期刊
IEEE ACCESS
卷 9, 期 -, 页码 12896-12905出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3051367
关键词
Sleep; Electroencephalography; Entropy; Complexity theory; Time series analysis; Broadband communication; Rhythm; Multiscale entropy analysis; complexity; sleep; brain wave
资金
- Natural Science Foundation of Shaanxi Province [2015JM3104]
Research has found that multi-scale entropy curves of sleep and wakefulness EEG signals intersect, with the origin of this phenomenon remaining unexplored. Simulation results show that the crossover phenomenon is prevalent in the MSE analysis, with the crossover point moving towards larger scale factors with increasing sampling rate. Real-world EEG results indicate a higher MSE curve for wakefulness compared to sleep, showing no interception.
Generally, for healthy adults, the entropy of electroencephalogram (EEG) signals gradually decreases from wake to sleep stages N1, N2, to N3, and increases during REM. However, some researchers found that multiscale entropy curves of sleep and wakefulness intercept, a cross-over phenomenon whose origin remains unexplored. The objective of the present work is to trace the origin of the cross-over phenomenon and to propose a workaround strategy. We simulated EEG by generating 1/f broadband signal and chirp signals with continuously varying frequencies. We then retrieved the rhythmic component from simulated EEG and real-world EEG and conducted MSE analysis of the instantaneous frequency variation (IFV) of the rhythmic component. The simulation revealed that this interception was ubiquitous in the MSE analysis of simulated EEG with rhythmic components of different frequencies. The cross-over point moved toward larger scale factors with the increasing sampling rate. We found that the MSE curve of IFV from real-world EEG for the wakefulness group was higher than that for sleep, showing no interception. These results suggest that (1) for a rhythmic signal like EEG, MSE analysis of the raw signal is highly affected by the rhythmic component, presenting artificial cross-over curves in sleep EEG study, (2) frequency variation of rhythmic components are complex signal which differs between wakefulness and sleep, in accordance with the complexity loss theory.
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