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In recent years, the incidence of depression is increasing year by year, and depression lasts too long after the onset of the disease, which seriously hinders people's normal working life. In this study, based on the characteristics of scalp EEG signals, we compared the classification performance of Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM) classification models for depression by using 16 channels of clean EEG data, and the accuracy of the model using the combination of CNN and LSTM was improved about 9.21%, which confirms that the use of LSTM to help process EEG signals and improve classification is real and effective, and the effect of model parameters on model performance is discussed at the end of the paper to adjust model parameters and algorithms to improve the performance of classification. © 2022 IEEE.
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年份: 2022
页码: 125-130
语种: 英文
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