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Multi-view Convolutional Network with Fused Attention for Intelligent-assisted Epilepsy Detection and Recognition
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Qi LI1, 2, Xu-rong YAN1, Yan WU1, 2, Di ZHAO1, Li-na CHANG1, Han-lin SUN1
Science Technology and Engineering | 2025, 25(5) : 1988 - 1995
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Science Technology and Engineering | 2025, 25(5): 1988-1995
• Papers·Automation and Computational Technology •
Multi-view Convolutional Network with Fused Attention for Intelligent-assisted Epilepsy Detection and Recognition
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Qi LI1, 2, Xu-rong YAN1, Yan WU1, 2, Di ZHAO1, Li-na CHANG1, Han-lin SUN1
Affiliations
  • 1 School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
  • 2 Zhongshan Institute, Changchun University of Science and Technology, Zhongshan 528400, China
Published: 2025-02-18 doi: 10.12404/j.issn.1671-1815.2400599
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In response to the problem of low accuracy in epilepsy detection and recognition using single-view networks, a multi-view convolutional network model with fused attention mechanism (FAM-MCNN) was proposed. Multiple view features were extracted from time domain, frequency domain, time-frequency domain and nonlinear domain to characterize electroencephalogram(EEG) signals comprehensively. Multi-scale convolution was used to capture different levels of detail information. In order to improve the ability to distinguish different types of EEG signals in epileptic patients, the attention mechanism was introduced to combine the features from view dimension and single feature vector dimension respectively. The results of the comparison experiments performed on the CHB-MIT epilepsy dataset show that the average accuracy, sensitivity, and specificity of the FAM-MCNN model are improved by 14.29%, 16.13%, and 12.54%, respectively, when compared to a single-view network. In addition, experiments under a small number of training samples (25%) show that its detection performance reaches the level of the comparison model with a large number of training samples (80%~90%).

electroencephalogram signal  /  multi-view convolution  /  attention mechanisms  /  intelligent auxiliary detection of epilepsy
Qi LI, Xu-rong YAN, Yan WU, Di ZHAO, Li-na CHANG, Han-lin SUN. Multi-view Convolutional Network with Fused Attention for Intelligent-assisted Epilepsy Detection and Recognition[J]. Science Technology and Engineering, 2025 , 25 (5) : 1988 -1995 . DOI: 10.12404/j.issn.1671-1815.2400599
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doi: 10.12404/j.issn.1671-1815.2400599
  • Receive Date：2024-01-20
  • Online Date：2025-07-29
  • Published：2025-02-18
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  • Received：2024-01-20
  • Revised：2024-11-13
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    1 School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
    2 Zhongshan Institute, Changchun University of Science and Technology, Zhongshan 528400, China
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