Linked Life Data

16235655

Source:http://linkedlifedata.com/resource/pubmed/id/16235655

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2005-10-20
pubmed:abstractText
The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and nonstationary time series. The central idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode functions (IMFs). An IMF is defined as any function having the number of extrema and the number of zero-crossings equal (or differing at most by one), and also having symmetric envelopes defined by the local minima, and maxima respectively. The decomposition procedure is adaptive, data-driven, therefore, highly efficient. In this contribution, we applied the idea of EMD to develop strategies to automatically identify the relevant IMFs that contribute to the slow-varying trend in the data, and presented its application on the analysis of esophageal manometric time series in gastroesophageal reflux disease. The results from both extensive simulations and real data show that the EMD may prove to be a vital technique for the analysis of esophageal manometric data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0018-9294
pubmed:author
pubmed:issnType
Print
pubmed:volume
52
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1692-701
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease.
pubmed:affiliation
School of Health Information Sciences, University of Texas at Houston, 7000 Fannin, Suite 600, Houston, TX 77030, USA. hualou.liang@uth.tmc.edu
pubmed:publicationType
Journal Article, Clinical Trial