Linked Life Data

19163941

Source:http://linkedlifedata.com/resource/pubmed/id/19163941

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2009-2-16
pubmed:abstractText
The management of many neurological disorders such as traumatic brain injuries relies on the continuous measurement of intracranial pressure (ICP). Following recent studies, the automatic analysis of ICP pulse seems to be a promising tool for forecasting intracranial and cerebrovascular pathophysiological changes. MOCAIP algorithm has recently been developed to automatically extract ICP morphological features in real time. The algorithm is capable of enhancing ICP signal quality, recognizing legitimate ICP pulses, and designating the three peaks in an ICP pulse. This paper extends MOCAIP by using a regression model instead of Gaussian priors during the peak designation to improve the accuracy of the process. The experimental evaluations of the proposed algorithm are performed on a ICP signal database built from 700 hours of recordings from 66 neurosurgical patients. They indicate that the use of a regression model significantly increases the peak designation accuracy.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
2008
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
5411-4
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Nonlinear regression for sub-peak detection of intracranial pressure signals.
pubmed:affiliation
Division of Neurosurgery, Geffen School of Medicine, University of California, Los Angeles, CA, USA. fscalzo@mednet.ucla.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural