Linked Life Data

16761834

Source:http://linkedlifedata.com/resource/pubmed/id/16761834

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2006-6-9
pubmed:abstractText
Entropy and complexity of the electroencephalogram (EEG) have recently been proposed as measures of depth of anesthesia and sedation. Using surrogate data of predefined spectrum and probability distribution we show that the various algorithms used for the calculation of entropy and complexity actually measure different properties of the signal. The tested methods, Shannon entropy (ShEn), spectral entropy, approximate entropy (ApEn), Lempel-Ziv complexity (LZC), and Higuchi fractal dimension (HFD) are then applied to the EEG signal recorded during sedation in the intensive care unit (ICU). It is shown that the applied measures behave in a different manner when compared to clinical depth of sedation score--the Ramsay score. ShEn tends to increase while the other tested measures decrease with deepening sedation. ApEn, LZC, and HFD are highly sensitive to the presence of high-frequency components in the EEG signal.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0018-9294
pubmed:author
pubmed:issnType
Print
pubmed:volume
53
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1067-77
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Comparison of entropy and complexity measures for the assessment of depth of sedation.
pubmed:affiliation
Department of Information Technology, Tampere University of Technology, Pori, Finland.
pubmed:publicationType
Journal Article, Comparative Study, Controlled Clinical Trial, Research Support, Non-U.S. Gov't