title: Electroencephalographic order pattern analysis for the separation of consciousness and unconsciousness: an analysis of approximate entropy, permutation entropy, recurrence rate, and phase coupling of order recurrence plots.
abstract: BACKGROUND
  Nonlinear electroencephalographic parameters, e.g., approximate entropy, have been suggested as measures of the hypnotic component of anesthesia. Compared with linear methods, they may detect additional information and quantify the irregularity of a dynamical system. High dimensionality of a signal and disturbances may affect these parameters and change their ability to distinguish consciousness from unconsciousness. Methods of order pattern analysis, in this investigation represented by permutation entropy, recurrence rate, and phase coupling of order recurrence plots, are suitable for any type of time series, whether deterministic or noisy. They may provide a better estimation of the hypnotic component of anesthesia than other nonlinear parameters.
  
  
  METHODS
  The current analysis is based on electroencephalographic data from two similar clinical studies in adult patients undergoing general anesthesia with sevoflurane or propofol. The study period was from induction until patients followed command after surgery, including a reduction of the hypnotic agent after tracheal intubation until patients followed command. Prediction probability was calculated to assess the parameter's ability to separate consciousness from unconsciousness at the transition between both states.
  
  
  RESULTS
  Parameters of order pattern analysis provide a prediction probability of maximal 0.85 (training study) and 0.78 (evaluation study) with frequencies from 0 to 30 Hz, and maximal 0.87 (training study) and 0.83 (evaluation study) including frequencies up to 70 Hz, both higher than 0.77 (approximate entropy).
  
  
  CONCLUSIONS
  Parameters of the nonlinear method order pattern analysis separate consciousness from unconsciousness and are grossly independent of high-frequency components of the electroencephalogram.
authors.affiliation: Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Germany.
authors.forename: Denis
authors.initials: D
authors.lastname: Jordan
authors.forename: Gudrun
authors.initials: G
authors.lastname: Stockmanns
authors.forename: Eberhard F
authors.initials: EF
authors.lastname: Kochs
authors.forename: Stefanie
authors.initials: S
authors.lastname: Pilge
authors.forename: Gerhard
authors.initials: G
authors.lastname: Schneider
mesh_terms: D000328:Adult; D000758:Anesthesia / Q000379:methods / Q000592:standards; D003243:Consciousness / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods* / Q000592:standards; D019277:Entropy; D005544:Forecasting; D006801:Humans; D013997:Time Factors; D014474:Unconsciousness / Q000175:diagnosis / Q000503:physiopathology*