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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-8101756 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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