Key result
HybridEEGNet achieves ~79% accuracy in classifying normal, medicated, and unmedicated patients with major depressive disorder.
Why the study?
Few deep learning studies have focused on designing convolutional filters to learn features based on EEG activity characteristics for depression discrimination.
Can HybridEEGNet accurately discriminate between normal controls, medicated MDD patients, and unmedicated MDD patients using EEG features?
Cross-Sectional (n=35)
No
Can HybridEEGNet accurately discriminate between normal controls, medicated MDD patients, and unmedicated MDD patients using EEG features?
HybridEEGNet demonstrates moderate accuracy in classifying depression status using EEG features, highlighting the potential of spatial distributions and amplitude ranges in the alpha rhythm as distinctive attributes.
May enhance EEG-based MDD discrimination via tailored filters; leaves open generalizability and clinical utility in larger cohorts.
Electroencephalogram (EEG) measurement, being an appropriate approach to understanding the underlying mechanisms of the major depressive disorder (MDD), is used to discriminate between depressive and normal control. With the advancement of deep learning methods, many studies have designed deep learning models to improve the classification accuracy of depression discrimination. However, few of them have focused on designing a convolutional filter to learn features according to EEG activity characteristics. In this study, a novel convolutional neural network named HybridEEGNet that is composed of two parallel lines is proposed to learn the synchronous and regional EEG features, and further differentiate normal controls from medicated and unmedicated MDD patients. A ten-fold cross validation method is used to train and test the model. The results show that HybridEEGNet achieves a sensitivity of 68.78%, a specificity of 84.45%, and an accuracy of 79.08% in three-category classification. The result of EEG feature analysis indicates that the differences of spatial distributions and amplitude ranges in the alpha rhythm (especially at approximately 10 Hz) among three categories might be distinctive attributes for depression discrimination.
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Wan et al. (2020) conducted a cross-sectional in Major Depressive Disorder (n=35). HybridEEGNet vs. Other CNN models (DeepConvNet, AchCNN, EEGNet) was evaluated on Three-category classification accuracy. HybridEEGNet achieved a sensitivity of 68.78%, a specificity of 84.45%, and an accuracy of 79.08% in classifying normal controls, medicated, and unmedicated patients with major depressive disorder.
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