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Abstract：
Epilepsy is a disease caused by abnormal neural discharge, which severely harms the health of patients. Its pathogenesis is complex and variable with various forms of seizures, leading to significant differences in epilepsy manifestations among different patients. The changes of brain network are strongly correlated with related pathologies. Therefore, it is crucial to effectively and deeply explore the intrinsic features of epilepsy signals to reveal the rules of epilepsy occurrence and achieve accurate detection. Existing methods have faced the following issues: 1) single approach for feature extraction, resulting in insufficient classification information due to the lack of rich dimensions in captured features; 2) inability to deeply analyze the essential commonality of epilepsy signal after feature extraction, making the model susceptible to data distribution and noise interference. Thus, we proposed a high-precision and robust model for epileptic seizure detection, which, for the first time, applies hypergraph convolution to the field of epilepsy detection. Through a hypergraph network structure constructed based on relationships between channels in electroencephalogram (EEG) signals, the model explores higher-order characteristics of epilepsy EEG data. Specifically, we use the Conv-LSTM module and Power spectral density (PSD), a two-branch parallel method, to extract channel features from space-time and frequency domains to solve the problem of insufficient feature extraction, and can adequately describe the data structure and distribution from multiple perspectives through double-branch parallel feature extraction. In addition, we construct a hypergraph on the captured features to explore the intrinsic features in the high-dimensional space in an attempt to reveal the essential commonality of epileptic signal feature extraction. Finally, using the ensemble learning concept, we accomplished epilepsy detection on the dual-branch hypergraph convolution. The model underwent leave-one-out cross-validation on the TUH dataset, achieving an average accuracy of 96.9%, F1 score of 97.3%, Pre of 98.2% and Re of 96.7%. In addition, the model was generalized performance tested on CHB-MIT scalp EEG dataset with leave-one-out cross-validation, and the average ACC, F1 score, Pre and Re were 94.4%, 95.1%, 95.8%, and 93.9% respectively. Experimental results indicate that the model outperforms related literature, providing valuable reference for the clinical application of epilepsy detection.
摘要：
癫痫是一种由异常神经放电引起的疾病，这严重损害了患者的健康。它的发病机制是复杂和多变的各种形式的癫痫发作，导致不同患者之间癫痫表现存在显著差异。脑网络的变化与相关病理密切相关。因此,有效深入地探究癫痫信号的内在特征,对揭示癫痫发生规律,实现准确检测至关重要。现有方法面临以下问题:1)单一的特征提取方法,由于捕获的特征缺乏丰富的维度,导致分类信息不足;2)特征提取后无法深入分析癫痫信号的本质共性,使得模型易受数据分布和噪声干扰的影响。因此，提出了一种高精度、鲁棒的癫痫发作检测模型,which,第一次,将超图卷积应用于癫痫检测领域。通过基于脑电图（EEG）信号中通道之间的关系构建的超图网络结构，该模型探索了癫痫脑电数据的高阶特征。具体来说,我们使用Conv-LSTM模块和功率谱密度(PSD)，两分支并行方法，从空域和频域提取信道特征,解决特征提取不足的问题,通过双分支并行特征提取，可以从多个角度充分描述数据结构和分布。此外,我们在捕获的特征上构造超图，以探索高维空间中的内在特征，试图揭示癫痫信号特征提取的本质共性。最后,使用合奏学习概念，我们在双分支超图卷积上完成了癫痫的检测。该模型在TUH数据集上进行了留一法交叉验证，平均准确率为96.9%，F1得分为97.3%，Pre为98.2%，Re为96.7%。此外,该模型在CHB-MIT头皮脑电图数据集上进行了广义性能测试，具有留一交叉验证，和平均ACC，F1得分,Pre和Re分别为94.4%，95.1%,95.8%,分别为93.9%。实验结果表明，该模型优于相关文献，为癫痫检测的临床应用提供有价值的参考。
