Multiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG

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 ori...

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Autores principales:	Yan Li, Juan Liu, Chi Tang, Wei Han, Shengyi Zhou, Siqi Yang, Long He, Da Jing, Erping Luo, Kangning Xie
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Multiscale entropy analysis complexity sleep brain wave Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/0ac7c9fcba8f42ccbbbd4c8cab104b43
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spelling	oai:doaj.org-article:0ac7c9fcba8f42ccbbbd4c8cab104b432021-11-19T00:05:05ZMultiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG2169-353610.1109/ACCESS.2021.3051367https://doaj.org/article/0ac7c9fcba8f42ccbbbd4c8cab104b432021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9321409/https://doaj.org/toc/2169-3536Generally, 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.Yan LiJuan LiuChi TangWei HanShengyi ZhouSiqi YangLong HeDa JingErping LuoKangning XieIEEEarticleMultiscale entropy analysiscomplexitysleepbrain waveElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 12896-12905 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multiscale entropy analysis complexity sleep brain wave Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Multiscale entropy analysis complexity sleep brain wave Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yan Li Juan Liu Chi Tang Wei Han Shengyi Zhou Siqi Yang Long He Da Jing Erping Luo Kangning Xie Multiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG
description	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.
format	article
author	Yan Li Juan Liu Chi Tang Wei Han Shengyi Zhou Siqi Yang Long He Da Jing Erping Luo Kangning Xie
author_facet	Yan Li Juan Liu Chi Tang Wei Han Shengyi Zhou Siqi Yang Long He Da Jing Erping Luo Kangning Xie
author_sort	Yan Li
title	Multiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG
title_short	Multiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG
title_full	Multiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG
title_fullStr	Multiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG
title_full_unstemmed	Multiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG
title_sort	multiscale entropy analysis of instantaneous frequency variation to overcome the cross-over artifact in rhythmic eeg
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/0ac7c9fcba8f42ccbbbd4c8cab104b43
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Multiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG

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