J Bruhn1, H Röpcke, A Hoeft. 1. Department of Anesthesiology and Intensive Care Medicine, University of Bonn, Germany. jbruhn@mailer.meb.uni-bonn.de
Abstract
BACKGROUND: The authors hypothesized that the electroencephalogram (EEG) during higher anesthetic concentrations would show more "order" and less "randomness" than at lower anesthetic concentrations. "Approximate entropy" is a new statistical parameter derived from the Kolmogorov-Sinai entropy formula which quantifies the amount of regularity in data. The approximate entropy quantifies the predictability of subsequent amplitude values of the EEG based on the knowledge of the previous amplitude values. The authors investigated the dose-response relation of the EEG approximate entropy during desflurane anesthesia in comparison with spectral edge frequency 95, median frequency, and bispectral index. METHODS: Twelve female patients were studied during gynecologic laparotomies. Between opening and closure of the peritoneum, end-tidal desflurane concentrations were varied between 0.5 and 1.6 minimum alveolar concentration (MAC). The EEG approximate entropy, median EEG frequency, spectral edge frequency 95, and bispectral index were determined and the performance of each to predict the desflurane effect compartment concentration, obtained by simultaneous pharmacokinetic-pharmacodynamic modeling, was compared. RESULTS: Electroencephalogram approximate entropy decreased continuously over the observed concentration range of desflurane. The performance of the approximate entropy (prediction probability PK = 0.86 +/- 0.06) as an indicator for desflurane concentrations is similar to spectral edge frequency 95 (PK = 0.86 +/- 0.06) and bispectral index (PK = 0.82 +/- 0.06) and is statistically significantly better than median frequency (PK = 0.78 +/- 0.06). CONCLUSIONS: The amount of regularity in the EEG increases with increasing desflurane concentrations. The approximate entropy could be a useful EEG measure of anesthetic drug effect.
BACKGROUND: The authors hypothesized that the electroencephalogram (EEG) during higher anesthetic concentrations would show more "order" and less "randomness" than at lower anesthetic concentrations. "Approximate entropy" is a new statistical parameter derived from the Kolmogorov-Sinai entropy formula which quantifies the amount of regularity in data. The approximate entropy quantifies the predictability of subsequent amplitude values of the EEG based on the knowledge of the previous amplitude values. The authors investigated the dose-response relation of the EEG approximate entropy during desflurane anesthesia in comparison with spectral edge frequency 95, median frequency, and bispectral index. METHODS: Twelve female patients were studied during gynecologic laparotomies. Between opening and closure of the peritoneum, end-tidal desflurane concentrations were varied between 0.5 and 1.6 minimum alveolar concentration (MAC). The EEG approximate entropy, median EEG frequency, spectral edge frequency 95, and bispectral index were determined and the performance of each to predict the desflurane effect compartment concentration, obtained by simultaneous pharmacokinetic-pharmacodynamic modeling, was compared. RESULTS: Electroencephalogram approximate entropy decreased continuously over the observed concentration range of desflurane. The performance of the approximate entropy (prediction probability PK = 0.86 +/- 0.06) as an indicator for desflurane concentrations is similar to spectral edge frequency 95 (PK = 0.86 +/- 0.06) and bispectral index (PK = 0.82 +/- 0.06) and is statistically significantly better than median frequency (PK = 0.78 +/- 0.06). CONCLUSIONS: The amount of regularity in the EEG increases with increasing desflurane concentrations. The approximate entropy could be a useful EEG measure of anesthetic drug effect.
Authors: Xiaolin Liu; Kathryn K Lauer; B Douglas Ward; Christopher J Roberts; Suyan Liu; Suneeta Gollapudy; Robert Rohloff; William Gross; Zhan Xu; Shanshan Chen; Lubin Wang; Zheng Yang; Shi-Jiang Li; Jeffrey R Binder; Anthony G Hudetz Journal: Brain Imaging Behav Date: 2019-04 Impact factor: 3.978
Authors: Carmen Hernández-Gancedo; David Pestaña; Hanna Pérez-Chrzanowska; Elena Martinez-Casanova; Antonio Criado Journal: J Clin Monit Comput Date: 2007-08-16 Impact factor: 2.502