Abstract
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 MSEμ (mean), MSEσ2 (variance) and MSEskew (skewness). The simulated results show that the generalized MSE (including MSEσ2 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 (<0.04 s), 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Communications in Nonlinear Science and Numerical Simulation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
