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- W3018049579 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." @default.
- W3018049579 created "2020-05-01" @default.
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- W3018049579 date "2020-05-11" @default.
- W3018049579 modified "2023-10-14" @default.
- W3018049579 title "Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder" @default.
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- W3018049579 doi "https://doi.org/10.1007/s11071-020-05665-9" @default.
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