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 withsevoflurane 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.
RCT Entities:
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: Robin von Rotz; Michael Kometer; Dario Dornbierer; Jürg Gertsch; M Salomé Gachet; Franz X Vollenweider; Erich Seifritz; Oliver G Bosch; Boris B Quednow Journal: Psychopharmacology (Berl) Date: 2017-04-20 Impact factor: 4.530
Authors: Matthias Kreuzer; Matthew A Stern; Darren Hight; Sebastian Berger; Gerhard Schneider; James W Sleigh; Paul S García Journal: Anesthesiology Date: 2020-05 Impact factor: 7.892
Authors: Andreas Ranft; Daniel Golkowski; Tobias Kiel; Valentin Riedl; Philipp Kohl; Guido Rohrer; Joachim Pientka; Sebastian Berger; Alexander Thul; Max Maurer; Christine Preibisch; Claus Zimmer; George A Mashour; Eberhard F Kochs; Denis Jordan; Rüdiger Ilg Journal: Anesthesiology Date: 2016-11 Impact factor: 7.892