兰州大学机构库 >信息科学与工程学院
Analysis of Functional Brain Network in MDD Based on Improved Empirical Mode Decomposition with Resting State EEG Data
Shao, Xuexiao; Sun, Shuting; Li, Jianxiu; Kong, Wenwen; Zhu, J(祝婧); Li, XW(李小伟); Hu, B(胡斌)
2021
Source PublicationIEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING   Impact Factor & Quartile
ISSN1534-4320
EISSN1558-0210
Volume29Pages:1546-1556
page numbers11
AbstractAt present, most brain functional studies are based on traditional frequency bands to explore the abnormal functional connections and topological organization of patients with depression. However, they ignore the characteristic relationship of electroencephalogram (EEG) signals in the time domain. Therefore, this paper proposes a network decomposition model based on Improved Empirical Mode Decomposition (EMD), it is suitable for time-frequency analysis of brain functional network. On the one hand, it solves the problem of mode mixing on original EMD method, especially on high-density EEG data. On the other hand, by building brain function networks on different intrinsic mode function (IMF), we can perform time-frequency analysis of brain function connections. It provides a new insight for brain function connectivity analysis of major depressive disorder (MDD). Experimental results found that the IMFs waveform decomposed by Improved EMD was more stable and the difference between IMFs was obvious, it indicated that the mode mixing can be effectively solved. Besides, the analysis of the brain network, we found that the changes in MDD functional connectivity on different IMFs, it may be related to the pathological changes for MDD. More statistical results on three network metrics proved that there were significant differences between MDD and normal controls (NC) group. In addition, the aberrant brain network structure of MDDs was also confirmed in the hubs characteristic. These findings may provide potential biomarkers for the clinical diagnosis of MDD patients. © 2001-2011 IEEE.
KeywordElectroencephalography Mixing Signalprocessing Brainfunctionnetworks Brainfunctionalnetworks Electroencephalogramsignals EmpiricalModeDecomposition Improvedempiricalmodedecompositions(EMD) IntrinsicModefunctions Majordepressivedisorder(MDD) Timefrequencyanalysis Functional connectivity major depressive disorder high-density improved empirical mode decomposition resting state EEG
PublisherInstitute of Electrical and Electronics Engineers Inc.
DOI10.1109/TNSRE.2021.3092140
Indexed ByEI ; SCIE ; IEEE
Language英语
WOS Research AreaEngineering ; Rehabilitation
WOS SubjectEngineering, Biomedical ; Rehabilitation
WOS IDWOS:000683979500001
EI Accession Number20213310762922
EI Classification Number461.6 Medicine and Pharmacology ; 716.1 Information Theory and Signal Processing ; 802.3 Chemical Operations ; 921 Mathematics
Original Document TypeJournal article (JA)
PMID 34166194
Citation statistics
Cited Times:18[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/468861
Collection信息科学与工程学院
Corresponding AuthorLi, Xiaowei; Hu, Bin
Affiliation
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
First Author AffilicationSchool of Information Science and Engineering
Corresponding Author AffilicationSchool of Information Science and Engineering
Recommended Citation
GB/T 7714
Shao, Xuexiao,Sun, Shuting,Li, Jianxiu,et al. Analysis of Functional Brain Network in MDD Based on Improved Empirical Mode Decomposition with Resting State EEG Data[J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,2021,29:1546-1556.
APA Shao, Xuexiao.,Sun, Shuting.,Li, Jianxiu.,Kong, Wenwen.,Zhu, Jing.,...&Hu, Bin.(2021).Analysis of Functional Brain Network in MDD Based on Improved Empirical Mode Decomposition with Resting State EEG Data.IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,29,1546-1556.
MLA Shao, Xuexiao,et al."Analysis of Functional Brain Network in MDD Based on Improved Empirical Mode Decomposition with Resting State EEG Data".IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 29(2021):1546-1556.
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