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The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method

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资源类型：
WOS体系：
Pubmed体系：

收录情况： ◇ SCIE ◇ EI

作者：
Dong Wen[a,b,*1] * ; Peng Li[a,b] ; Xiaoli Li[c] ; Zhenhao Wei[a,b] ; Yanhong Zhou[a,d,*2] ; Huan Pei[a,b] ; Fengnian Li[e] ; Zhijie Bian[f] ; Lei Wang[g] ; Shimin Yin[g] ;
单位： [a]School of Information Science and Engineering, Yanshan University, Qinhuangdao, China [b]The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, China [c]The National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China [d]School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China [e]Yanshan University Library, Yanshan University, Qinhuangdao, China [f]Department of Neurology, Beijing Friendship Hospital, Beijing, China [g]Department of Neurology, The Rocket Force General Hospital of Chinese People’s Liberation Army, Beijing, China
出处：
DOI： 10.1016/j.neunet.2020.01.025
ISSN：

关键词： Feature-fusion multispectral image aMCI with T2DM EEG signal Convolutional neural network

摘要：
Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognitive impairment. However, it remains unclear which feature of EEG signals is best effective in assessing amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) when combining one classifier. This study proposed a novel feature extraction method of EEG signals named feature-fusion multispectral image method (FMIM) for diagnosis of aMCI with T2DM. The FMIM was integrated with convolutional neural network (CNN) to classify the processed multispectral image data. The results showed that FMIM could effectively identify aMCI with T2DM from the control group compared to existing multispectral image method (MIM), with improvements including the type and quantity of feature extraction. Meanwhile, part of the invalid calculation could be avoided during the classification process. In addition, the classification evaluation indexes were best under the combination of Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-1, and were also best under the combination of the Theta-Alphal-Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-2. Therefore, FMIM can be used as an effective feature extraction method of aMCI with T2DM, and as a valuable biomarker in clinical applications. (C) 2020 Elsevier Ltd. All rights reserved.

基金：
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61876165, 61503326, 61761166003, 61827811, 61801310]; Natural Science Foundation of Hebei Province in ChinaNatural Science Foundation of Hebei Province [F2016203343]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2015M581317]; Research and Development Plan of Qinhuangdao Science and Technology of China [201703A072]
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2019]版：
大类 | 1 区 工程技术
小类 | 2 区 计算机：人工智能 2 区 神经科学
最新[2025]版：
大类 | 2 区 计算机科学
小类 | 2 区 计算机：人工智能 2 区 神经科学
JCR分区：
出版当年[2018]版：
Q1 NEUROSCIENCES Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
最新[2024]版：
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q1 NEUROSCIENCES

影响因子： 最新[2024版] 最新五年平均[2021-2025] 出版当年[2018版] 出版当年五年平均[2014-2018] 出版前一年[2017版] 出版后一年[2019版]

第一作者：
第一作者单位： [a]School of Information Science and Engineering, Yanshan University, Qinhuangdao, China [b]The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, China [*1]School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.
通讯作者：
通讯机构： [a]School of Information Science and Engineering, Yanshan University, Qinhuangdao, China [b]The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, China [d]School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China [*1]School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China. [*2]School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China.
推荐引用方式(GB/T 7714)：
Dong Wen,Peng Li,Xiaoli Li,et al.The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method[J].NEURAL NETWORKS.2020,124:373-382.doi:10.1016/j.neunet.2020.01.025.
APA：
Dong Wen,Peng Li,Xiaoli Li,Zhenhao Wei,Yanhong Zhou...&Shimin Yin.(2020).The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method.NEURAL NETWORKS,124,
MLA：
Dong Wen,et al."The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method".NEURAL NETWORKS 124.(2020):373-382

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