GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals.

Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE

Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004-

Depressive Disorder, Major*/diagnosis ; Depressive Disorder, Major*/physiopathology ; Electroencephalography*/methods ; Neural Networks, Computer*; Humans ; Male ; Female ; Adult ; Middle Aged ; Algorithms ; Signal Processing, Computer-Assisted ; Young Adult

Objective. Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge. Approach. Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance. Main results . The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls, in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks. Significance . The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.
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Keywords: EEG; contrastive learning; deep learning; major depressive disorder; phase locking value

Date Created: 20240524 Date Completed: 20240614 Latest Revision: 20240614

20240614

10.1088/1741-2552/ad5048

38788706