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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- 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 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
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