Literature DB >> 1763588

A comparison of median frequency, spectral edge frequency, a frequency band power ratio, total power, and dominance shift in the determination of depth of anesthesia.

J C Drummond1, C A Brann, D E Perkins, D E Wolfe.   

Abstract

Five numerical descriptors were derived from the electroencephalogram (EEG), recorded, and processed (Tracor Nomad) during emergence from isoflurane-nitrous oxide anesthesia. The five descriptors (median frequency, spectral edge frequency-90%, total power, a frequency band power ratio, and the ratio of frontal to occipital power) were compared for their ability to predict imminent arousal. Arousal was defined as spontaneous movement, coughing or eye opening. All of the descriptors except the frontal-occipital power ratio underwent significant (P less than 0.05) changes between the initial recordings made intraoperatively during surgical stimulus under anesthesia and later recordings in the 40 s preceding arousal. A post hoc analysis was performed to identify the threshold value for each parameter that best served to predict imminent arousal. For median frequency, spectral edge frequency-90%, total power, and the frequency band power ratio, thresholds that predicted imminent arousal with sensitivities of 90% and specificities of 82-90% could be identified. The data indicate that, even in the favorable circumstances of the present study (uniform anesthetic technique, post hoC identification of thresholds), none of several previously popularized EEG descriptors (median frequency, spectral edge frequency-90%, total power, a frequency band power ratio) can serve as a completely reliable sole predictor of imminent arousal. As presently derived, these EEG descriptors at best provide trend information to be used in concert with other clinical signs of depth of anesthesia.

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Year:  1991        PMID: 1763588     DOI: 10.1111/j.1399-6576.1991.tb03374.x

Source DB:  PubMed          Journal:  Acta Anaesthesiol Scand        ISSN: 0001-5172            Impact factor:   2.105


  14 in total

1.  Predicting movement during anaesthesia by complexity analysis of electroencephalograms.

Authors:  X S Zhang; R J Roy
Journal:  Med Biol Eng Comput       Date:  1999-05       Impact factor: 2.602

2.  Intraoperative electroencephalographic monitoring: quantitative analysis of bioelectrical data detected during surgical stimulation.

Authors:  G Trucchi; L Bergamasco; V Argento
Journal:  Vet Res Commun       Date:  2003-09       Impact factor: 2.459

Review 3.  Clinical Electroencephalography for Anesthesiologists: Part I: Background and Basic Signatures.

Authors:  Patrick L Purdon; Aaron Sampson; Kara J Pavone; Emery N Brown
Journal:  Anesthesiology       Date:  2015-10       Impact factor: 7.892

4.  A study of electroencephalographic descriptors and end-tidal concentration in estimating depth of anesthesia.

Authors:  J Muthuswamy; A Sharma
Journal:  J Clin Monit       Date:  1996-09

5.  Density-modulated t's array, a new technique of processed electroencephalogram, for monitoring the effects of midazolam and nitrous oxide during spinal anesthesia.

Authors:  Hidekazu Katoh; Yoshio Kinefuchi; Mamoru Takiguchi; Yonosuke Yamasaki
Journal:  J Anesth       Date:  1994-12       Impact factor: 2.078

6.  Neural Correlates of Consciousness at Near-Electrocerebral Silence in an Asphyxial Cardiac Arrest Model.

Authors:  Donald E Lee; Lauren G Lee; Danny Siu; Afsheen K Bazrafkan; Maryam H Farahabadi; Tin J Dinh; Josue Orellana; Wei Xiong; Beth A Lopour; Yama Akbari
Journal:  Brain Connect       Date:  2017-04

7.  EEG Predicts movement response to surgical stimuli during general anesthesia with combinations of isoflurane, 70% N2O, and fentanyl.

Authors:  R C Dutton; W D Smith; N T Smith
Journal:  J Clin Monit       Date:  1996-03

8.  In vivo relationships between the cerebral pharmacokinetics and pharmacodynamics of thiopentone in sheep after short-term administration.

Authors:  R N Upton; G L Ludbrook; C Grant; E C Gray
Journal:  J Pharmacokinet Biopharm       Date:  1996-02

9.  Non-invasive combined surrogates of remifentanil blood concentrations with relevance to analgesia.

Authors:  Jörn Lötsch; Carsten Skarke; Jutta Darimont; Michael Zimmermann; Lutz Bräutigam; Gerd Geisslinger; Alfred Ultsch; Bruno G Oertel
Journal:  Naunyn Schmiedebergs Arch Pharmacol       Date:  2013-06-18       Impact factor: 3.000

10.  Automated detection of anesthetic depth levels using chaotic features with artificial neural networks.

Authors:  V Lalitha; C Eswaran
Journal:  J Med Syst       Date:  2007-12       Impact factor: 4.460

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