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

Citation

Li, Y and Shi, W and Liu, Z and Li, J and Wang, Q and Yan, X and Cao, Z and Wang, G, Effective brain state estimation during propofol-induced sedation using advanced EEG microstate spectral analysis, IEEE Journal of Biomedical and Health Informatics pp. 1-10. ISSN 2168-2208 (2020) [Refereed Article]


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Copyright Statement

copyright 2020 IEEE

DOI: doi:10.1109/JBHI.2020.3008052

Abstract

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 Details

Item Type:Refereed Article
Keywords:electroencephalogram, microstate spectral analysis, multivariate empirical mode decomposition, sedation, transition of consciousness, EEG, brain state
Research Division:Information and Computing Sciences
Research Group:Library and information studies
Research Field:Human information interaction and retrieval
Objective Division:Health
Objective Group:Clinical health
Objective Field:Clinical health not elsewhere classified
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:139984
Year Published:2020
Web of Science® Times Cited:5
Deposited By:Information and Communication Technology
Deposited On:2020-07-21
Last Modified:2020-12-09
Downloads:9 View Download Statistics

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