Abstract
To enhance the accuracy of depression (DP) recognition, this paper proposes a DP recognition method based on improved variational mode decomposition (VMD). Firstly, the adaptive particle swarm optimization (APSO) algorithm is adopted to improve VMD, aiming to find the optimal combination of the number of modes K and the penalty factor α, and thereby achieve the decomposition of electroencephalogram (EEG) signals. Then EEG signals are reconstructed based on the fitness between signal components and the original signal, noise is removed to obtain pure EEG signals, and their frequency-space features are extract. Next, a self-attention (SA) mechanism is introduced into the parallel architecture of two-dimensional convolutional neural network (2D-CNN) and bidirectional long short-term memory network (BiLSTM), to form the 2D-CNN-BiLSTM-SA detection model. Finally, the frequency-spatial features of the EEG signal are input into 2D-CNN-BILSTM-SA for DP recognition. Through comparative experiments on public datasets, the research results of this paper show that the improved VMD not only outperforms VMD but also achieves DP recognition accuracy rate of up to 94.47%. In conclusion, the method proposed in this paper provides a potential computer-aided tool for DP recognition.