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dc.contributor.authorLozano, Manuel
dc.contributor.authorFiz Fernández, José Antonio
dc.contributor.authorJané Campos, Raimon
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2017-03-09T11:12:01Z
dc.date.available2018-04-03T00:30:33Z
dc.date.issued2016-03
dc.identifier.citationLozano, M., Fiz, J., Jane, R. Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization. "Signal processing", Març 2016, vol. 120, p. 99-116.
dc.identifier.issn0165-1684
dc.identifier.urihttp://hdl.handle.net/2117/102186
dc.description© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.abstractThe use of the Hilbert–Huang transform in the analysis of biomedical signals has increased during the past few years, but its use for respiratory sound (RS) analysis is still limited. The technique includes two steps: empirical mode decomposition (EMD) and instantaneous frequency (IF) estimation. Although the mode mixing (MM) problem of EMD has been widely discussed, this technique continues to be used in many RS analysis algorithms. In this study, we analyzed the MM effect in RS signals recorded from 30 asthmatic patients, and studied the performance of ensemble EMD (EEMD) and noise-assisted multivariate EMD (NA-MEMD) as means for preventing this effect. We propose quantitative parameters for measuring the size, reduction of MM, and residual noise level of each method. These parameters showed that EEMD is a good solution for MM, thus outperforming NA-MEMD. After testing different IF estimators, we propose Kay¿s method to calculate an EEMD-Kay-based Hilbert spectrum that offers high energy concentrations and high time and high frequency resolutions. We also propose an algorithm for the automatic characterization of continuous adventitious sounds (CAS). The tests performed showed that the proposed EEMD-Kay-based Hilbert spectrum makes it possible to determine CAS more precisely than other conventional time-frequency techniques.
dc.format.extent18 p.
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Sèries temporals
dc.subject.lcshHilbert-Huang transform
dc.subject.otherHilbert–Huang transform
dc.subject.otherEnsemble empirical mode decomposition
dc.subject.otherInstantaneous frequency
dc.subject.otherRespiratory sounds
dc.subject.otherContinuous adventitious sounds
dc.titlePerformance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization
dc.typeArticle
dc.subject.lemacHilbert-Huang, Transformació de
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOSPIN - Biomedical Signal Processing and Interpretation
dc.identifier.doi10.1016/j.sigpro.2015.09.005
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0165168415002996
dc.rights.accessOpen Access
local.identifier.drac19770449
dc.description.versionPostprint (author's final draft)
local.citation.authorLozano, M.; Fiz, J.; Jane, R.
local.citation.publicationNameSignal processing
local.citation.volume120
local.citation.startingPage99
local.citation.endingPage116


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