Literature DB >> 19459551

Comparison of contemporary EEG derived depth of anesthesia monitors with a 5 step validation process.

B Heyse1, B Van Ooteghem, B Wyler, M M R F Struys, L Herregods, H Vereecke.   

Abstract

During the last decennium, a growing number of depth of anesthesia monitors, extracting information from the spontaneous electroencephalogram (EEG) have been developed and commercialized. The growing interest in depth of anesthesia monitoring resulted in an intensified technological progress. Innovations on both hardware and mathematical algorithms were introduced for improving the extraction of data. Because of the abundance of monitors now commercially available, it becomes increasingly important to develop a standardized reproducible methodology for comparing depth of anesthesia monitors. In this review, the authors present a strategy to compare monitors of the hypnotic component of anesthesia, based on the available literature and their own experience with validation studies. They also discuss the level of validation of the most commonly used EEG derived depth of anesthesia monitors.

Mesh:

Year:  2009        PMID: 19459551

Source DB:  PubMed          Journal:  Acta Anaesthesiol Belg        ISSN: 0001-5164


  6 in total

1.  Brain Monitoring of Sedation in the Intensive Care Unit Using a Recurrent Neural Network.

Authors:  Haoqi Sun; Sunil B Nagaraj; Oluwaseun Akeju; Patrick L Purdon; Brandon M Westover
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2018-07

Review 2.  Monitoring the depth of anaesthesia.

Authors:  Bojan Musizza; Samo Ribaric
Journal:  Sensors (Basel)       Date:  2010-12-03       Impact factor: 3.576

3.  Evaluation of the Cerebral State Index in Cats under Isoflurane Anaesthesia: Dose-Effect Relationship and Prediction of Clinical Signs.

Authors:  Joana R Sousa; Lénio Ribeiro; Aura Silva; David A Ferreira
Journal:  Vet Med Int       Date:  2014-01-30

4.  Assessing anesthetic activity through modulation of the membrane dipole potential.

Authors:  Benjamin Michael Davis; Jonathan Brenton; Sterenn Davis; Ehtesham Shamsher; Claudia Sisa; Ljuban Grgic; M Francesca Cordeiro
Journal:  J Lipid Res       Date:  2017-08-17       Impact factor: 5.922

5.  Normative values for SedLine-based processed electroencephalography parameters in awake volunteers: a prospective observational study.

Authors:  Alessandro Belletti; Thummaporn Naorungroj; Fumitaka Yanase; Glenn M Eastwood; Laurence Weinberg; Rinaldo Bellomo
Journal:  J Clin Monit Comput       Date:  2020-11-11       Impact factor: 1.977

6.  EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks.

Authors:  Quan Liu; Yi-Feng Chen; Shou-Zen Fan; Maysam F Abbod; Jiann-Shing Shieh
Journal:  Comput Math Methods Med       Date:  2015-09-28       Impact factor: 2.238

  6 in total

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