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Effective brain state estimation during propofol-induced sedation using advanced EEG microstate spectral analysis


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Li, Y, Shi, W, Liu, Z, Li, J, Wang, Qiang, Yan, X, Cao, Z ORCID: 0000-0003-3656-0328 and Wang, G 2020 , 'Effective brain state estimation during propofol-induced sedation using advanced EEG microstate spectral analysis' , IEEE Journal of Biomedical and Health Informatics , pp. 1-10 , doi: 10.1109/JBHI.2020.3008052.

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Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provided a promising tool to non-invasively characterize and analyze the synchronous neural firing. However, the topographical spectral information for each predominate microstate is still unclear during the switch of consciousness, such as sedation, and the practical usage of the EEG microstate is worth probing. Also, the mechanism behind the anesthetic-induced alternations of brain states remains poorly understood. In this study, a novel EEG microstate spectral analysis was utilized using multivariate empirical mode decomposition in Hilbert-Huang transform. The practicability was further investigated in scalp EEG recordings during the propofol-induced transition of consciousness. The process of transition from awake to moderate sedation was accompanied by apparent increases in microstate (A, B, and F) energy, especially in the whole-brain delta band, frontal alpha band and beta band. In comparison to other effective EEG-based parameters that commonly used to measure anesthetic depth, utilizing the selected spectral features reached better performance (80% sensitivity, 90% accuracy) to estimate the brain states during sedation. The changes in microstate energy also exhibited high correlations with individual behavioral data during sedation. In a nutshell, the EEG microstate spectral analysis is an effective method to estimate brain states during propofol-induced sedation, giving great insights into the underlying mechanism. The generated spectral features can be promising markers to dynamically assess the consciousness level.

Item Type: Article
Authors/Creators:Li, Y and Shi, W and Liu, Z and Li, J and Wang, Qiang and Yan, X and Cao, Z and Wang, G
Keywords: Electroencephalogram, microstate spectral analysis, multivariate empirical mode decomposition, sedation, transition of consciousness, EEG, brain state
Journal or Publication Title: IEEE Journal of Biomedical and Health Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 2168-2208
DOI / ID Number: 10.1109/JBHI.2020.3008052
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copyright 2020 IEEE

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