Predicting epileptic seizures based on a multi-convolution fusion network_Journal of Biomedical Engineering

Authors：

SHEN Xueting ,  PIAO Yan , YANG Huiru , ZHAO Haitong

School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, P. R. China;

Corresponding author：

PIAO Yan, Email: piaoyan@cust.edu.cn

Keywords：

Epilepsy prediction; Multi-scale sparse adaptive mechanism; Attention mechanism; Focal loss training strategy

DOI：

10.7507/1001-5515.202502059

Video：

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Current epilepsy prediction methods are not effective in characterizing the multi-domain features of complex long-term electroencephalogram (EEG) data, leading to suboptimal prediction performance. Therefore, this paper proposes a novel multi-scale sparse adaptive convolutional network based on multi-head attention mechanism (MS-SACN-MM) model to effectively characterize the multi-domain features. The model first preprocesses the EEG data, constructs multiple convolutional layers to effectively avoid information overload, and uses a multi-layer perceptron and multi-head attention mechanism to focus the network on critical pre-seizure features. Then, it adopts a focal loss training strategy to alleviate class imbalance and enhance the model's robustness. Experimental results show that on the publicly created dataset (CHB-MIT) by MIT and Boston Children's Hospital, the MS-SACN-MM model achieves a maximum accuracy of 0.999 for seizure prediction 10 ~ 15 minutes in advance. This demonstrates good predictive performance and holds significant importance for early intervention and intelligent clinical management of epilepsy patients.

Citation： SHEN Xueting, PIAO Yan, YANG Huiru, ZHAO Haitong. Predicting epileptic seizures based on a multi-convolution fusion network. Journal of Biomedical Engineering, 2025, 42(5): 987-993. doi: 10.7507/1001-5515.202502059 Copy

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Journal of Biomedical Engineering

Predicting epileptic seizures based on a multi-convolution fusion network

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