Seizure prediction method based on multi-domain feature and GATv2 network

doi:10.3785/j.issn.1008-973X.2026.08.015

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (8): 1760-1769 DOI: 10.3785/j.issn.1008-973X.2026.08.015

Seizure prediction method based on multi-domain feature and GATv2 network

Zhe HAN1,2(

),Qingfang MENG1,2,*(

),Qiang ZHANG3,Xianglong ZHANG1,2,Yaou ZHAO1,2

1. School of Information Science and Engineering, University of Jinan, Jinan 250022, China
2. Shandong Key Laboratory of Ubiquitous Intelligent Computing, University of Jinan, Jinan 250022, China
3. Jinan Jingheng Electronics Limited Company, Jinan 250014, China

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Abstract

To reduce seizure-related injury risks for seizure patients and optimize treatment strategies, an seizure prediction method based on multi-domain feature fusion and a graph attention network was proposed. Multi-dimensional features were extracted from the time domain, the frequency domain, and the complex network domain. A novel triple attention mechanism was employed for deep feature fusion, constructing node feature representations. Additionally, by modeling the spatial topological relationships between electrodes, an adjacency matrix was generated. The node feature matrix, together with the generated adjacency matrix, was then fed into a GATv2 model for deep analysis, achieving accurate seizure prediction. Experimental results on the CHB-MIT dataset demonstrate that the proposed method achieves a classification accuracy of 97.58% in seizure prediction tasks.

Key words： seizure prediction EEG complex network multi-domain feature graph attention network

Received: 18 July 2025 Published: 16 July 2026

CLC:

TP 391

Fund: 山东省自然科学基金资助项目（ZR2024MF124）；济南大学学科交叉融合建设项目 2023（XKJC-202308）；济南市高校创新团队项目（2019GXRC015）.

Corresponding Authors: Qingfang MENG E-mail: 202321100407@stu.ujn.edu.cn;ise_mengqf@ujn.edu.cn

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Cite this article:

Zhe HAN,Qingfang MENG,Qiang ZHANG,Xianglong ZHANG,Yaou ZHAO. Seizure prediction method based on multi-domain feature and GATv2 network. Journal of ZheJiang University (Engineering Science), 2026, 60(8): 1760-1769.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.08.015 OR https://www.zjujournals.com/eng/Y2026/V60/I8/1760

基于多域特征和GATv2网络的癫痫发作预测方法

为了降低癫痫患者的发作伤害风险并优化治疗策略，提出基于多域特征融合和图注意力网络的癫痫发作预测方法. 提取时域、频域和复杂网络域的特征，采用新颖的三元注意力机制对特征进行深度融合，构建节点特征表示. 建模电极间的空间拓扑关系生成邻接矩阵，节点特征矩阵与邻接矩阵联合输入GATv2模型进行深度分析，实现癫痫发作的精准预测. 基于CHB-MIT数据集的实验结果表明，所提方法在癫痫发作预测任务中的分类准确率达到97.58%.

关键词： 癫痫发作预测, 脑电图, 复杂网络, 多域特征, 图注意力网络

Fig.1 Overall workflow of proposed seizure prediction method

Tab.1 Statistical characteristics of time domain

Fig.2 Flowchart of weighted neighborhood field graph construction for EEG signal complex network

Fig.3 Structure diagram of triple attention mechanism

Tab.2 Evaluation results of proposed seizure prediction method on CHB-MIT dataset %

Tab.3 Evaluation results of proposed seizure prediction method on TUSZ dataset %

Tab.4 Cross-subject experimental results %

Tab.5 Comparison of evaluation results among different prediction methods %

Tab.6 Comparison of evaluation results among different learning methods %

Tab.7 Impact of multi-dimensional features on model performance %

Tab.8 Impact of decay parameters on model performance %

Tab.9 Module ablation study results %


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