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Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia

In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning appro...

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Autores principales: Abel, John H., Badgeley, Marcus A., Meschede-Krasa, Benyamin, Schamberg, Gabriel, Garwood, Indie C., Lecamwasam, Kimaya, Chakravarty, Sourish, Zhou, David W., Keating, Matthew, Purdon, Patrick L., Brown, Emery N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101756/
https://www.ncbi.nlm.nih.gov/pubmed/33956800
http://dx.doi.org/10.1371/journal.pone.0246165
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author Abel, John H.
Badgeley, Marcus A.
Meschede-Krasa, Benyamin
Schamberg, Gabriel
Garwood, Indie C.
Lecamwasam, Kimaya
Chakravarty, Sourish
Zhou, David W.
Keating, Matthew
Purdon, Patrick L.
Brown, Emery N.
author_facet Abel, John H.
Badgeley, Marcus A.
Meschede-Krasa, Benyamin
Schamberg, Gabriel
Garwood, Indie C.
Lecamwasam, Kimaya
Chakravarty, Sourish
Zhou, David W.
Keating, Matthew
Purdon, Patrick L.
Brown, Emery N.
author_sort Abel, John H.
collection PubMed
description In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95—0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88—0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients’ neural activity.
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spelling pubmed-81017562021-05-17 Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia Abel, John H. Badgeley, Marcus A. Meschede-Krasa, Benyamin Schamberg, Gabriel Garwood, Indie C. Lecamwasam, Kimaya Chakravarty, Sourish Zhou, David W. Keating, Matthew Purdon, Patrick L. Brown, Emery N. PLoS One Research Article In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95—0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88—0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients’ neural activity. Public Library of Science 2021-05-06 /pmc/articles/PMC8101756/ /pubmed/33956800 http://dx.doi.org/10.1371/journal.pone.0246165 Text en © 2021 Abel et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Abel, John H.
Badgeley, Marcus A.
Meschede-Krasa, Benyamin
Schamberg, Gabriel
Garwood, Indie C.
Lecamwasam, Kimaya
Chakravarty, Sourish
Zhou, David W.
Keating, Matthew
Purdon, Patrick L.
Brown, Emery N.
Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia
title Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia
title_full Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia
title_fullStr Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia
title_full_unstemmed Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia
title_short Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia
title_sort machine learning of eeg spectra classifies unconsciousness during gabaergic anesthesia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101756/
https://www.ncbi.nlm.nih.gov/pubmed/33956800
http://dx.doi.org/10.1371/journal.pone.0246165
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