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

• Main Authors: Cao, Z. (Author), Li, J. (Author), Li, Y. (Author), Liu, Z. (Author), Shi, W. (Author), Wang, G. (Author), Wang, Q. (Author), Yan, X. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: adult; algorithm; altered state of consciousness; anesthesia level; Anesthetic-depth; Anesthetics; Article; behavior; Behavioral data; bispectral index; blood sampling; brain; Brain; brain analysis; brain mapping; Brain Mapping; clinical article; conscious sedation; consciousness; Consciousness; consciousness level; continuous infusion; correlation analysis; decomposition; disease marker; dorsal anterior cingulate cortex; Electroencephalogram; Electro-encephalogram (EEG); electroencephalogram microstate spectral analysis; electroencephalography; Electroencephalography; empirical mode decomposition; event related potential; female; Fourier transform; functional connectivity; Hilbert Huang transform; Hilbert Huang transforms; human; human cell; human experiment; Humans; hypnosis; Induced transitions; male; mathematical phenomena; Mathematical transformations; microstate spectral analysis; multivariate empirical mode decomposition; Multivariate empirical mode decomposition (MEMD); normal human; principal component analysis; program effectiveness; propofol; Propofol; scalp; sedation; signal noise ratio; signal processing; Signal processing; Spectral feature; Spectral information; spectroscopy; Spectrum analysis; support vector machine; topography; transition of consciousness; wakefulness
• Online Access: View Fulltext in Publisher

 Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provides a promising tool to 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, an advanced 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 the awake baseline 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, using 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. © 2013 IEEE.