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dc.contributor.authorTellez Gabriel, Juan Pablo
dc.contributor.authorHerrera, Sergio
dc.contributor.authorBenito Vales, Salvador
dc.contributor.authorGiraldo Giraldo, Beatriz
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.otherInstitut de Bioenginyeria de Catalunya
dc.date.accessioned2015-04-30T08:31:03Z
dc.date.created2014
dc.date.issued2014
dc.identifier.citationTellez , J. [et al.]. Analysis of the breathing pattern in elderly patients using the Hurst exponent applied to the respiratory flow signal. A: IEEE Engineering in Medicine and Biology Society. "36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC)". Chicago: 2014, p. 3422-3425.
dc.identifier.isbn978-1-4244-7929-0
dc.identifier.urihttp://hdl.handle.net/2117/27681
dc.description.abstractDue to the increasing elderly population and the extensive number of comorbidities that affect them, studies are required to determine future increments in admission to emergency departments. Some of these studies could focus on the relation between chronic diseases and breathing pattern in elderly patients. Variations in the fractal properties of respiratory signals can be associated with several diseases. To determine the relationship between these variations and breathing patterns, and to quantify the fractal properties of respiratory flow signals, we estimated the Hurst exponent (H). Detrended fluctuation analysis (DFA) and discrete wavelet transform-based estimation (DWTE) methods were applied. The estimation methods were analyzed using simulated data series generated by fractional Gaussian noise. 43 elderly patients (19 patients with a non-periodic breathing pattern - nPB, and 24 patients with a periodic breathing pattern - PB) were studied. The results were evaluated according to the length of data and the number of averaged data series used to obtain a good estimation. The DWTE method estimated the respiratory flow signals better than the DFA method, and obtained Hurst values clustered by group. We found significant differences in the H exponent (p = 0.002) between PB and nPB patients, which showed different behavior in the fractal properties.
dc.format.extent4 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
dc.subject.lcshMedical electronics
dc.titleAnalysis of the breathing pattern in elderly patients using the Hurst exponent applied to the respiratory flow signal
dc.typeConference report
dc.subject.lemacRespiració
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOSPIN - Biomedical Signal Processing and Interpretation
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
drac.iddocument15536932
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorTellez , J.; Herrera, S.; Benito, S.; Giraldo, B.
upcommons.citation.contributorIEEE Engineering in Medicine and Biology Society
upcommons.citation.pubplaceChicago
upcommons.citation.publishedtrue
upcommons.citation.publicationName36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC)
upcommons.citation.startingPage3422
upcommons.citation.endingPage3425


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