Show simple item record

dc.contributor.authorCastillo Escario, Yolanda
dc.contributor.authorFerrer Lluís, Ignasi
dc.contributor.authorJané Campos, Raimon
dc.contributor.authorMontserrat Canal, Josep Maria
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Biomèdica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2020-03-11T11:30:31Z
dc.date.available2020-03-11T11:30:31Z
dc.date.issued2019-01-01
dc.identifier.citationCastillo-Escario, Y. [et al.]. Entropy analysis of acoustic signals recorded with a smartphone for detecting apneas and hypopneas: A comparison with a commercial system for home sleep apnea diagnosis. "IEEE access", 1 Gener 2019, vol. 7, p. 128224-128241.
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/2117/179688
dc.description.abstractObstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Here we propose analyzing smartphone audio signals for screening OSA patients at home. Our objectives were to: (1) develop an algorithm for detecting silence events and classifying them into apneas or hypopneas; (2) evaluate the performance of this system; and (3) compare the information provided with a type 3 portable sleep monitor, based mainly on nasal airflow. Overnight signals were acquired simultaneously by both systems in 13 subjects (3 healthy subjects and 10 OSA patients). The sample entropy of audio signals was used to identify apnea/hypopnea events. The apnea-hypopnea indices predicted by the two systems presented a very high degree of concordance and the smartphone correctly detected and stratified all the OSA patients. An event-by-event comparison demonstrated good agreement between silence events and apnea/hypopnea events in the reference system (Sensitivity = 76%, Positive Predictive Value = 82%). Most apneas were detected (89%), but not so many hypopneas (61%). We observed that many hypopneas were accompanied by snoring, so there was no sound reduction. The apnea/hypopnea classification accuracy was 70%, but most discrepancies resulted from the inability of the nasal cannula of the reference device to record oral breathing. We provided a spectral characterization of oral and nasal breathing to correct this effect, and the classification accuracy increased to 82%. This novel knowledge from acoustic signals may be of great interest for clinical practice to develop new non-invasive techniques for screening and monitoring OSA patients at home
dc.format.extent18 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
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::Informàtica::Automàtica i control
dc.subject.lcshSleep apnea syndromes
dc.subject.lcshSignal processing
dc.subject.otherAcoustics
dc.subject.otherBiomedical signal processing
dc.subject.otherHealth
dc.subject.othermonitoring
dc.subject.otherSleep apnea
dc.subject.otherSmartphone
dc.titleEntropy analysis of acoustic signals recorded with a smartphone for detecting apneas and hypopneas: A comparison with a commercial system for home sleep apnea diagnosis
dc.typeArticle
dc.subject.lemacSíndromes d'apnea del son
dc.subject.lemacTelèfons intel·ligents
dc.subject.lemacTractament del senyal
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOSPIN - Biomedical Signal Processing and Interpretation
dc.identifier.doi10.1109/ACCESS.2019.2939749
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8825772
dc.rights.accessOpen Access
local.identifier.drac25892423
dc.description.versionPostprint (published version)
local.citation.authorCastillo-Escario, Y.; Ferrer, I.; Jane, R.; Montserrat, J.
local.citation.publicationNameIEEE access
local.citation.volume7
local.citation.startingPage128224
local.citation.endingPage128241


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain