EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network - Wang, Baiyang; Kang, Yuyun; Huo, Dongyue; Feng, Guifang; Zhang, Jiawei; Li, Jiadong (Frontiers in Physiology, 2022)
Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diagnosis. One is that professional psychiatrists make diagnosis results for patients, but it is not conducive to large-scale depression detection. The other is to use electroencephalogram (EEG) to record neuronal activity, and use artificial or traditional machine learning methods to systematically screen the brain function status of patients to diagnose depression status and types, which has achieved good results, but does not make full use of the multi-channel information of EEG. Aiming at this problem, this paper proposes an EEG diagnosis method for depression based on multi-channel data fusion clipping augmentation and convolutional neural network, the multi-channel EEG data is transformed into a 2D image after multi-channel fusion (MCF) and multi-scale clipping (MSC) augmentation, and then trained them by a multi-channel convolutional neural network (MCNN). Finally, the trained model is loaded into the detection device to classify the input EEG signals. The experimental results show that the combination of MCF and MSC can make full use of the information contained in the single sensor records, and significantly improve the classification accuracy and clustering effect of depression diagnosis. This method has the advantages of low complexity, robustness and effectiveness, which is beneficial to the wide application of detection system.