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.
The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
Traditional depression research based on electroencephalogram (EEG) regards electrodes as isolated nodes and ignores the correlation between them. So it is difficult to discover abnormal brain topology alters in patients with depression. To resolve this problem, this paper proposes a framework for depression recognition based on brain function network (BFN). To avoid the volume conductor effect, the phase lag index is used to construct BFN. BFN indexes closely related to the characteristics of “small world” and specific brain regions of minimum spanning tree were selected based on the information complementarity of weighted and binary BFN and then potential biomarkers of depression recognition are found based on the progressive index analysis strategy. The resting state EEG data of 48 subjects was used to verify this scheme. The results showed that the synchronization between groups was significantly changed in the left temporal, right parietal occipital and right frontal, the shortest path length and clustering coefficient of weighted BFN, the leaf scores of left temporal and right frontal and the diameter of right parietal occipital of binary BFN were correlated with patient health questionnaire 9-items (PHQ-9), and the highest recognition rate was 94.11%. In addition, the study found that compared with healthy controls, the information processing ability of patients with depression reduced significantly. The results of this study provide a new idea for the construction and analysis of BFN and a new method for exploring the potential markers of depression recognition.