Abstract: Healthy sleep is essential for the normal functioning of the human body and for the maintenance of mental vitality. One’s overnight sleep is usually evaluated by several sleep stages with sleep cycles. Automatic sleep stage identification methods are effective tools for sleep staging where feature extraction is an important procedure affecting the identification performance. In this study, an automatic sleep staging method based on multi-channel EEG signals and its optimized tensor feature space is developed. Several characteristic features are calculated from the two EEG recording channels and constructed as the original tensor feature space. A non-negative tensor factorization method based on sparse improvement is developed to optimize the tensor feature space for sleep staging. A classification model is constructed based on BP neural network and the parameters are estimated by PSO algorithm. Totally 10 overnight sleep recordings were tested. The averaged classification accuracy is about 84%. The developed method can be an assistant computerized tool for sleep staging.

Keywords: Sleep staging; EEG; Non-negative tensor factorization; BP neural network; PSO