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rdf:type |
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lifeskim:mentions |
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pubmed:dateCreated |
2007-11-16
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pubmed:abstractText |
Specific features of hemodynamic signals are invaluable for elucidating ventricular and vascular function. A semi-automatic algorithm is presented that enables accurate detection of any feature in any hemodynamic signal, using feature extraction from local maxima and minima in signal curvature. A particular feature is selected manually in the first beat and then detected automatically in subsequent beats.
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:issn |
1557-170X
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pubmed:author |
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pubmed:issnType |
Print
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pubmed:volume |
2007
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
1691-4
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pubmed:meshHeading |
pubmed-meshheading:18002300-Algorithms,
pubmed-meshheading:18002300-Aorta,
pubmed-meshheading:18002300-Automatic Data Processing,
pubmed-meshheading:18002300-Automation,
pubmed-meshheading:18002300-Computers,
pubmed-meshheading:18002300-Equipment Design,
pubmed-meshheading:18002300-Hemodynamics,
pubmed-meshheading:18002300-Humans,
pubmed-meshheading:18002300-Image Processing, Computer-Assisted,
pubmed-meshheading:18002300-Models, Theoretical,
pubmed-meshheading:18002300-Pattern Recognition, Automated,
pubmed-meshheading:18002300-Pressure,
pubmed-meshheading:18002300-Signal Processing, Computer-Assisted,
pubmed-meshheading:18002300-Software,
pubmed-meshheading:18002300-Time Factors
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pubmed:year |
2007
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pubmed:articleTitle |
A semi-automatic feature detection algorithm for hemodynamic signals using curvature-based feature extraction.
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pubmed:affiliation |
Heart Research Group, Murdoch Children's Research Institute, Melbourne, Australia. jonathan.mynard@mcri.edu.au
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pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
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