基于CNN-BiLSTM的自动睡眠分期方法

To solve the problems, including the manual dependence in the extraction of sleep staging features, difficult identification to the timing pattern in long-term correlated data, and the inaccuracy of EEG timing data staging in the model, and so on, an automatic sleep staging method based on CNN-BiLSTM was proposed. Firstly, the original data was over-sampled with improving the MSMOTE algorithm to form the class equilibrium data. And then the advanced features were expressed by CNN and fed to BiLSTM to explore the dependency relationship between sleep stages, so as to realize the automatic learning and sleep cycle determination of sleep data staging characteristics. The experimental results on the Sleep-EDF open data set show that the classification accuracy of the CNN-BiLSTM model can reach 92.21%. The improved over-sampling technique of MSMOTE can alleviate the problem of inaccuracy in the determination of sleep stage. In the case of unbalanced class of original data set, automatic sleep data staging is realized, which can effectively improve the accuracy of sleep staging model, possessing a certain practical value.