EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network.

ABSTRACT: 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. Another is to use electroencephalography (EEG) to record neuronal activity. Then, the features of the EEG are extracted using manual or traditional machine learning methods to diagnose the state and type of depression. Although this method achieves good results, it does not fully utilize the multi-channel information of EEG. Aiming at this problem, an EE

SUBMITTER: Wang B 

PROVIDER: S-EPMC9632488 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature