PMID- 32746302
OWN - NLM
STAT- MEDLINE
DCOM- 20210624
LR  - 20210624
IS  - 1558-0210 (Electronic)
IS  - 1534-4320 (Linking)
VI  - 28
IP  - 8
DP  - 2020 Aug
TI  - Feature Classification Method of Resting-State EEG Signals From Amnestic Mild 
      Cognitive Impairment With Type 2 Diabetes Mellitus Based on Multi-View 
      Convolutional Neural Network.
PG  - 1702-1709
LID - 10.1109/TNSRE.2020.3004462 [doi]
AB  - The convolutional neural network (CNN) model is an active research topic in the 
      field of EEG signals analysis. However, the classification effect of CNN on EEG 
      signals of amnestic mild cognitive impairment (aMCI) with type 2 diabetes 
      mellitus (T2DM) is not ideal. Even if EEG signals are transformed into 
      multispectral images that are more closely matched with the model, the best 
      classification performance can not be achieved. Therefore, to improve the 
      performance of CNN toward EEG multispectral image classification, a multi-view 
      convolutional neural network (MVCNN) classification model based on inceptionV1 is 
      designed in this study. This model mainly improves and optimizes the 
      convolutional layers and stochastic gradient descent (SGD) in the convolutional 
      architecture model. Firstly, based on the discreteness of EEG multispectral image 
      features, the multi-view convolutional layer structure was proposed. Then the 
      learning rate change function of the SGD was optimized to increase the 
      classification performance. The multi-view convolutional nerve was used in an EEG 
      multispectral classification task involving 19 aMCI with T2DM and 20 normal 
      controls. The results showed that compared with the traditional classification 
      models, MVCNN had a better stability and accuracy. Therefore, MVCNN could be used 
      as an effective feature classification method for aMCI with T2DM.
FAU - Wen, Dong
AU  - Wen D
FAU - Li, Peng
AU  - Li P
FAU - Zhou, Yanhong
AU  - Zhou Y
FAU - Sun, Yanbo
AU  - Sun Y
FAU - Xu, Jian
AU  - Xu J
FAU - Liu, Yijun
AU  - Liu Y
FAU - Li, Xiaoli
AU  - Li X
FAU - Li, Jihui
AU  - Li J
FAU - Bian, Zhijie
AU  - Bian Z
FAU - Wang, Lei
AU  - Wang L
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20200623
PL  - United States
TA  - IEEE Trans Neural Syst Rehabil Eng
JT  - IEEE transactions on neural systems and rehabilitation engineering : a 
      publication of the IEEE Engineering in Medicine and Biology Society
JID - 101097023
SB  - IM
MH  - *Cognitive Dysfunction/diagnosis
MH  - *Diabetes Mellitus, Type 2/complications
MH  - Electroencephalography
MH  - Humans
MH  - Neural Networks, Computer
EDAT- 2020/08/04 06:00
MHDA- 2021/06/25 06:00
CRDT- 2020/08/04 06:00
PHST- 2020/08/04 06:00 [pubmed]
PHST- 2021/06/25 06:00 [medline]
PHST- 2020/08/04 06:00 [entrez]
AID - 10.1109/TNSRE.2020.3004462 [doi]
PST - ppublish
SO  - IEEE Trans Neural Syst Rehabil Eng. 2020 Aug;28(8):1702-1709. doi: 
      10.1109/TNSRE.2020.3004462. Epub 2020 Jun 23.