Linked Life Data

9216153

Source:http://linkedlifedata.com/resource/pubmed/id/9216153

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
1997-8-19
pubmed:abstractText
Implantable antitachycardia devices rely upon schemes for detecting cardiac arrhythmias which utilize rate and its variations; yet rate parameters often identify nonpathologic tachycardias as potentially dangerous and deliver unwarranted therapy. I have developed a predictive filter based upon the time-sequenced adaptive algorithm to be used as a supplement to rate criteria for detecting and identifying serious arrhythmias. The method does not require a fixed template and is independent of a priori patient information. The algorithm also provides arrhythmia diagnosis immediately at the change in rhythm. Algorithmic parameters were determined based upon a training set of patient data, and performance of the technique was evaluated with a completely new test set of 20 arrhythmia passages. The new algorithm yielded a sensitivity and specificity for ventricular tachycardia of 91% and 82% and for ventricular fibrillation of 71% and 93%. Correlation waveform analysis was used to diagnose the same test set of arrhythmias. It yielded a sensitivity and specificity for ventricular tachycardia of 100% and 67% and for ventricular fibrillation of 50% and 100%.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0018-9294
pubmed:author
pubmed:issnType
Print
pubmed:volume
43
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
811-9
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
1996
pubmed:articleTitle
The time-sequenced adaptive filter for analysis of cardiac arrhythmias in intraventricular electrograms.
pubmed:affiliation
Department of Electrical and Computer Engineering, GMI Engineering and Management Institute, Flint, MI 48504-4898, USA. cfinelli@nova.gmi.edu
pubmed:publicationType
Journal Article