Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia
Name
journal.pone.0246165.pdf
Description
Published version
Size
2.12 MB
Format
Adobe PDF
Checksum (MD5)
351f4dd28896b51f6df2650a9459628e
Author(s)
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
Date Issued
2021
Journal
PLoS ONE
Publisher
Public Library of Science (PLoS)
Citation
Abel, John H, Badgeley, Marcus A, Meschede-Krasa, Benyamin, Schamberg, Gabriel, Garwood, Indie C et al. 2021. "Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia." PLoS ONE, 16 (5).
Version
Final published version
Abstract
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.
MIT Department
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Harvard University--MIT Division of Health Sciences and Technology
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/JOURNAL.PONE.0246165
