Linked Life Data

9825319

Source:http://linkedlifedata.com/resource/pubmed/id/9825319

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11 Pt 2
pubmed:dateCreated
1999-2-10
pubmed:abstractText
The morphology of intracardiac electrograms (IEGMs) was used for pacemaker patient workload estimation. The body posture also was studied as another characteristic. The IEGMs were obtained and recorded via temporary transcutaneous leads connected to the implanted pacemaker. IEGMs were recorded during exercise and at rest. Recordings at rest were performed in different body positions. The morphology was analyzed visually in order to observe changes due to workload and posture. The recordings were digitized and processed by a computer-simulated neural network. The network was used as an automatic IEGM classifier based on the morphology. Our results show that the morphology of the IEGM may be used as an indicator of patient workload and body posture. The necessary information is found mainly in the ST segment. We conclude that neural networks seem to be useful in an active cardiac device.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0147-8389
pubmed:author
pubmed:issnType
Print
pubmed:volume
21
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2204-8
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
A software sensor using neural networks for detection of patient workload.
pubmed:affiliation
Lund University Hospital, Sweden. jonas.andersson@pacesetter.se
pubmed:publicationType
Journal Article