Sleep is an important part of the body’s recuperation and energy accumulation, and the quality of sleep also has a significant impact on people’s physical and mental state during the epidemic of Coronavirus Disease. It has attracted increasing attention on how to improve the quality of sleep and reduce the impact of sleep-related diseases on health.

The electroencephalogram

(EEG) signals collected during sleep belong to spontaneous EEG signals. Spontaneous sleep EEG signals can reflect the body’s changes, which is also an basis for diagnosis and treatment of related diseases.
Therefore, the establishment of an effective model for classifying sleep EEG signals is an important auxiliary tool for evaluating sleep quality, diagnosing and treating sleep-related diseases.
In this paper, outliers of each kind of original data were detected and deleted by using the principle of 3 Sigma and k-means clustering + Euclidean distance detection method. Then, using the Adam algorithm with adaptive learning rate constructs the Softmax multi-classification BP neural network the model , and relatively high accuracy and AUC values were finally obtained.

Keywords : Sleep EEG, Deep learning, Softmax function, Adam algorithm, Multiple classification problem

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