Classification of Depression with Brain Network Characteristics Based on Multiphase Map Deep Neural Network Equilibrium Compensation

Depression is a common mental disease, characterized by depression and pessimism. Suicide may occur when symptoms are severe. As the number of depression patients increases year by year, the diagnosis results are affected by subjective factors, which is easy to cause misdiagnosis and missed diagnosis, so it is urgent to improve the accuracy of diagnosis. Based on the comprehensive analysis of the research status, processing and analysis methods of depression EEG using stochastic parallel gradient descent algorithm deep neural network learning algorithm, it is found that the selection of EEG denoising methods and diagnostic models is crucial to improve the diagnostic accuracy. Experimental results show the effectiveness of the proposed algorithm.