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ORIGINAL RESEARCH article

Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 15 - 2024 | doi: 10.3389/fphys.2024.1439607

Multi-branch Fusion Graph Neural Network Based on Multi-head Attention for Childhood Seizure Detection

Provisionally accepted
Yang Li Yang Li 1Yang Yang Yang Yang 1*Shangling Song Shangling Song 2*Hongjun Wang Hongjun Wang 1*Mengzhou Sun Mengzhou Sun 3*Xiaoyun Liang Xiaoyun Liang 3Penghui Zhao Penghui Zhao 1*Baiyang Wang Baiyang Wang 1Na Wang Na Wang 1*Qiyue Sun Qiyue Sun 1*Zijuan Han Zijuan Han 1*
  • 1 Shandong University, Jinan, China
  • 2 The Second Hospital of Shandong University, Jinan, Shandong Province, China
  • 3 Neusoft Medical Systems Co., Ltd., Shenyang, Liaoning Province, China

The final, formatted version of the article will be published soon.

    The most common manifestation of neurological disorders in children is the occurrence of epileptic seizures. In this study, we propose a multi-branch graph convolutional network (MGCNA) framework with a multi-head attention mechanism for detecting seizures in children. The MGCNA framework extracts effective and reliable features from high-dimensional data, particularly by exploring the relationships between EEG features and electrodes and considering the spatial and temporal dependencies in epileptic brains. This method incorporates three graph learning approaches to systematically assess the connectivity and synchronization of multi-channel EEG signals. The multi-branch graph convolutional network is employed to dynamically learn temporal correlations and spatial topological structures. Utilizing the multi-head attention mechanism to process multi-branch graph features further enhances the capability to handle local features.Experimental results demonstrate that the MGCNA exhibits superior performance on patientspecific and patient-independent experiments. Our end-to-end model for automatic detection of epileptic seizures could be employed to assist in clinical decision-making.

    Keywords: Childhood seizure detection, Graph convolutional network, Adjacency matrix, EEG, Multi-head attention

    Received: 28 May 2024; Accepted: 07 Oct 2024.

    Copyright: © 2024 Li, Yang, Song, Wang, Sun, Liang, Zhao, Wang, Wang, Sun and Han. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yang Yang, Shandong University, Jinan, China
    Shangling Song, The Second Hospital of Shandong University, Jinan, 250012, Shandong Province, China
    Hongjun Wang, Shandong University, Jinan, China
    Mengzhou Sun, Neusoft Medical Systems Co., Ltd., Shenyang, Liaoning Province, China
    Penghui Zhao, Shandong University, Jinan, China
    Na Wang, Shandong University, Jinan, China
    Qiyue Sun, Shandong University, Jinan, China
    Zijuan Han, Shandong University, Jinan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.