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dc.contributor.authorBrieva, Jorge
dc.contributor.authorPonce, Hiram
dc.contributor.authorMoya-Albor, Ernesto
dc.contributor.otherCampus Ciudad de Méxicoes
dc.creatorJORGE EDUARDO BRIEVA RICO;121435
dc.creatorHIRAM EREDIN PONCE ESPINOSA;376768
dc.creatorERNESTO MOYA ALBOR;102463
dc.date.accessioned2022-02-01T18:23:18Z
dc.date.available2022-02-01T18:23:18Z
dc.date.issued2020
dc.identifier.citationBrieva, J., Ponce, H., & Moya-Albor, E. (2020). Non-contact breathing rate monitoring system using a magnification technique and artificial hydrocarbon networks. 16th International Symposium on Medical Information Processing and Analysis, 1-2. https://doi.org/10.1117/12.2580077en
dc.identifier.isbn9781510639911
dc.identifier.issn0277-786X
dc.identifier.issn1996-756X
dc.identifier.urihttps://hdl.handle.net/20.500.12552/5889
dc.identifier.urihttps://doi.org/10.1117/12.2580077
dc.description.abstractIn this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and an Artificial Hydrocarbon Networks (AHN) as classifier. After the magnification procedure, a AHN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. The respiratory rate (RR) is estimated from the classified frames. We have tested the method on 10 healthy subjects in different positions. To compare performance of methods to respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for our strategy is 4.46 ± 3.68% with and agreement with respect of the reference of ˜ 98 %. © 2020 SPIEen
dc.description.tableofcontents1. Introduction -- 2. Video processing and detection -- 2.1 Video Magnification -- 2.2 Artificial Hydrocarbon Networks -- 2.3 Estimation of the Breathing Rate -- 2.4 Experiments -- 3. Results -- 4. Discussion and conclusionen
dc.language.isoengen
dc.publisherSPIEen
dc.relation.ispartofREPOSITORIO SCRIPTAes
dc.relation.ispartofOPENAIREes
dc.relation.ispartofseries115830R
dc.rightsAcceso Restringidoes
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.urihttps://v2.sherpa.ac.uk/id/publication/27454
dc.source16th International Symposium on Medical Information Processing and Analysis : The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peruen
dc.subjectArtificial Hydrocarbon networksen
dc.subjectBreathing rate estimationen
dc.subjectHermite transformen
dc.subjectMotion video magnificationen
dc.subjectNon-contact monitoringen
dc.subjectBioinformaticsen
dc.subjectHydrocarbonsen
dc.subjectAverage errorsen
dc.subjectBland and altman analysisen
dc.subjectBreathing rateen
dc.subjectEulerianen
dc.subjectHealthy subjectsen
dc.subjectMotion videoen
dc.subjectNon-contacten
dc.subjectRespiratory rateen
dc.subjectHydrocarbon refininen
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes
dc.subject.classificationIngenieríaes
dc.titleNon-contact breathing rate monitoring system using a magnification technique and artificial hydrocarbon networksen
dc.typeContribución a congresoes
dcterms.audienceInvestigadoreses
dcterms.audienceMaestroses
dcterms.audienceEstudianteses
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dc.description.versionVersión del editores
dc.identifier.doihttps://doi.org/10.1117/12.2580077


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