Journal
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
Volume 44, Issue -, Pages 292-303Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.cnsns.2016.08.019
Keywords
Multiscale entropy; Higher moments coarse-graining; EEG; Sleep stages
Categories
Funding
- China National Science [61371130]
- Beijing National Science [4162047]
- China Postdoctoral Science Foundation [2015M580040]
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It is of great interests in identifying dynamical properties of human sleep signals using electroencephalographic (EEG) measures. Multiscale entropy (MSE) is effective in quantifying the degree of unpredictability of time series in different time scales. To understand the superior coarse-graining approach for the EEG analysis, we therefor use different moments to coarse-grain a time series, and examine their volatility as well as the effectiveness in quantifying the complexities of sleep EEG in different sleep stages. Both the simulated signals (logistic map) and the EEGs with different sleep stages are calculated and compared using three types of coarse-graining procedure: including MSE mu (mean), MSE sigma 2 (variance) and MSEskew (skewness). The simulated results show that the generalized MSE (including MSE sigma 2 and MSEskew) can identify the differences in chaotic more easily with less fluctuation of entropy values in different time scales. As for the analysis of human sleep EEG, we find: (1) at small scales (<0.04s), the entropy is higher during wakefulness and increasing time scales. (2) At large scales (0.25 s-2 s) in contrast, entropy is higher during deep sleep and lower with increasing time scales. (C) 2016 Elsevier B.V. All rights reserved.
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