| pubmed-article:18002300 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:18002300 | lifeskim:mentions | umls-concept:C0019010 | lld:lifeskim |
| pubmed-article:18002300 | lifeskim:mentions | umls-concept:C0185115 | lld:lifeskim |
| pubmed-article:18002300 | lifeskim:mentions | umls-concept:C1511790 | lld:lifeskim |
| pubmed-article:18002300 | lifeskim:mentions | umls-concept:C1710082 | lld:lifeskim |
| pubmed-article:18002300 | lifeskim:mentions | umls-concept:C0002045 | lld:lifeskim |
| pubmed-article:18002300 | lifeskim:mentions | umls-concept:C2348519 | lld:lifeskim |
| pubmed-article:18002300 | pubmed:dateCreated | 2007-11-16 | lld:pubmed |
| pubmed-article:18002300 | 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. | lld:pubmed |
| pubmed-article:18002300 | pubmed:language | eng | lld:pubmed |
| pubmed-article:18002300 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18002300 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:18002300 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:18002300 | pubmed:issn | 1557-170X | lld:pubmed |
| pubmed-article:18002300 | pubmed:author | pubmed-author:SmolichJoseph... | lld:pubmed |
| pubmed-article:18002300 | pubmed:author | pubmed-author:PennyDaniel... | lld:pubmed |
| pubmed-article:18002300 | pubmed:author | pubmed-author:MynardJonatha... | lld:pubmed |
| pubmed-article:18002300 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:18002300 | pubmed:volume | 2007 | lld:pubmed |
| pubmed-article:18002300 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:18002300 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:18002300 | pubmed:pagination | 1691-4 | lld:pubmed |
| pubmed-article:18002300 | pubmed:meshHeading | pubmed-meshheading:18002300... | lld:pubmed |
| pubmed-article:18002300 | pubmed:meshHeading | pubmed-meshheading:18002300... | lld:pubmed |
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| pubmed-article:18002300 | pubmed:meshHeading | pubmed-meshheading:18002300... | lld:pubmed |
| pubmed-article:18002300 | pubmed:meshHeading | pubmed-meshheading:18002300... | lld:pubmed |
| pubmed-article:18002300 | pubmed:meshHeading | pubmed-meshheading:18002300... | lld:pubmed |
| pubmed-article:18002300 | pubmed:meshHeading | pubmed-meshheading:18002300... | lld:pubmed |
| pubmed-article:18002300 | pubmed:meshHeading | pubmed-meshheading:18002300... | lld:pubmed |
| pubmed-article:18002300 | pubmed:meshHeading | pubmed-meshheading:18002300... | lld:pubmed |
| pubmed-article:18002300 | pubmed:year | 2007 | lld:pubmed |
| pubmed-article:18002300 | pubmed:articleTitle | A semi-automatic feature detection algorithm for hemodynamic signals using curvature-based feature extraction. | lld:pubmed |
| pubmed-article:18002300 | pubmed:affiliation | Heart Research Group, Murdoch Children's Research Institute, Melbourne, Australia. jonathan.mynard@mcri.edu.au | lld:pubmed |
| pubmed-article:18002300 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:18002300 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
