Epilepsy detection method based on multi-scale adaptive residual network
10.3969/j.issn.1005-202X.2025.03.015
- VernacularTitle:基于多尺度自适应残差网络的癫痫检测方法
- Author:
Peiling ZHANG
1
;
Kang HOU
1
Author Information
1. 河南理工大学物理与电子信息学院,河南 焦作 454003
- Publication Type:Journal Article
- Keywords:
electroencephalography signal;
epilepsy;
empirical mode decomposition;
multi-scale adaptive residual network;
attention mechanism
- From:
Chinese Journal of Medical Physics
2025;42(3):381-387
- CountryChina
- Language:Chinese
-
Abstract:
A novel approach based on multi-scale adaptive residual network(MSAR)is proposed to address the issues of single input data and inadequate feature extraction in current epilepsy detection approaches.The first 5 orders intrinsic mode functions for electroencephalography signal is obtained using empirical mode decomposition,and the decomposed the first 5 orders intrinsic mode functions are input into MSAR which incorporates CBAM-Residual and multi-scale adaptive convolutional network to extract multi-scale time-frequency information as well as fine-grained features of the signal.Subsequently,the signal features extracted by MSAR are fused and input into the fully connected layer to realize classification.The proposed approach obtains a classification accuracy of 98.94%on the CHB-MIT dataset,which is a notable improvement above the existing methods.
