Linked Life Data

20126649

Source:http://linkedlifedata.com/resource/pubmed/id/20126649

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-2-3
pubmed:abstractText
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.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1932-6203
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
e8876
pubmed:dateRevised
2010-9-27
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Anaesthesia monitoring by recurrence quantification analysis of EEG data.
pubmed:affiliation
Bioelectronics, Vienna University of Technology, Vienna, Austria. klaus.becker@meduniwien.ac.at
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't