Spectral EEG correlations from the different phases of general anesthesia.

Bayesian statistics EEG segmentation alpha rhythms correlation analysis iso-electric suppressions optimal fit time-frequency analysis

Journal

Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047

Informations de publication

Date de publication:
2023
Historique:
received: 01 08 2022
accepted: 15 02 2023
entrez: 23 3 2023
pubmed: 24 3 2023
medline: 24 3 2023
Statut: epublish

Résumé

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

Identifiants

pubmed: 36950512
doi: 10.3389/fmed.2023.1009434
pmc: PMC10025404
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1009434

Informations de copyright

Copyright © 2023 Sun, Longrois and Holcman.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Références

Clin Neurophysiol. 2019 Jan;130(1):55-60
pubmed: 30476711
Br J Anaesth. 2019 Aug;123(2):206-218
pubmed: 31202561
N Engl J Med. 2010 Dec 30;363(27):2638-50
pubmed: 21190458
Commun Biol. 2019 Sep 2;2:327
pubmed: 31508502
Prog Neurobiol. 2012 Sep;98(3):265-78
pubmed: 22420981
Science. 2008 Nov 7;322(5903):876-80
pubmed: 18988836
J Neurophysiol. 2001 Jul;86(1):1-39
pubmed: 11431485
J Neural Eng. 2015 Jun;12(3):036013
pubmed: 25973635
IEEE Trans Neural Syst Rehabil Eng. 2022;30:286-295
pubmed: 35085086
Br J Anaesth. 1998 Jun;80(6):767-75
pubmed: 9771306
Sleep. 1979;2(2):175-91
pubmed: 232563
Anesth Analg. 1984 Apr;63(4):386-93
pubmed: 6230952
Proc Natl Acad Sci U S A. 2017 Aug 8;114(32):E6660-E6668
pubmed: 28743752
World J Methodol. 2018 Oct 12;8(2):9-16
pubmed: 30345225
Psychon Bull Rev. 2012 Dec;19(6):1057-64
pubmed: 22798023
Anaesthesia. 2008 Sep;63(9):954-8
pubmed: 18557970
PLoS Comput Biol. 2018 Aug 30;14(8):e1006424
pubmed: 30161118
J Neurosci. 2013 Jul 3;33(27):11070-5
pubmed: 23825412
Anesthesiology. 2015 Oct;123(4):937-60
pubmed: 26275092
Proc Natl Acad Sci U S A. 2013 Mar 19;110(12):E1142-51
pubmed: 23487781
Front Syst Neurosci. 2017 May 29;11:36
pubmed: 28611600
Neuroscientist. 2005 Aug;11(4):357-72
pubmed: 16061522
Clin Pharmacokinet. 1995 Aug;29(2):80-94
pubmed: 7586903
Anesth Analg. 2020 Nov;131(5):1529-1539
pubmed: 33079876

Auteurs

Christophe Sun (C)

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

Dan Longrois (D)

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

David Holcman (D)

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

Classifications MeSH