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作 者:王兴起 李明爱 WANG Xingqi;LI Ming’ai(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,P.R.China;Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,P.R.China;Engineering Research Center of Digital Community,Ministry of Education,Beijing 100124,P.R.China)
机构地区:[1]北京工业大学信息学部,北京100124 [2]北京市计算智能和智能系统重点实验室,北京100124 [3]教育部数字社区工程研究中心,北京100124
出 处:《生物医学工程学杂志》2024年第2期253-261,共9页Journal of Biomedical Engineering
基 金:国家自然科学基金项目(62173010)。
摘 要:基于深度学习的癫痫脑电自动检测方法,能避免人为因素的影响而倍受关注,而其有效性主要取决于深度神经网络模型。为此,本研究将根据癫痫脑电的多尺度、时空特点及导联间的信息流动特征,设计一种基于注意力的多尺度残差网络(AMSRN),并与多尺度主元分析法(MSPCA)相结合,实现癫痫的自动检测。首先,利用MSPCA对原始癫痫脑电信号进行去噪和特征增强;进而,设计AMSRN模型结构与参数。其中,注意力模块(AM)、多尺度卷积模块(MCM)、时空特征提取模块(STFEM)和分类模块(CM)相继完成基于注意力加权机制的信号重表达以及多尺度-时空特征的提取、融合与分类。基于麻省理工学院的波士顿儿童医院(CHBMIT)公共数据集进行5折交叉验证实验研究,AMSRN模型在灵敏度(98.56%)、F1分数(98.35%)、准确度(98.41%)及精确度(98.43%)等方面均取得了较好结果。结果表明,AMSRN模型能够很好地利用癫痫发作引起的脑网络信息流动强化导联间差异性,并有效捕获癫痫脑电的多尺度和时空特征,有利于改善癫痫检测性能。The deep learning-based automatic detection of epilepsy electroencephalogram(EEG),which can avoid the artificial influence,has attracted much attention,and its effectiveness mainly depends on the deep neural network model.In this paper,an attention-based multi-scale residual network(AMSRN)was proposed in consideration of the multiscale,spatio-temporal characteristics of epilepsy EEG and the information flow among channels,and it was combined with multiscale principal component analysis(MSPCA)to realize the automatic epilepsy detection.Firstly,MSPCA was used for noise reduction and feature enhancement of original epilepsy EEG.Then,we designed the structure and parameters of AMSRN.Among them,the attention module(AM),multiscale convolutional module(MCM),spatio-temporal feature extraction module(STFEM)and classification module(CM)were applied successively to signal reexpression with attention weighted mechanism as well as extraction,fusion and classification for multiscale and spatio-temporal features.Based on the Children’s Hospital Boston-Massachusetts Institute of Technology(CHB-MIT)public dataset,the AMSRN model achieved good results in sensitivity(98.56%),F1 score(98.35%),accuracy(98.41%)and precision(98.43%).The results show that AMSRN can make good use of brain network information flow caused by seizures to enhance the difference among channels,and effectively capture the multiscale and spatio-temporal features of EEG to improve the performance of epilepsy detection.
关 键 词:脑电信号 多尺度主元分析 脑网络 深度学习 癫痫检测
分 类 号:TN911.7[电子电信—通信与信息系统] R742.1[电子电信—信息与通信工程]
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