Linked Life Data

12745912

Source:http://linkedlifedata.com/resource/pubmed/id/12745912

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2003-5-14
pubmed:abstractText
The aim of this study was to design a diagnostic model to identify patients with Cheyne-Stokes respiration (CSR-CSA) based on indices of oximetric spectral analysis. A retrospective analysis of oximetric recordings of 213 sleep studies conducted over a one-year period at a Veterans Affairs medical facility was performed. A probabilistic neural network (PNN) was developed from salient features of the oximetric spectral analysis, desaturation events and the delta index. A fivefold cross-validation was used to assess the accuracy of the neural network in identifying CSR-CSA. When compared to overnight polysomnography, the PNN achieved a sensitivity of 100% (95% confidence interval [CI] 85%-100%) and a specificity of 99% (95% 97%-100%) with a corresponding area under the curve of 99% (95% CI 99%-100%). When combined with overnight pulse oximetry, PNN offers an accurate and easily applicable tool to detect CSR-CSA.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0309-1902
pubmed:author
pubmed:issnType
Print
pubmed:volume
27
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
54-8
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:articleTitle
The utility of neural network in the diagnosis of Cheyne-Stokes respiration.
pubmed:affiliation
Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Veteran Affairs Medical Center and Erie County Medical Center, Buffalo, NY, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't