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dc.contributor.authorGarde Martínez, Ainara
dc.contributor.authorSörnmo, Leif
dc.contributor.authorJané Campos, Raimon
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.accessioned2010-10-19T11:44:58Z
dc.date.available2010-10-19T11:44:58Z
dc.date.created2009
dc.date.issued2009
dc.identifier.citationGarde, A. [et al.]. Correntropy-based Analysis of Respiratory Patterns with Chronic Heart Failure. A: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. "31th Annual International Conference of the IEEE Engineering in Medicine and Biology Society". Minneapolis, Minnesota: 2009, p. 4687-4690.
dc.identifier.isbn978-1-4244-3296-7
dc.identifier.urihttp://hdl.handle.net/2117/9809
dc.description.abstractA correntropy-based technique is proposed for the analysis and characterization of respiratory flow signals in chronic heart failure (CHF) patients with both periodic and nonperiodic breathing (PB and nPB), and healthy subjects. Correntropy is a novel similarity measure which provides information on temporal structure and statistical distribution simultaneously. Its properties lend itself to the definition of the correntropy spectral density (CSD). An interesting result from CSD-based spectral analysis is that both the respiratory frequency and modulation frequency can be detected at their original positions in the spectrum without prior demodulation of the flow signal. The respiratory pattern is characterized by a number of spectral parameters extracted from the respiratory and modulation frequency bands. The results show that the power of the modulation frequency band offers excellent performance when classifying CHF patients versus healthy subjects, with an accuracy of 95.3%, and nPB patients versus healthy subjects with 90.7%. The ratio between the power in the modulation and respiration frequency bands provides the best results classifying CHF patients into PB and nPB, with an accuracy of 88.9%.
dc.format.extent4 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject.lcshMedical signal processing
dc.subject.lcshChronic heart failure
dc.titleCorrentropy-based Analysis of Respiratory Patterns with Chronic Heart Failure
dc.typeConference report
dc.subject.lemacProcessament digital -- Biomedicina
dc.subject.lemacCardiologia -- Informàtica
dc.contributor.groupUniversitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics
dc.identifier.doi10.1109/IEMBS.2009.5334219
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
local.identifier.drac3095590
dc.description.versionPostprint (published version)
local.citation.authorGarde, A.; Sörnmo, L.; Jané, R.; Giraldo, B. F.
local.citation.contributorAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
local.citation.pubplaceMinneapolis, Minnesota
local.citation.publicationName31th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
local.citation.startingPage4687
local.citation.endingPage4690


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