兰州大学机构库
A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography
Li, XW(李小伟)1; La, Rong1; Wang, Ying1; Hu, B(胡斌)1,2,3; Zhang, Xuemin4,5,6,7
2020-04
Source PublicationFRONTIERS IN NEUROSCIENCE   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
EISSN1662-453X
Volume14
page numbers20
AbstractEarly detection remains a significant challenge for the treatment of depression. In our work, we proposed a novel approach to mild depression recognition using electroencephalography (EEG). First, we explored abnormal organization in the functional connectivity network of mild depression using graph theory. Second, we proposed a novel classification model for recognizing mild depression. Considering the powerful ability of CNN to process two-dimensional data, we applied CNN separately to the two-dimensional data form of the functional connectivity matrices from five EEG bands (delta, theta, alpha, beta, and gamma). In addition, inspired by recent breakthroughs in the ability of deep recurrent CNNs to classify mental load, we merged the functional connectivity matrices from the three EEG bands that performed the best into a three-channel image to classify mild depression-related and normal EEG signals using the CNN. The results of the graph theory analysis showed that the brain functional network of the mild depression group had a larger characteristic path length and a lower clustering coefficient than the healthy control group, showing deviation from the small-world network. The proposed classification model obtained a classification accuracy of 80.74% for recognizing mild depression. The current study suggests that the combination of a CNN and functional connectivity matrix may provide a promising objective approach for diagnosing mild depression. Deep learning approaches such as this might have the potential to inform clinical practice and aid in research on psychiatric disorders.
KeywordEEG functional connectivity convolutional neural network mild depression classification
PublisherFRONTIERS MEDIA SA
DOI10.3389/fnins.2020.00192
Indexed BySCIE
Language英语
Funding ProjectNational Basic Research Program of China (973 Program)[2014CB744600] ; National Natural Science Foundation of China[61632014][61210010][61402211][661300231][61627808] ; Fundamental Research Funds for the Central Universities[lzujbky2017-it74][lzujbky-2017-it75] ; Program of Beijing Municipal Science and Technology Commission[Z171100000117005]
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeurosciences
WOS IDWOS:000597672300001
PublisherFRONTIERS MEDIA SA
Original Document TypeArticle
PMID 32300286
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/444053
Collection兰州大学
信息科学与工程学院
Corresponding AuthorHu, Bin
Affiliation
1.Lanzhou Univ, Sch Informat Sci & Engn, Gansu Prov Key Lab Wearable Comp, Lanzhou, Peoples R China
2.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai Inst Biol Sci, Shanghai, Peoples R China
3.Capital Med Univ, Beijing Inst Brain Disorders, Beijing, Peoples R China
4.Beijing Normal Univ, Fac Psychol, Natl Demonstrat Ctr Expt Psychol Educ, Beijing Key Lab Appl Expt Psychol, Beijing, Peoples R China
5.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China
6.Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing, Peoples R China
7.Beijing Normal Univ, Ctr Collaborat & Innovat Brain & Learning Sci, Beijing, Peoples R China
First Author AffilicationSchool of Information Science and Engineering
Corresponding Author AffilicationSchool of Information Science and Engineering
First Signature AffilicationSchool of Information Science and Engineering
Recommended Citation
GB/T 7714
Li, Xiaowei,La, Rong,Wang, Ying,et al. A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography[J]. FRONTIERS IN NEUROSCIENCE,2020,14.
APA Li, Xiaowei,La, Rong,Wang, Ying,Hu, Bin,&Zhang, Xuemin.(2020).A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography.FRONTIERS IN NEUROSCIENCE,14.
MLA Li, Xiaowei,et al."A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography".FRONTIERS IN NEUROSCIENCE 14(2020).
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