Detection of consciousness by electroencephalogram and auditory evoked potentials.
BACKGROUND
A set of electroencephalographic and auditory evoked potential (AEP) parameters should be identified that allows separation of consciousness from unconsciousness (reflected by responsiveness/unresponsiveness to command).


METHODS
Forty unpremedicated patients received anesthesia with remifentanil and either sevoflurane or propofol. With remifentanil infusion (0.2 microg . kg . min), patients were asked every 30 s to squeeze the investigator's hand. Sevoflurane or propofol was given until loss of consciousness. After intubation, propofol or sevoflurane was stopped until patients followed the command (return of consciousness). Thereafter, propofol or sevoflurane was started again (loss of consciousness), and surgery was performed. Return of consciousness was observed after surgery. The electroencephalogram and AEP from immediately before and after the transitions were selected. Logistic regression was calculated to identify models for the separation between consciousness and unconsciousness. For the top 10 models, 1,000-fold cross-validation was performed. Backward variable selection was applied to identify a minimal model. Prediction probability was calculated. The digitized electroencephalogram was replayed, and the Bispectral Index was measured and accordingly analyzed.


RESULTS
The best full model (prediction probability 0.89) contained 15 AEP and 4 electroencephalographic parameters. The best minimal model (prediction probability 0.87) contained 2 AEP and 2 electroencephalographic parameters (median frequency of the amplitude spectrum from 8-30 Hz and approximate entropy). The prediction probability of the Bispectral Index was 0.737.


CONCLUSIONS
A combination of electroencephalographic and AEP parameters can be used to differentiate between consciousness and unconsciousness even in a very challenging data set. The minimal model contains a combination of AEP and electroencephalographic parameters and has a higher prediction probability than Bispectral Index for the separation between consciousness and unconsciousness.
Schneider
Gerhard
G
Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany. gerhard.schneider@lrz.tum.de
Hollweck
Regina
R
Ningler
Michael
M
Stockmanns
Gudrun
G
Kochs
Eberhard F
EF
D000328:Adult; D000465:Algorithms; D000768:Anesthesia, General; D003243:Consciousness / Q000187:drug effects*; D004569:Electroencephalography / Q000187:drug effects*; D005072:Evoked Potentials, Auditory / Q000187:drug effects*; D005260:Female; D006439:Hemodynamics / Q000187:drug effects; D006801:Humans; D008297:Male; D008568:Memory / Q000187:drug effects; D016343:Monitoring, Intraoperative