Abstract: Depression is a common and curable psychiatric disorder.If a prompt diagnosis can be taken at the early stage of depression(early screening),appropriate treatment could effectively control the depression progression or even cure it.The traditional method of diagnosing depression is a comprehensive judgment from doctors by clinical manifestations and clinical examination(diagnostic scales,etc.),but the diagnosis accuracy relies heavily on the clinical experience of the physician and the inclination of cooperation from the patient.In addition,early-stage symptoms of depression are difficult to observe,making traditional diagnostic methods susceptible to underdiagnosis.Research indicates that electroencephalogram(EEG) responds effectively to the mental state of subjects from a physiological perspective,which provides an effective way of early screening for depression.On this basis,this paper proposes an EEG-based method combined with deep learning models for early screening of depression.First,extracting the temporal-spectral-spatial sequences of EEG signals by segmentation processing,frequency domain transformation,etc.Secondly,constructing a hybrid deep neural network based on extracted sequences to identify the EEG signals of mild depression patients.Finally,the feasibility and effectiveness of proposed method are verified by conducting numerical experiments in the public datasets MODMA.Numerical results show that the accuracy,recall rate and sensitivity of the proposed method is 82.64%,78.42%,and 75.37%,respectively.

Key words: Depression, Electroencephalogram, Early screening, Temporal-Spectral-Spatial sequences, Hybrid deep neural networks