Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-11-24
pubmed:abstractText
Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heartbeat. We propose an algorithm for quantifying instantaneous RSA as applied to heart beat interval and respiratory recordings under dynamic respiration conditions. The blood volume pressure derived heart beat series (pulse intervals, PI) are modeled as an inverse gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PI and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated by a frequency domain transfer function approach. The model is statistically validated using Kolmogorov-Smirnov (KS) goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. Experimental results confirm the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
2010
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1622-5
pubmed:dateRevised
2011-7-26
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Point process time-frequency analysis of respiratory sinus arrhythmia under altered respiration dynamics.
pubmed:affiliation
Applied Signal Processing Group, School of Engineering, The Australian National University, Canberra, Australia. sandun.kodituwakku@anu.edu.au
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural