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

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Abstract

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 frequency-dependent 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.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants Nos. 61922062 and 61873181.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Weidong Dang, Zhongke Gao, Xinlin Sun, Rumei Li & Qing Cai

  2. Institute for Complex Systems and Mathematical Biology, King’s College, University of Aberdeen, Aberdeen, AB24 3UE, UK

    Celso Grebogi

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  1. Weidong Dang
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Correspondence to Zhongke Gao.

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Dang, W., Gao, Z., Sun, X. et al. Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder. Nonlinear Dyn 102, 667–677 (2020). https://doi.org/10.1007/s11071-020-05665-9

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