Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network
单位：[1]School of Information Science and Engineering, Yanshan University, Qinhuangdao,China[2]The Key Laboratory for Computer Virtual Technology and System Integration of HebeiProvince, Yanshan University, Qinhuangdao, China[3]School of Mathematics and Information Science and Technology, Hebei Normal University ofScience and Technology, Qinhuangdao, China[4]Departemt of Information, First Middle School of Qinhuangdao City, Qinhuangdao, China[5]The National Key Laboratory of Cognitive Neuroscience and Learning, Beijing NormalUniversity, Beijing, China[6]Department of Neurology, Beijing Friendship Hospital, Beijing, China临床科室神经内科神经内科首都医科大学附属北京友谊医院[7]Department of Neurology, The Rocket Force General Hospital of Chinese People's LiberationArmy, Beijing, China
Objective. The purpose of this study is to judge whether this combination method of multispectral image and convolutional neural network (CNN) method can be used to distinguish amnestic mild cognitive impairment (aMCI) with Type 2 diabetes mellitus (T2DM) and normal controls (NC) with T2DM effectively. Approach. In this study, the authors first combined EEG signals from aMCI patients with T2DM and NC with T2DM on five different frequency bands, including Theta, Alpha1, Alpha2, Beta1, and Beta2. Then, the authors converted these time series into a series of multispectral images. Finally, the images data were classified with the CNN method. Main results. The classification effects of up to 89%, 91%, and 92% are obtained on the three combinations of frequency bands: Theta, Alpha1, and Alpha2; Alpha1, Alpha2, and Beta1; and Alpha2, Beta1, and Beta2. The spatial properties of EEG signals are highlighted, and its classification performance is found to be better than all the previous methods in the field of aMCI and T2DM diagnosis. The combination of multispectral images and CNN can be used as an effective biomarker for distinguishing the EEG signals in patients with aMCI and T2DM and in patients with NC with T2DM. Significance. The combined approach used in this paper provides a new perspective for the analysis of EEG signals in patients with aMCI and T2DM.
基金：
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61876165, 61503326]; Natural Science Foundation of Hebei Province in ChinaNatural Science Foundation of Hebei Province [F2016203343, F2018203390]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2015M581317]
第一作者单位：[1]School of Information Science and Engineering, Yanshan University, Qinhuangdao,China[2]The Key Laboratory for Computer Virtual Technology and System Integration of HebeiProvince, Yanshan University, Qinhuangdao, China
共同第一作者：
通讯作者：
通讯机构：[1]School of Information Science and Engineering, Yanshan University, Qinhuangdao,China[2]The Key Laboratory for Computer Virtual Technology and System Integration of HebeiProvince, Yanshan University, Qinhuangdao, China
推荐引用方式(GB/T 7714)：
Wen Dong,Zhou Yanhong,Li Peng,et al.Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network[J].JOURNAL of NEURAL ENGINEERING.2020,17(3):doi:10.1088/1741-2552/ab8b7b.
APA：
Wen, Dong,Zhou, Yanhong,Li, Peng,Zhang, Peng,Li, Jihui...&Xu, Yuchen.(2020).Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network.JOURNAL of NEURAL ENGINEERING,17,(3)
MLA：
Wen, Dong,et al."Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network".JOURNAL of NEURAL ENGINEERING 17..3(2020)
