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dc.contributor.authorFerrer Lluís, Ignasi
dc.contributor.authorCastillo Escario, Yolanda
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.accessioned2020-10-20T09:57:25Z
dc.date.available2020-10-20T09:57:25Z
dc.date.issued2020-04-13
dc.identifier.citationFerrer, I. [et al.]. Analysis of Smartphone Triaxial Accelerometry for Monitoring Sleep-Disordered Breathing and Sleep Position at Home. "IEEE access", 13 Abril 2020, vol. 8, p. 71231-71244.
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/2117/330478
dc.description.abstractObstructive sleep apnea (OSA) is a sleep disorder in which repetitive upper airway obstructive events occur during sleep. These events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. OSA is also known to be position-dependent in some patients, which is referred to as positional OSA (pOSA). Screening for pOSA is necessary in order to design more personalized and effective treatment strategies. In this article, we propose analyzing accelerometry signals, recorded with a smartphone, to detect and monitor OSA at home. Our objectives were to: (1) develop an algorithm for detecting thoracic movement associated with disordered breathing events; (2) compare the performance of smartphones as OSA monitoring tools with a type 3 portable sleep monitor; and (3) explore the feasibility of using smartphone accelerometry to retrieve reliable patient sleep position data and assess pOSA. Accelerometry signals were collected through simultaneous overnight acquisition using both devices with 13 subjects. The smartphone tool showed a high degree of concordance compared to the portable device and succeeded in estimating the apnea-hypopnea index (AHI) and classifying the severity level in most subjects. To assess the agreement between the two systems, an event-by-event comparison was performed, which found a sensitivity of 90% and a positive predictive value of 80%. It was also possible to identify pOSA by determining the ratio of events occurring in a specific position versus the time spent in that position during the night. These novel results suggest that smartphones are promising mHealth tools for OSA and pOSA monitoring at home.
dc.format.extent14 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAttribution-NonCommercial-NoDerivates 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.lcshSleep apnea syndromes
dc.subject.lcshSignal processing
dc.subject.lcshBiomedical engineering
dc.subject.otherAccelerometry
dc.subject.otherBiomedical signal processing
dc.subject.otherMHealth
dc.subject.othermonitoring
dc.subject.otherSleep apnea
dc.subject.otherSleep position
dc.subject.otherSmartphone
dc.titleAnalysis of Smartphone Triaxial Accelerometry for Monitoring Sleep-Disordered Breathing and Sleep Position at Home
dc.typeArticle
dc.subject.lemacSíndromes d'apnea del son
dc.subject.lemacTractament del senyal
dc.subject.lemacEnginyeria biomèdica
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOSPIN - Biomedical Signal Processing and Interpretation
dc.identifier.doi10.1109/ACCESS.2020.2987488
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9064544
dc.rights.accessOpen Access
local.identifier.drac28875888
dc.description.versionPostprint (published version)
local.citation.authorFerrer, I.; Castillo-Escario, Y.; Jane, R.; Montserrat, J.
local.citation.publicationNameIEEE access
local.citation.volume8
local.citation.startingPage71231
local.citation.endingPage71244


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