Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder

Weidong Dang, Zhongke Gao*, Xinlin Sun, Rumei Li, Qing Cai, Celso Grebogi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
2 Downloads (Pure)


As a global and grievous mental disease, major depressive disorder (MDD) has received much attention. Accurate detection of MDD via physiological signals represents an urgent research topic. Here, a frequencydependent multilayer brain network, combined with deep convolutional neural network (CNN), is developed to detect the MDD. Multivariate pseudo Wigner distribution is firstly introduced to extract the time-frequency characteristics from the multi-channel EEG signals. Then multilayer brain network is constructed, with each layer corresponding to a specific frequency band. Such multilayer framework is in line with the nature of the workings of the brain, and can effectively characterize the brain state. Further, a multilayer deep CNN architecture is designed to study the brain network topology features, which is finally used to accurately detect MDD. The experimental results on a publicly available MDD dataset show that the proposed approach is able to detect MDD with state-of-the-art accuracy of 97.27%. Our approach, combining multilayer brain network and deep CNN, enriches the multivariate time series analysis theory and helps to better characterize and recognize the complex brain states.
Original languageEnglish
Pages (from-to)667-677
Number of pages11
JournalNonlinear Dynamics
Early online date11 May 2020
Publication statusPublished - Oct 2020


  • Electroencephalogram
  • Major depressive disorder
  • Complex network
  • Convolutional neural network


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