Linked Life Data

11214261

Source:http://linkedlifedata.com/resource/pubmed/id/11214261

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2001-2-13
pubmed:abstractText
A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics).
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0140-0118
pubmed:author
pubmed:issnType
Print
pubmed:volume
39
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
105-12
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms.
pubmed:affiliation
Department of Medical Physics, Medical School, University of Ioannina, Greece.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't