EEG-based mild depression recognition using convolutional neural network - Li, Xiaowei; La, Rong; Wang, Ying; Niu, Junhong; Zeng, Shuai; Sun, Shuting; Zhu, Jing (Medical & biological engineering & computing, 2019)
Electroencephalography (EEG)–based studies focus on depression recognition using data mining methods, while those on mild depression are yet in infancy, especially in effective monitoring and quantitative measure aspects. Aiming at mild depression recognition, this study proposed a computer-aided detection (CAD) system using convolutional neural network (ConvNet). However, the architecture of ConvNet derived by trial and error and the CAD system used in clinical practice should be built on the basis of the local database; we therefore applied transfer learning when constructing ConvNet architecture. We also focused on the role of different aspects of EEG, i.e., spectral, spatial, and temporal information, in the recognition of mild depression and found that the spectral information of EEG played a major role and the temporal information of EEG provided a statistically significant improvement to accuracy. The proposed system provided the accuracy of 85.62% for recognition of mild depression and normal controls with 24-fold cross-validation (the training and test sets are divided based on the subjects). Thus, the system can be clinically used for the objective, accurate, and rapid diagnosis of mild depression. Graphical abstract The EEG power of theta, alpha, and beta bands is calculated separately under trial-wise and frame-wise strategies and is organized into three input forms of deep neural networks: feature vector, images without electrode location (spatial information), and images with electrode location. The role of EEG’s spectral and spatial information in mild depression recognition is investigated through ConvNet, and the role of EEG’s temporal information is investigated using different architectures to aggregate temporal features from multiple frames. The ConvNet and models for aggregating temporal features are transferred from the state-of-the-art model in mental load classification.