Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction
Hongcheng, Ma1,2; Yajing, Wu2; Yongqiang, Tang2; Rui, Chen1,2; Tao, Xu3; Wensheng, Zhang1,2,4
Source PublicationComputers in Biology and Medicine
2024-06
Volume176Pages:108565
Abstract

 Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal.

Sub direction classification脑网络分析
planning direction of the national heavy laboratory脑启发多模态智能模型与算法
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56698
Collection多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队
Corresponding AuthorYajing, Wu
Affiliation1.School of Information and Communication Engineering, Hainan University, Haikou, China
2.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
3.Shanxi Key Laboratory of Big Data Analysis and Parallel Computing, Taiyuan University of Science and Technology, Taiyuan, China
4.School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Hongcheng, Ma,Yajing, Wu,Yongqiang, Tang,et al. Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction[J]. Computers in Biology and Medicine,2024,176:108565.
APA Hongcheng, Ma,Yajing, Wu,Yongqiang, Tang,Rui, Chen,Tao, Xu,&Wensheng, Zhang.(2024).Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction.Computers in Biology and Medicine,176,108565.
MLA Hongcheng, Ma,et al."Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction".Computers in Biology and Medicine 176(2024):108565.
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