Sleep EEG analysis Based on the Multiscale Jenson-Shannon Divergence
Zhengxia Zhang, Jiafei Dai, Jun Wang, Fengzhen Hou · 2016
Sleep EEG signals analysis is a hotspot of research recently, this paper, by using nonlinear dynamics theory knowledge, JSD algorithm and multi-scale JSD algorithm is proposed for some individual conscious period and NREM sleep stage I analyzed the research of EEG signals, and the use of SPSS statistical software to verify the veracity and reliability of the experiment, at the same time, with the error bar graph method to analysis the two different states of sleep EEG signals, the results show that both the JSD algorithm and the multi-scale JSD algorithm can effectively distinguish between awake and NREM sleep stage I of EEG signals, these two conditions' EEG signals exist significant differences, The algorithm we proposed can be further used in the study of sleep EEG in installment, which can also provide all kinds of disease diagnosis and treatment of sleep with effective auxiliary function, the research has important practical significance in the future.