Emergence EEG pattern classification in sevoflurane anesthesia.

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dc.contributor.author Liang, Zhenhu en
dc.contributor.author Huang, Cheng en
dc.contributor.author Li, Yongwang en
dc.contributor.author Hight, Darren F en
dc.contributor.author Voss, Logan J en
dc.contributor.author Sleigh, James en
dc.contributor.author Li, Xiaoli en
dc.contributor.author Bai, Yang en
dc.date.accessioned 2018-10-15T21:49:32Z en
dc.date.issued 2018-04-26 en
dc.identifier.citation Physiological measurement 39(4):045006 26 Apr 2018 en
dc.identifier.issn 0967-3334 en
dc.identifier.uri http://hdl.handle.net/2292/41833 en
dc.description.abstract OBJECTIVE:Significant spectral electroencephalogram (EEG) pattern characteristics exist in individual patients during the re-establishment of consciousness after general anesthesia. However, these EEG patterns cannot be quantitatively identified using commercially available depth of anesthesia (DoA) monitors. This study proposes an effective classification method and indices to classify these patterns among patients. APPROACH:Four types of emergence EEG patterns were identified based on the EEG data set from 52 patients undergoing sevoflurane general anesthesia from two hospitals. Then, the relative power spectrum density (RPSD) of five frequency sub-bands of clinical interest (delta, theta, alpha, beta and gamma) were selected for emergence state analysis. Finally, a genetic algorithm support vector machine (GA-SVM) was used to identify the emergence EEG patterns. The performance was reported in terms of sensitivity (SE), specificity (SP) and accuracy (AC). MAIN RESULTS:The combination of the mean and mode of RPSD in the delta and alpha band (P (delta)/P (alpha) performed the best in the GA-SVM classification. The AC indices obtained by GA-SVM across the four patterns were 90.64  ±  7.61, 81.79  ±  5.84, 82.14  ±  7.99 and 72.86  ±  11.11 respectively. Furthermore, the emergence time of the patients with EEG emergence patterns I and III increased as the patients' age increased. However, for patients with EEG emergence pattern IV, the emergence time positively correlates with the patients' age when they are under 50, and negatively correlates with it when they are over 50. SIGNIFICANCE:The mean and mode of P (delta)/P (alpha) is a useful index to classify the different emergence EEG patterns. In addition, these patterns may correlate with an underlying neural substrate which is related to the patients' age. Highlights ► Four emergence EEG patterns were found in γ-amino-butyric acid (GABA)-ergic anesthetic drugs. ► A genetic algorithm combined with a support vector machine (GA-SVM) was proposed to identify the emergence EEG patterns. ► The relative power spectrum density (RPSD) was used as a feature to classify the emergence EEG patterns and good accuracy was achieved. ► The statistics shows that the emergence EEG patterns are age-related and may have value in assessing postoperative brain states. en
dc.format.medium Electronic en
dc.language eng en
dc.relation.ispartofseries Physiological measurement en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Humans en
dc.subject Electroencephalography en
dc.subject Anesthesia en
dc.subject Signal Processing, Computer-Assisted en
dc.subject Aged en
dc.subject Middle Aged en
dc.subject Female en
dc.subject Male en
dc.subject Young Adult en
dc.subject Support Vector Machine en
dc.subject Sevoflurane en
dc.title Emergence EEG pattern classification in sevoflurane anesthesia. en
dc.type Journal Article en
dc.identifier.doi 10.1088/1361-6579/aab4d0 en
pubs.issue 4 en
pubs.begin-page 045006 en
pubs.volume 39 en
dc.rights.holder Copyright: Institute of Physics and Engineering in Medicine en
dc.identifier.pmid 29513276 en
pubs.publication-status Published en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Research Support, Non-U.S. Gov't en
pubs.subtype Journal Article en
pubs.elements-id 732177 en
pubs.org-id Medical and Health Sciences en
pubs.org-id School of Medicine en
pubs.org-id Anaesthesiology en
dc.identifier.eissn 1361-6579 en
pubs.record-created-at-source-date 2018-03-08 en
pubs.dimensions-id 29513276 en

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