Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/5119
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | - |
dc.creator | Small, M | - |
dc.date.accessioned | 2014-12-11T08:24:22Z | - |
dc.date.available | 2014-12-11T08:24:22Z | - |
dc.identifier.issn | 1753-4631 | - |
dc.identifier.uri | http://hdl.handle.net/10397/5119 | - |
dc.language.iso | en | en_US |
dc.publisher | BioMed Central Ltd. | en_US |
dc.rights | © 2007 Small; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
dc.subject | Blood | en_US |
dc.subject | Entropy | en_US |
dc.subject | Plethysmography | en_US |
dc.subject | Time series | en_US |
dc.subject | Volume measurement | en_US |
dc.title | Estimating the distribution of dynamic invariants : illustrated with an application to human photo-plethysmographic time series | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 11 | - |
dc.identifier.volume | 1 | - |
dc.identifier.issue | 1 | - |
dc.identifier.doi | 10.1186/1753-4631-1-8 | - |
dcterms.abstract | Dynamic invariants are often estimated from experimental time series with the aim of differentiating between different physical states in the underlying system. The most popular schemes for estimating dynamic invariants are capable of estimating confidence intervals, however, such confidence intervals do not reflect variability in the underlying dynamics. We propose a surrogate based method to estimate the expected distribution of values under the null hypothesis that the underlying deterministic dynamics are stationary. We demonstrate the application of this method by considering four recordings of human pulse waveforms in differing physiological states and show that correlation dimension and entropy are insufficient to differentiate between these states. In contrast, algorithmic complexity can clearly differentiate between all four rhythms. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Nonlinear biomedical physics, 23 July 2007, v. 1, 8, p. 1-11 | - |
dcterms.isPartOf | Nonlinear biomedical physics | - |
dcterms.issued | 2007-07-23 | - |
dc.identifier.scopus | 2-s2.0-78649973588 | - |
dc.identifier.pmid | 17908340 | - |
dc.identifier.rosgroupid | r38443 | - |
dc.description.ros | 2007-2008 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Small_Estimating_distribution _dynamic.pdf | 1.23 MB | Adobe PDF | View/Open |
Page views
100
Last Week
1
1
Last month
Citations as of Apr 21, 2024
Downloads
85
Citations as of Apr 21, 2024
SCOPUSTM
Citations
2
Last Week
0
0
Last month
0
0
Citations as of Apr 5, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.