PMID- 32058892
OWN - NLM
STAT- MEDLINE
DCOM- 20200824
LR  - 20200824
IS  - 1879-2782 (Electronic)
IS  - 0893-6080 (Linking)
VI  - 124
DP  - 2020 Apr
TI  - 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.
PG  - 373-382
LID - S0893-6080(20)30035-6 [pii]
LID - 10.1016/j.neunet.2020.01.025 [doi]
AB  - 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-Alpha1-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.
CI  - Copyright (c) 2020 Elsevier Ltd. All rights reserved.
FAU - Wen, Dong
AU  - Wen D
AD  - School of Information Science and Engineering, Yanshan University, Qinhuangdao, 
      China; The Key Laboratory for Computer Virtual Technology and System Integration 
      of Hebei Province, Yanshan University, Qinhuangdao, China. Electronic address: 
      xjwd@ysu.edu.cn.
FAU - Li, Peng
AU  - Li P
AD  - School of Information Science and Engineering, Yanshan University, Qinhuangdao, 
      China; The Key Laboratory for Computer Virtual Technology and System Integration 
      of Hebei Province, Yanshan University, Qinhuangdao, China.
FAU - Li, Xiaoli
AU  - Li X
AD  - The National Key Laboratory of Cognitive Neuroscience and Learning, Beijing 
      Normal University, Beijing, China.
FAU - Wei, Zhenhao
AU  - Wei Z
AD  - School of Information Science and Engineering, Yanshan University, Qinhuangdao, 
      China; The Key Laboratory for Computer Virtual Technology and System Integration 
      of Hebei Province, Yanshan University, Qinhuangdao, China.
FAU - Zhou, Yanhong
AU  - Zhou Y
AD  - School of Information Science and Engineering, Yanshan University, Qinhuangdao, 
      China; School of Mathematics and Information Science and Technology, Hebei Normal 
      University of Science and Technology, Qinhuangdao, China. Electronic address: 
      yhzhou168@163.com.
FAU - Pei, Huan
AU  - Pei H
AD  - School of Information Science and Engineering, Yanshan University, Qinhuangdao, 
      China; The Key Laboratory for Computer Virtual Technology and System Integration 
      of Hebei Province, Yanshan University, Qinhuangdao, China.
FAU - Li, Fengnian
AU  - Li F
AD  - Yanshan University Library, Yanshan University, Qinhuangdao, China.
FAU - Bian, Zhijie
AU  - Bian Z
AD  - Department of Neurology, Beijing Friendship Hospital, Beijing, China.
FAU - Wang, Lei
AU  - Wang L
AD  - Department of Neurology, The Rocket Force General Hospital of Chinese People's 
      Liberation Army, Beijing, China.
FAU - Yin, Shimin
AU  - Yin S
AD  - Department of Neurology, The Rocket Force General Hospital of Chinese People's 
      Liberation Army, Beijing, China.
LA  - eng
PT  - Journal Article
DEP - 20200130
PL  - United States
TA  - Neural Netw
JT  - Neural networks : the official journal of the International Neural Network 
      Society
JID - 8805018
SB  - IM
MH  - Cognitive Dysfunction/complications/*physiopathology
MH  - Diabetes Mellitus, Type 2/*complications
MH  - Electroencephalography/*methods
MH  - Humans
MH  - *Neural Networks, Computer
OTO - NOTNLM
OT  - Convolutional neural network
OT  - EEG signal
OT  - Feature-fusion multispectral image
OT  - aMCI with T2DM
EDAT- 2020/02/15 06:00
MHDA- 2020/08/25 06:00
CRDT- 2020/02/15 06:00
PHST- 2019/07/15 00:00 [received]
PHST- 2019/10/06 00:00 [revised]
PHST- 2020/01/21 00:00 [accepted]
PHST- 2020/02/15 06:00 [pubmed]
PHST- 2020/08/25 06:00 [medline]
PHST- 2020/02/15 06:00 [entrez]
AID - S0893-6080(20)30035-6 [pii]
AID - 10.1016/j.neunet.2020.01.025 [doi]
PST - ppublish
SO  - Neural Netw. 2020 Apr;124:373-382. doi: 10.1016/j.neunet.2020.01.025. Epub 2020 
      Jan 30.