Linked Life Data

14565027

Source:http://linkedlifedata.com/resource/pubmed/id/14565027

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2003-10-20
pubmed:abstractText
The aim of this study was to develop a new method of epileptic prediction using nonlinear dynamic theory. When rat was falling sickness, its EEG was researched by using approximate entropy and correlation dimension. The results showed the approximate entropy and correlation dimension during epileptic seizure are obviously lower than those before seizure and after seizure. The span of time before seizure is a special phase. Before the seizure symptom appeared, the complexity of EEG had begun declining. Thus, the outbreak of epilepsy could be predicted in short time using nonlinear dynamic methods.
pubmed:language
chi
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1001-5515
pubmed:author
pubmed:issnType
Print
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
511-4
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
[Nonlinear analysis on the EEG information of rat epileptic model].
pubmed:affiliation
Department of Medical Engineering, Second Artillery General Hospital, Beijing 100088.
pubmed:publicationType
Journal Article, English Abstract