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全身麻醉不同阶段的脑电图频谱相关性。

Spectral EEG correlations from the different phases of general anesthesia.

作者信息

Sun Christophe, Longrois Dan, Holcman David

机构信息

Group of Data Modeling, Computational Biology and Predictive Medicine, Institut de Biologie (IBENS), École Normale Supérieure, Université PSL, Paris, France.

Département d'Anesthésie-Réanimation, Hôpital Bichat-Claude Bernard, Assistance Publique-Hôpitaux de Paris, Paris, France.

出版信息

Front Med (Lausanne). 2023 Mar 6;10:1009434. doi: 10.3389/fmed.2023.1009434. eCollection 2023.

Abstract

INTRODUCTION

Electroencephalography (EEG) signals contain transient oscillation patterns commonly used to classify brain states in responses to action, sleep, coma or anesthesia.

METHODS

Using a time-frequency analysis of the EEG, we search for possible causal correlations between the successive phases of general anesthesia. We hypothesize that it could be possible to anticipate recovery patterns from the induction or maintenance phases. For that goal, we track the maximum power of the α-band and follow its time course.

RESULTS AND DISCUSSION

We quantify the frequency shift of the α-band during the recovery phase and the associated duration. Using Pearson coefficient and Bayes factor, we report non-significant linear correlation between the α-band frequency and duration shifts during recovery and the presence of the δ or the α rhythms during the maintenance phase. We also found no correlations between the α-band emergence trajectory and the total duration of the flat EEG epochs (iso-electric suppressions) induced by a propofol bolus injected during induction. Finally, we quantify the instability of the α-band using the mathematical total variation that measures possible deviations from a flat line. To conclude, the present correlative analysis shows that EEG dynamics extracted from the initial and maintenance phases of general anesthesia cannot anticipate both the emergence trajectory and the extubation time.

摘要

引言

脑电图(EEG)信号包含瞬态振荡模式,常用于对因行动、睡眠、昏迷或麻醉产生的脑状态进行分类。

方法

通过对脑电图进行时频分析,我们探寻全身麻醉连续阶段之间可能存在的因果关联。我们假设从诱导期或维持期预测恢复模式是可行的。为实现该目标,我们追踪α波段的最大功率并记录其时间进程。

结果与讨论

我们对恢复阶段α波段的频率偏移及相关持续时间进行了量化。使用皮尔逊系数和贝叶斯因子,我们报告了恢复过程中α波段频率和持续时间偏移与维持期δ或α节律的存在之间不存在显著线性相关性。我们还发现α波段出现轨迹与诱导期注射异丙酚推注所诱发的脑电图平坦期(等电位抑制)总持续时间之间无相关性。最后,我们使用测量与平线可能偏差的数学全变差来量化α波段的不稳定性。总之,目前的相关性分析表明,从全身麻醉的初始阶段和维持阶段提取的脑电图动态无法预测苏醒轨迹和拔管时间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b24c/10025404/34255f237403/fmed-10-1009434-g0001.jpg

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