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使用高级脑电微状态谱分析技术对异丙酚诱导镇静期间的大脑状态进行有效估计。

Effective Brain State Estimation During Propofol-Induced Sedation Using Advanced EEG Microstate Spectral Analysis.

出版信息

IEEE J Biomed Health Inform. 2021 Apr;25(4):978-987. doi: 10.1109/JBHI.2020.3008052. Epub 2021 Apr 6.

DOI:10.1109/JBHI.2020.3008052
PMID:32749987
Abstract

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.

摘要

脑状态是神经元同步的模式,脑电图 (EEG) 微状态为特征化和分析同步神经放电提供了一种很有前途的工具。然而,在意识转变期间,如镇静,每个主要微状态的拓扑谱信息仍然不清楚,EEG 微状态的实际应用值得探究。此外,麻醉诱导的脑状态改变背后的机制仍知之甚少。在这项研究中,使用 Hilbert-Huang 变换中的多变量经验模态分解对高级 EEG 微状态谱进行了分析。进一步在异丙酚诱导意识转变期间的头皮 EEG 记录中研究了其实用性。从清醒基线到中度镇静的转变过程伴随着微状态(A、B 和 F)能量的明显增加,特别是在全脑 delta 波段、额 alpha 波段和 beta 波段。与通常用于测量麻醉深度的其他有效基于 EEG 的参数相比,使用所选的谱特征达到了更好的性能(80%的灵敏度,90%的准确性),以估计镇静期间的脑状态。微状态能量的变化也与镇静期间个体行为数据高度相关。总之,EEG 微状态谱分析是一种估计异丙酚诱导镇静期间脑状态的有效方法,深入了解了潜在的机制。生成的谱特征可以作为动态评估意识水平的有前途的标志物。

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