基于特征分析和图卷积神经网络（GCN）的脑电图（EEG）信号癫痫发作检测在不同频段的研究
Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals
AI总结：
提出一种频率感知框架，将EEG分解为五个频段并提取判别特征，利用图卷积神经网络建模电极空间依赖，在CHB-MIT数据集上实现99.01%的宽带准确率，提高了可解释性和诊断精度。
摘要：
癫痫发作是一种神经系统疾病，其特征是大脑中异常和过度的电活动，导致反复发作事件。脑电图（EEG）信号因其能够捕捉时间和空间的神经动力学而被广泛用于癫痫诊断。虽然最近的深度学习方法取得了高检测准确率，但它们往往缺乏可解释性和神经生理学相关性。本研究提出了一种基于发作期EEG分析的频率感知框架用于癫痫发作检测。原始EEG信号被分解为五个频段（delta、theta、alpha、低beta和高beta），并从每个频段提取十一个判别特征。然后采用图卷积神经网络（GCN）对EEG电极之间的空间依赖性进行建模，电极表示为图节点。在CHB-MIT头皮EEG数据集上的实验表明，该方法在相应频段上分别达到了97.1%、97.13%、99.5%、99.7%和51.4%的准确率，总体宽带准确率为99.01%。结果突出了中频段的强判别能力，并揭示了特定频率的发作模式。与传统的宽带EEG方法相比，所提出的方法提高了可解释性和诊断精度。
英文摘要：
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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