Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs

Full metadata record
DC Field Value Language
dc.contributor.authorCha, Kab-Mun-
dc.contributor.authorChoi, Byung-Moon-
dc.contributor.authorNoh, Gyu-Jeong-
dc.contributor.authorShin, Hyun-Chool-
dc.date.available2018-05-09T02:09:08Z-
dc.date.created2018-04-17-
dc.date.issued2017-03-
dc.identifier.issn1687-5265-
dc.identifier.urihttp://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/7402-
dc.description.abstractIn this paper, we propose novel methods for measuring depth of anesthesia (DOA) by quantifying dominant information flow in multichannel EEGs. Conventional methods mainly use few EEG channels independently and most of multichannel EEG based studies are limited to specific regions of the brain. Therefore the function of the cerebral cortex over wide brain regions is hardly reflected in DOA measurement. Here, DOA is measured by the quantification of dominant information flow obtained from principle bipartition. Three bipartitioningmethods are used to detect the dominant information flowin entire EEG channels and the dominant information flow is quantified by calculating information entropy. High correlation between the proposedmeasures and the plasma concentration of propofol is confirmed fromthe experimental results of clinical data in 39 subjects. To illustrate the performance of the proposedmethodsmore easily we present the results formultichannel EEG on a two-dimensional (2D) brainmap.-
dc.language영어-
dc.language.isoen-
dc.publisherHINDAWI LTD-
dc.relation.isPartOfCOMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE-
dc.subjectSPECTRAL EDGE FREQUENCY-
dc.subjectCENTRAL-NERVOUS-SYSTEM-
dc.subjectHEART-RATE-VARIABILITY-
dc.subjectGENERAL-ANESTHESIA-
dc.subjectPROPOFOL-
dc.subjectAWARENESS-
dc.subjectSEVOFLURANE-
dc.subjectELECTROENCEPHALOGRAM-
dc.subjectPHARMACODYNAMICS-
dc.subjectSURGERY-
dc.titleNovel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs-
dc.typeArticle-
dc.identifier.doi10.1155/2017/3521261-
dc.type.rimsART-
dc.identifier.bibliographicCitationCOMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE-
dc.description.journalClass1-
dc.identifier.wosid000405742200001-
dc.identifier.scopusid2-s2.0-85016620263-
dc.citation.titleCOMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE-
dc.contributor.affiliatedAuthorShin, Hyun-Chool-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.oadoiVersionpublished-
dc.subject.keywordPlusSPECTRAL EDGE FREQUENCY-
dc.subject.keywordPlusCENTRAL-NERVOUS-SYSTEM-
dc.subject.keywordPlusHEART-RATE-VARIABILITY-
dc.subject.keywordPlusGENERAL-ANESTHESIA-
dc.subject.keywordPlusPROPOFOL-
dc.subject.keywordPlusAWARENESS-
dc.subject.keywordPlusSEVOFLURANE-
dc.subject.keywordPlusELECTROENCEPHALOGRAM-
dc.subject.keywordPlusPHARMACODYNAMICS-
dc.subject.keywordPlusSURGERY-
dc.description.journalRegisteredClassscie-
Files in This Item
Go to Link
Appears in
Collections
College of Information Technology > ETC > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Shin, Hyun Chool photo

Shin, Hyun Chool
College of Information Technology (Department of Electronic Engineering)
Read more

Altmetrics

Total Views & Downloads

BROWSE