Linked Life Data

10390140

Source:http://linkedlifedata.com/resource/pubmed/id/10390140

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
1999-9-8
pubmed:abstractText
This paper investigates the modeling and analysis of physiological data recorded from a 49-year-old male and are composed of three time series: blood oxygen saturation, heart rate and respiration. In particular, it is desired to verify if the models estimated from data can distinguish between the dynamics underlying two different breathing patterns (normal breathing and apnea). The estimated models are nonlinear autoregressive, moving average with exogenous inputs (NARMAX) and the regressors used to compose such models are carefully chosen, among hundreds of candidates, by an automatic procedure. The results discussed in this paper suggest that the dynamics underlying the data are nonlinear and basically deterministic. Using estimated models it seems to be possible to quantify the stability of the fixed point in phase space reconstructed using the blood oxygen time series. This, as discussed, could be the basis of an algorithmic monitoring system.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0010-4825
pubmed:author
pubmed:issnType
Print
pubmed:volume
29
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
207-28
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Nonlinear multivariable modeling and analysis of sleep apnea time series.
pubmed:affiliation
Departamento de Engenharia Eletrônica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. aguirre@cpdee.ufmg.br
pubmed:publicationType
Journal Article, Case Reports, Research Support, Non-U.S. Gov't