frequency bands (delta, theta, alpha, lower beta, and higher beta), and eleven discriminative features are extracted from each band. A graph convolutional neural network (GCN) is then employed to model spatial dependencies among EEG electrodes, represented as graph nodes. Experiments on the CHB-MIT scalp EEG dataset demonstrate high detection performance, achieving accuracies of 97.1%, 97.13%, 99.5%, 99.7%, and 51.4% across the respective frequency bands, with an overall broadband accuracy of 99.01%. The results highlight the strong discriminative capability of mid-frequency bands and reveal frequency-specific seizure patterns. The proposed approach improves interpretability and diagnostic precision compared to conventional broadband EEG-based methods.
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