DynSeizureGAT: Multi-band Dynamic Graph Attention Network for Interpretable Seizure Detection and Analysis of Drug-Resistant Epilepsy Using SEEG.

Journal: IEEE journal of biomedical and health informatics
Published Date: Jun 20, 2025

Abstract

The dynamic propagation of epileptic discharges complicates Drug-Resistant Epilepsy (DRE) seizure detection using traditional machine learning methods and Stereotactic Electroencephalography (SEEG). Several challenges remain unresolved in prior studies: (1) incomprehensive representations of epileptic brain network features; (2) lacking of flexible and dynamic mechanisms to learn brain network evolving features; and (3) the absence of model mechanisms interpretation corresponds with seizure mechanisms. In response, we propose a novel multi-band dynamic graph attention network, DynSeizureGAT, to detect and analyze DRE seizures with precision and interpretability. Specifically, a seizure network sequence is first constructed by integrating a multi-band directed transfer function matrix and enhanced epileptic index node features. Second, a dynamic graph attention module is integrated to dynamically weigh the contribution of various spatial scales. Third, spatial-spectral-temporal attention mechanisms enhance the model's capacity to better characterize and interpret the ictal and interictal states. Extensive experiments are conducted on the large-scale public clinical SEEG dataset (OpenNeuro). The proposed model demonstrates high seizure detection performance, achieving an average of 94.6% accuracy, 93.4% sensitivity, and 96.4% specificity. In addition, the importance of frequency bands and dynamic abnormal connectivity patterns is successfully quantified and visualized, which contributes most to the explainability. Experimental results indicate that DynSeizureGAT demonstrates strong dynamic propagation feature learning capability, corresponding with seizure propagation mechanisms, and is promising to assist DRE epileptogenic zone localization.

Authors

  • Yiping Wang
    Shenzhen Key Laboratory of Photonic Devices and Sensing Systems for Internet of Things, Guangdong and Hong Kong Joint Research Centre for Optical Fibre Sensors, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
  • Jinjie Guo
    Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
  • Ziyu Jia
    Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China; Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. Electronic address: jia.ziyu@outlook.com.
  • Gongpeng Cao
    Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
  • Yanfeng Yang
    Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.
  • Guixia Kang
    School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
  • Jinguo Huang
    Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.

Keywords

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