Evidence map›Paper›PMID 36950512›Full record

ArticleFrontiers in medicine2023

Spectral EEG correlations from the different phases of general anesthesia.

Christophe Sun, Dan Longrois, David Holcman

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Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Christophe SunGroup of Data Modeling, Computational Biology and Predictive Medicine, Institut de Biologie (IBENS), École Normale Supérieure, Université PSL, Paris, France.
Dan LongroisDépartement d'Anesthésie-Réanimation, Hôpital Bichat-Claude Bernard, Assistance Publique-Hôpitaux de Paris, Paris, France.
David HolcmanGroup of Data Modeling, Computational Biology and Predictive Medicine, Institut de Biologie (IBENS), École Normale Supérieure, Université PSL, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

alpha rhythmsBayesian statisticscorrelation analysisEEG segmentationiso-electric suppressionsoptimal fittime-frequency analysis

Identifiers

PMID36950512
PMCPMC10025404

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