摘要
脑电图(EEG)中蕴含着有关脑功能的丰富信息,这些信息对不同类型神经系统疾病的检测和诊断非常重要。针对单一特征无法充分表达脑电信号的问题,本文融合了频域特征和时空信息来更好的对信号进行表征,并提出一种基于时空和频域特征的注意力网络(STFACN)用于帕金森疾病(PD)的自动检测。在频域角度,利用快速傅里叶变换法从多通道脑电图中求取Delta、Theta、Alpha频段的平均功率特征。同时构建基于时空特征的紧凑型卷积神经网络,并将通道注意力机制嵌入到网络中,自适应提取表征PD的时空特征。最后将基于频域特征的模型与基于时空特征的紧凑型卷积神经网络模型进行融合,在新墨西哥州大学(UNM)数据集上进行实验,特异性、敏感性、准确率分别达到87.97%、84.39%、86.89%。在爱荷华大学(UI)数据集上进行跨数据集实验,准确率达到77.33%。实验结果表明:与现有的方法相比,本文提出的方法能够从原始脑电图中挖掘出有效特征,在基于EEG的帕金森疾病识别问题上准确率高,泛化能力强。
Elctroencephalography(EEG)contains rich information about brain function,which is very important for the detection and diagnosis of different types of neurological diseases.In view of the fact that a single feature cannot fully express the EEG signal,this paper combines frequency domain features and spatiotemporal information to better represent the signal,and proposes an attention network based on spatio-temporal and frequency domain features(STFACN)for automatic detection of Parkinson's disease(PD).From the perspective of frequency domain,the average power characteristics of Delta,Theta and Alpha frequency bands were obtained from the multir channel EEG using the fast Fourier transform method.In terms of spatiotemporal feature extraction,a compact convolutional neural network based on spatiotemporal features is constructed,and the channel attention mechanism is embedded into the network,and the adaptive extraction can characterize the effective features of PD.Finally,the model based on frequency domain features is fused with the compact convolutional neural network model based on spatiotemporal features,and experiments are carried out on the University of New Mexico(UNM)dataset.The specificity,sensitivity and accuracy reach 87.97%,84.39%and 86.89%respectively.Cross dataset experiments are performed on the University of Iowa(UI)dataset,and the accuracy rate reaches 77.33%.The experimental results show that compared with the existing methods,the method proposed in this paper can mine effective features from the original EEG,and has high accuracy and strong generalization ability in the EEG-based Parkinson's identification problem.
作者
杜淑慧
何小海
赵晓玲
卿粼波
陈洪刚
Du Shuhui;He Xiaohai;Zhao Xiaoling;Qing Linbo;Chen Honggang(College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China;Affiliated Hospital of Southwest Jiaotong University&The Third People's Hospital of Chengdu,Chengdu 610031,China)
出处
《电子测量技术》
北大核心
2023年第3期121-127,共7页
Electronic Measurement Technology
基金
成都市重大科技应用示范项目(2019-YF09-00120-SN)资助
关键词
脑电信号
频段平均功率
时空特征
通道注意力
EEG signal
frequency band average power
spatiotemporal features
channel attention
