PMID- 34492241
OWN - NLM
STAT- MEDLINE
DCOM- 20211021
LR  - 20211021
IS  - 1872-678X (Electronic)
IS  - 0165-0270 (Linking)
VI  - 363
DP  - 2021 Nov 1
TI  - Classification of ERP signal from amnestic mild cognitive impairment with type 2 
      diabetes mellitus using single-scale multi-input convolution neural network.
PG  - 109353
LID - S0165-0270(21)00288-0 [pii]
LID - 10.1016/j.jneumeth.2021.109353 [doi]
AB  - BACKGROUND: The application of deep learning models to electroencephalogram (EEG) 
      signal classification has recently become a popular research topic. Several deep 
      learning models have been proposed to classify EEG signals in patients with 
      various neurological diseases. However, no effective deep learning model for 
      event-related potential (ERP) signal classification is yet available for amnestic 
      mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM). METHOD: 
      This study proposed a single-scale multi-input convolutional neural network 
      (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the 
      control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta 
      frequency bands was extracted by using the fast Fourier transform, and then the 
      mean, sum of squares, and absolute value feature of each frequency band were 
      calculated. Finally, these three features are converted into multispectral images 
      respectively and used as the input of the SSMICNN network to realize the 
      classification task. RESULTS: The results show that the SSMICNN can fuse MSI 
      formed by different features, SSMICNN enriches the feature quantity of the neural 
      network input layer and has excellent robustness, and the errors of SSMICNN can 
      be simultaneously transmitted to the three convolution channels in the 
      back-propagation phase. Comparison with Existing Method(s): SSMICNN could more 
      effectively identify ERP signals from aMCI with T2DM from the control group 
      compared to existing classification methods, including convolution neural 
      network, support vector machine, and logistic regression. CONCLUSIONS: The 
      combination of SSMICNN and MSI can be used as an effective biological marker to 
      distinguish aMCI patients with T2DM from the control group.
CI  - Copyright (c) 2021 Elsevier B.V. All rights reserved.
FAU - Wen, Dong
AU  - Wen D
AD  - Institute of Artificial Intelligence, University of Science and Technology 
      Beijing, Beijing, China.
FAU - Cheng, Zihao
AU  - Cheng 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 - Li, Jingjing
AU  - Li J
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 - Zheng, Xinxiang
AU  - Zheng X
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 - Yao, Wang
AU  - Yao W
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 - Dong, Xianling
AU  - Dong X
AD  - Department of Biomedical Engineering, Chengde Medical University, Chengde, China.
FAU - Saripan, M Iqbal
AU  - Saripan MI
AD  - Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
FAU - Li, Xiaoli
AU  - Li X
AD  - The National Key Laboratory of Cognitive Neuroscience and Learning, Beijing 
      Normal University, 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.
FAU - Zhou, Yanhong
AU  - Zhou Y
AD  - School of Mathematics and Information Science and Technology, Hebei Normal 
      University of Science and Technology, Qinhuangdao, China. Electronic address: 
      yhzhou168@163.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210904
PL  - Netherlands
TA  - J Neurosci Methods
JT  - Journal of neuroscience methods
JID - 7905558
SB  - IM
MH  - *Cognitive Dysfunction/diagnosis
MH  - *Diabetes Mellitus, Type 2/complications
MH  - Electroencephalography
MH  - Evoked Potentials
MH  - Humans
MH  - Neural Networks, Computer
OTO - NOTNLM
OT  - Amnestic mild cognitive impairment
OT  - Event-related potential signal
OT  - Single-scale multi-input convolution neural network
OT  - Type 2 diabetes mellitus
EDAT- 2021/09/08 06:00
MHDA- 2023/02/25 06:00
CRDT- 2021/09/07 20:11
PHST- 2020/12/29 00:00 [received]
PHST- 2021/08/11 00:00 [revised]
PHST- 2021/09/01 00:00 [accepted]
PHST- 2021/09/08 06:00 [pubmed]
PHST- 2023/02/25 06:00 [medline]
PHST- 2021/09/07 20:11 [entrez]
AID - S0165-0270(21)00288-0 [pii]
AID - 10.1016/j.jneumeth.2021.109353 [doi]
PST - ppublish
SO  - J Neurosci Methods. 2021 Nov 1;363:109353. doi: 10.1016/j.jneumeth.2021.109353. 
      Epub 2021 Sep 4.