Anaesthesia monitoring by recurrence quantification analysis of EEG data.

PloS One
Klaus BeckerHans-Ulrich Dodt

Abstract

Appropriate monitoring of the depth of anaesthesia is crucial to prevent deleterious effects of insufficient anaesthesia on surgical patients. Since cardiovascular parameters and motor response testing may fail to display awareness during surgery, attempts are made to utilise alterations in brain activity as reliable markers of the anaesthetic state. Here we present a novel, promising approach for anaesthesia monitoring, basing on recurrence quantification analysis (RQA) of EEG recordings. This nonlinear time series analysis technique separates consciousness from unconsciousness during both remifentanil/sevoflurane and remifentanil/propofol anaesthesia with an overall prediction probability of more than 85%, when applied to spontaneous one-channel EEG activity in surgical patients.

References

Jul 23, 2003·Anesthesia and Analgesia·M MessnerK Tschaikowsky
Oct 27, 2005·Anesthesiology·Gerhard SchneiderEberhard F Kochs
Aug 22, 2007·Neuroscience Letters·Xiaoli LiGaoxiang Ouyang
Feb 4, 2010·PloS One·Klaus BeckerHans-Ulrich Dodt

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Citations

Nov 5, 2013·BioMed Research International·L MesinR Cattaneo
Dec 18, 2013·Journal of Clinical Monitoring and Computing·Luca Mesin, Paolo Costa
Sep 10, 2015·Journal of Clinical Monitoring and Computing·Zhenhu LiangXiaoli Li
Feb 4, 2010·PloS One·Klaus BeckerHans-Ulrich Dodt
Jul 4, 2013·Clinical EEG and Neuroscience·Nicoletta Nicolaou, Julius Georgiou
Apr 6, 2011·Anesthesiology·George A MashourMichael S Avidan
Oct 8, 2019·Computer Methods and Programs in Biomedicine·Verónica Barroso-GarcíaRoberto Hornero

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Software Mentioned

RQA
GE

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