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dc.contributor.authorBrieva, Jorge
dc.contributor.authorPonce, Hiram
dc.contributor.authorMoya-Albor, Ernesto
dc.contributor.otherCampus Ciudad de Méxicoes
dc.creatorERNESTO MOYA ALBOR;102463
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.
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.relation.ispartofREPOSITORIO SCRIPTAes
dc.rightsAcceso Restringidoes
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.subjectAverage errorsen
dc.subjectBland and altman analysisen
dc.subjectBreathing rateen
dc.subjectHealthy subjectsen
dc.subjectMotion videoen
dc.subjectRespiratory rateen
dc.subjectHydrocarbon refininen
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes
dc.titleNon-contact breathing rate monitoring system using a magnification technique and artificial hydrocarbon networksen
dc.typeContribución a congresoes
dcterms.bibliographicCitationAl-Naji, A., Gibson, K., Lee, S.-H., and Chahl, J., “Monitoring of cardiorespiratory signal: Principles of remote measurements and review of methods,” IEEE Access, 5 15776 –15790 (2017).
dcterms.bibliographicCitationLi, C., Chen, F., Jin, J., Lv, H., Li, S., Lu, G., and Wang, J., “A method for remotely sensing vital signs of human subjects outdoors,” Sensors (Switzerland), 15 (7), 14830 –14844 (2015).
dcterms.bibliographicCitationLee, Y., Pathirana, P., Evans, R., and Steinfort, C., “Noncontact detection and analysis of respiratory function using microwave doppler radar,” Journal of Sensors, 20 15 (2015).en
dcterms.bibliographicCitationDafna, E., Rosenwein, T., Tarasiuk, A., and Zigel, Y., “Breathing rate estimation during sleep using audio signal analysis,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2015-November, 5981 –5984 (2015).en
dcterms.bibliographicCitationWu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., and Freeman, W. T., “Eulerian video magnification for revealing subtle changes in the world,” in ACM Transactions on Graphics (Proc. SIGGRAPH 2012), (2012).en
dcterms.bibliographicCitationAl-Naji, A. and Chahl, J., “Remote respiratory monitoring system based on developing motion magnification technique,” Biomedical Signal Processing and Control, 29 1 –10 (2016).
dcterms.bibliographicCitationAl-Naji, A., Gibson, K., and Chahl, J., “Remote sensing of physiological signs using a machine vision system,” Journal of Medical Engineering and Technology, 41 (5), 396 –405 (2017).
dcterms.bibliographicCitationGanfure, G., “Using video stream for continuous monitoring of breathing rate for general setting,” Signal, Image and Video Processing, (2019).
dcterms.bibliographicCitationAlinovi, D., Ferrari, G., Pisani, F., and Raheli, R., “Respiratory rate monitoring by video processing using local motion magnification,” in European Signal Processing Conference 2018-September, 1780 –1784 (2018).en
dcterms.bibliographicCitationAlam, S., Singh, S., and Abeyratne, U., “Considerations of handheld respiratory rate estimation via a stabilized video magnification approach,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 4293 –4296 (2017).en
dcterms.bibliographicCitationChen, W. and McDuff, D., “Deepphys: Video-based physiological measurement using convolutional attention networks,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11206 LNCS, 356 –373 (2018).en
dcterms.bibliographicCitationBrieva, J., Ponce, H., and Moya-Albor, E., “A contactless respiratory rate estimation method using a hermite magnification technique and convolutional neural networks,” Applied Sciences (Switzerland), 10 (2), (2020).en
dcterms.bibliographicCitationPonce, H. and Ponce, P., “Artificial organic networks,” in Electronics, Robotics and Automotive Mechanics Conference (CERMA), 29 –34 (2011).en
dcterms.bibliographicCitationPonce, H., Ponce, P., and Molina, A., “Artificial Organic Networks: Artificial Intelligence Based on Carbon Networks,” of Studies in Computational Intelligence, Springer, 521 (2014).en
dcterms.bibliographicCitationPonce, H., de Campos Souza, P. V., Guimarães, A. J., and Gonzalez-Mora, G., “Stochastic parallel extreme artificial hydrocarbon networks: An implementation for fast and robust supervised machine learning in high-dimensional data,” Engineering Applications of Artificial Intelligence, 89 103427 (2020).
dcterms.bibliographicCitationMassaroni, C., Lo Presti, D., Formica, D., Silvestri, S., and Schena, E., “Non-contact monitoring of breathing pattern and respiratory rate via rgb signal measurement,” Sensors, 19 (12), (2019).
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