[1]张培玲,候康.基于多尺度自适应残差网络的癫痫检测方法[J].中国医学物理学杂志,2025,42(3):381-387.[doi:10.3969/j.issn.1005-202X.2025.03.015]
　ZHANG Peiling,HOU Kang.Epilepsy detection method based on multi-scale adaptive residual network[J].Chinese Journal of Medical Physics,2025,42(3):381-387.[doi:10.3969/j.issn.1005-202X.2025.03.015]
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基于多尺度自适应残差网络的癫痫检测方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第3期
页码:
381-387
栏目:
医学信号处理与医学仪器
出版日期:
2025-03-20

文章信息/Info

Title:
Epilepsy detection method based on multi-scale adaptive residual network
文章编号:
1005-202X(2025)03-0381-07
作者:
张培玲; 候康
河南理工大学物理与电子信息学院,河南 焦作 454003
Author(s):
ZHANG Peiling;  HOU Kang
School of Physics and Electronic Information, Henan Polytechnic University, Jiaozuo 454003, China
关键词:
脑电信号; 癫痫; 经验模态分解; 多尺度自适应残差网络; 注意力机制
Keywords:
electroencephalography signal;  epilepsy;  empirical mode decomposition;  multi-scale adaptive residual network;  attention mechanism
分类号:
R318TP391
DOI:
10.3969/j.issn.1005-202X.2025.03.015
文献标志码:
A
摘要:
针对现有癫痫检测方法输入数据单一、特征提取不充分问题,提出一种基于多尺度自适应残差网络的癫痫检测方 法。该方法首先对脑电信号使用经验模态分解获得 5 阶固有模态函数(IMF);接着将分解后的 5 阶 IMF 分别输入到多尺 度自适应残差网络(MSAR)中,该网络结合 CBAM-Residual 和多尺度自适应卷积网络用于提取信号的多尺度时频信息以 及细粒度特征;然后将 MSAR 提取的信号特征进行融合;最后输入到全连接层中实现分类。所提方法在 CHB-MIT 数据 集的分类准确率达到 98.94%,与现有方法相比取得了显著提升。
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.

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备注/Memo

备注/Memo:
【收稿日期】2024-10-09 【基金项目】国家自然科学基金(62101176);河南省高等学校大学生创 新创业训练计划(202210460071) 【作者简介】张培玲,博士,副教授,研究方向:通信技术和信号处理, E-mail: plzhang@hpu.edu.cn
更新日期/Last Update: 2025-03-27