Research on Sleep Staging Method Based on DeepStageNet

In order to solve the problem of inaccurate results in the prediction of sleep stages by traditional networks, a DeepStageNet network was proposed. The network firstly express that deep characteristics through three-layer parallel CNN. Secondly, the extracted features are respectively transferred to the density at the next layer and further backward to the BiLSTM, which continues to transfer information and mine the dependency relationship between the various stages of sleep in the same way, thus realizing the automatic learning of the staging features of EEG signals and the determination of sleep periods. Finally, the experimental results on Sleep-EDF Database show that, after removing the influence of the data in the half-hour awake period before and after sleep, the Staging Accuracy of DeepStageNet Network on the traditiona130s data set and the new 210s data set after secondary processing are 86.5% and 87.7%. This effectively improves the Staging Accuracy of Sleep Model, and has certain practical value.