Affiliation:
1. Postgraduate Fellow, Stanford University School of Medicine.
2. Professor of Anesthesiology, Chairman of the Department of Anesthesiology and Intensive Care Medicine, University of Bonn.
3. Staff Anesthesiologist, Palo Alto VA Health Care Center. Associate Professor of Anesthesia, Stanford University School of Medicine.
Abstract
Background
Artifact robustness (i.e., size of deviation of an electroencephalographic parameter value from baseline caused by artifacts) and baseline stability (i.e., consistency of median baseline values) of electroencephalographic parameters profoundly influence electroencephalography-based pharmacodynamic parameter estimation and the usefulness of the processed electroencephalogram as measure of the arousal state of the central nervous system (depth of anesthesia). In this study, the authors compared the artifact robustness and the interindividual and intraindividual baseline stability of several univariate descriptors of the electroencephalogram (Shannon entropy, approximate entropy, spectral edge frequency 95, delta ratio, and canonical univariate parameter).
Methods
Electroencephalographic data of 16 healthy volunteers before and after administration of an intravenous bolus of propofol (2 mg/kg body weight) were analyzed. Each volunteer was studied twice. The baseline electroencephalogram was recorded for a median of 18 min before drug administration. For each electroencephalographic descriptor, the authors calculated the following: (1) baseline variability (= (median baseline - median effect) [i.e., signal]/SD baseline [i.e., noise]) without artifact rejection; (2) baseline variability with artifact rejection; and (3) baseline stability within and between individuals (= (median baseline - median effect) averaged over all volunteers/SD of all median baselines).
Results
Without artifact rejection, Shannon entropy and canonical univariate parameter displayed the highest signal-to-noise ratio. After artifact rejection, approximate entropy, Shannon entropy, and the canonical univariate parameter displayed the highest signal-to-noise ratio. Baseline stability within and between individuals was highest for approximate entropy.
Conclusions
With regard to robustness against artifacts, the electroencephalographic entropy parameters and the canonical univariate parameter were superior to spectral edge frequency 95 and delta ratio. Electroencephalographic approximate entropy displayed the best interindividual and intraindividual baseline stability.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Anesthesiology and Pain Medicine
Cited by
25 articles.
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