Comparison of video-based methods for respiration rhythm measurement

•Assessment of respiratory rhythm obtained through video-based methods.•Three video-based methods were evaluated versus a gold standard: an RGB camera, a depth camera and a thermal camera.•The depth and RGB camera present high agreement with no statistical differences between them.•The depth and RGB...

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Published inBiomedical signal processing and control Vol. 51; pp. 138 - 147
Main Authors Mateu-Mateus, M., Guede-Fernández, F., Ferrer-Mileo, V., García-González, M.A., Ramos-Castro, J., Fernández-Chimeno, M.
Format Journal Article Publication
LanguageEnglish
Published Elsevier Ltd 01.05.2019
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Online AccessGet full text
ISSN1746-8094
1746-8108
DOI10.1016/j.bspc.2019.02.004

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Abstract •Assessment of respiratory rhythm obtained through video-based methods.•Three video-based methods were evaluated versus a gold standard: an RGB camera, a depth camera and a thermal camera.•The depth and RGB camera present high agreement with no statistical differences between them.•The depth and RGB camera, and their respective acquisition algorithms, can be used in controlled conditions to measure respiration rhythm and its variability. The aim of this work is to characterize the differences in the respiratory rhythm obtained through three video based methods by comparing the obtained respiratory signals with the one obtained with the gold standard method in adult population. The analysed methods are an RGB camera, a depth camera and a thermal camera while the gold standard is an inductive thorax plethysmography system (Respiband system from BioSignals Plux). 21 healthy subjects where measured, performing 4 tests for each subject. The respiratory rhythm and its variability was obtained from the four respiratory signals (3 video methods and gold standard). The signal acquisition was performed with custom and proprietary algorithms. To characterize the respiratory rhythm and its variability obtained with the different video sources and gold standard, the instantaneous frequency, Bland-Altman plots and standard deviation of the error between video methods and the gold standard have been computed. The depth and RGB camera present high agreement with no statistical differences between them, with errors when comparing with the gold standard in the range of mHz. The thermal camera performs poorly if compared with the two other methods, nevertheless it cannot be discarded directly because some errors produced by the subjects head movement could not be corrected. From these results we conclude that the depth and RGB camera, and their respective acquisition algorithms, can be used in controlled conditions to measure respiration rhythm and its variability. The thermal camera on the other hand, although it cannot be discarded directly, performed poorly if compared with the other two methods. Further studies are needed to confirm that these methods can be used in real life conditions.
AbstractList •Assessment of respiratory rhythm obtained through video-based methods.•Three video-based methods were evaluated versus a gold standard: an RGB camera, a depth camera and a thermal camera.•The depth and RGB camera present high agreement with no statistical differences between them.•The depth and RGB camera, and their respective acquisition algorithms, can be used in controlled conditions to measure respiration rhythm and its variability. The aim of this work is to characterize the differences in the respiratory rhythm obtained through three video based methods by comparing the obtained respiratory signals with the one obtained with the gold standard method in adult population. The analysed methods are an RGB camera, a depth camera and a thermal camera while the gold standard is an inductive thorax plethysmography system (Respiband system from BioSignals Plux). 21 healthy subjects where measured, performing 4 tests for each subject. The respiratory rhythm and its variability was obtained from the four respiratory signals (3 video methods and gold standard). The signal acquisition was performed with custom and proprietary algorithms. To characterize the respiratory rhythm and its variability obtained with the different video sources and gold standard, the instantaneous frequency, Bland-Altman plots and standard deviation of the error between video methods and the gold standard have been computed. The depth and RGB camera present high agreement with no statistical differences between them, with errors when comparing with the gold standard in the range of mHz. The thermal camera performs poorly if compared with the two other methods, nevertheless it cannot be discarded directly because some errors produced by the subjects head movement could not be corrected. From these results we conclude that the depth and RGB camera, and their respective acquisition algorithms, can be used in controlled conditions to measure respiration rhythm and its variability. The thermal camera on the other hand, although it cannot be discarded directly, performed poorly if compared with the other two methods. Further studies are needed to confirm that these methods can be used in real life conditions.
The aim of this work is to characterize the di erences in the respiratory rhythm obtained through three video based methods by comparing the obtained respiratory signals with the one obtained with the gold standard method in adult population. The analysed methods are an RGB camera, a depth camera and a thermal camera while the gold standard is an inductive thorax plethysmography system (Respiband system from BioSignals Plux). 21 healthy subjects where measured, performing 4 tests for each subject. The respiratory rhythm and its variability was obtained from the four respiratory signals (3 video methods and gold standard). The signal acquisition was performed with custom and proprietary algorithms. To characterize the respiratory rhythm and its variability obtained with the di erent video sources and gold standard, the instantaneous frequency, Bland-Altman plots and standard deviation of the error between video methods and the gold standard have been computed. The depth and RGB camera present high agreement with no statistical di erences between them, with errors when comparing with the gold standard in the range of mHz. The thermal camera performs poorly if compared with the two other methods, nevertheless it cannot be discarded directly because some errors produced by the subjects head movement could not be corrected. From these results we conclude that the depth and RGB camera, and their respective acquisition algorithms) can be used in controlled conditions to measure respiration rhythm and its variability. The thermal camera on the other hand, although it can not be discarded directly, performed poorly if compared with the other two methods. Further studies are needed to con rm that these methods can be used in real life conditions.
Author García-González, M.A.
Ramos-Castro, J.
Fernández-Chimeno, M.
Guede-Fernández, F.
Ferrer-Mileo, V.
Mateu-Mateus, M.
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Cites_doi 10.3390/s121013167
10.1109/RBME.2015.2414661
10.1002/ppul.21416
10.1016/j.protcy.2016.08.082
10.1007/978-3-540-45243-0_39
10.1046/j.1365-2273.2002.00544.x
10.2307/2347973
10.1364/BOE.6.004378
10.1016/j.compbiomed.2016.12.005
10.1007/s11517-007-0302-y
10.1023/B:VISI.0000013087.49260.fb
10.1049/htl.2014.0063
10.1016/S0960-0779(03)00441-7
10.2307/2953682
10.1088/0967-3334/35/5/807
10.4236/ojmi.2016.61003
10.3390/s16070996
10.1016/S0034-5687(00)00154-7
10.1109/JSEN.2010.2044239
10.1515/cdbme-2015-0048
10.1109/TBME.2009.2032415
10.11613/BM.2015.015
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Keywords Respiration variability
Camera
Respiration rhythm
Depth
Video-based
Thermal
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References Bruser, Antink, Wartzek, Walter, Leonhardt (bib0015) 2015; 8
Chatterjee, Prathosh, Praveena (bib0110) 2016
Lee, Pathirana, Evans, Steinfort (bib0035) 2015; 2015
Tarassenko, Villarroel, Guazzi, Jorge, Clifton, Pugh (bib0050) 2014; 35
Microsoft Research (bib0090) 2011
Hollander, Wolfe, Chicken (bib0175) 2015
Viola, Jones (bib0120) 2004; 57
Mateu-Mateus, Guede-Fernández, García-González (bib0150) 2015
AL-Khalidi, Saatchi, Burke, Elphick, Tan (bib0020) 2011; 46
Procházka, Schätz, Vyšata, Vališ (bib0060) 2016; 16
Bernal, Mestha, Shilla (bib0105) 2014
Bartula, Tigges, Muehlsteff (bib0055) 2013
Benchetrit (bib0010) 2000; 122
Lindemann, Leiacker, Rettinger, Keck (bib0045) 2002; 27
Suhr (bib0100) 2009
Giavarina (bib0180) 2015; 25
Fei, Pavlidis (bib0080) 2010; 57
Orphanidou (bib0135) 2017; 81
Longhi, Monteriù, Freddi, Benetazzo (bib0070) 2014; 1
Tomasi (bib0130) 1994
Royston (bib0170) 1982; 31
World Medical Association (bib0095) 2001; 79
.
N. Rodriguez Ibanez, M. Fernandez Chimeno, J.J. Ramos Castro, M.A. Garcia Gonzalez, E. Montseny Masip, D. Bande Matinez, Method and System for Determining an Individual's State of Attention, US2011028857 (A1) (2014).
Kumagai, Uemura, Ishibashi, Nakabayashi, Arai, Kobayashi, Kotoku (bib0115) 2016; 06
Lienhart, Kuranov, Pisarevsky (bib0125) 2003
Hodrick, Prescott (bib0165) 1997; 29
Ernst, Saß (bib0065) 2015; 1
Zhang, Zheng, Wu, Wang, Wang, Liu (bib0030) 2012; 12
Park, Noh, Park, Yoon (bib0145) 2008; 46
Sahoo, Biswal, Das, Sabut (bib0160) 2016; 25
Benitez, Gaydecki, Zaidi, Fitzpatrick (bib0155) 2000; 27
M. Fernandez Chimeno, J. Ramos Castro, M.A. García Gonzalez, F. Guede Fernandez, M. Mateu Mateus, N. Rodriguez Ibañez, B. Bas Pujols, J.M. Alvarez Gomez, Respiratory Signal Extraction, WO/2018/121861 (2018), URL
Rodriguez-Ibanez, Garcia-Gonzalez, Fernandez-Chimeno, Ramos-Castro (bib0005) 2011
Se Dong, Jin Kwon, Hang Sik, Yong Hyeon, Chung Keun, Myoungho (bib0040) 2010; 10
Pereira, Yu, Czaplik, Rossaint, Blazek, Leonhardt (bib0075) 2015; 6
Balocchi, Menicucci, Santarcangelo, Sebastiani, Gemignani, Ghelarducci, Varanini (bib0140) 2004; 20
Hollander (10.1016/j.bspc.2019.02.004_bib0175) 2015
Benchetrit (10.1016/j.bspc.2019.02.004_bib0010) 2000; 122
10.1016/j.bspc.2019.02.004_bib0085
World Medical Association (10.1016/j.bspc.2019.02.004_bib0095) 2001; 79
Mateu-Mateus (10.1016/j.bspc.2019.02.004_bib0150) 2015
Orphanidou (10.1016/j.bspc.2019.02.004_bib0135) 2017; 81
Procházka (10.1016/j.bspc.2019.02.004_bib0060) 2016; 16
Zhang (10.1016/j.bspc.2019.02.004_bib0030) 2012; 12
Viola (10.1016/j.bspc.2019.02.004_bib0120) 2004; 57
Bruser (10.1016/j.bspc.2019.02.004_bib0015) 2015; 8
Kumagai (10.1016/j.bspc.2019.02.004_bib0115) 2016; 06
Tomasi (10.1016/j.bspc.2019.02.004_bib0130) 1994
Park (10.1016/j.bspc.2019.02.004_bib0145) 2008; 46
Tarassenko (10.1016/j.bspc.2019.02.004_bib0050) 2014; 35
Giavarina (10.1016/j.bspc.2019.02.004_bib0180) 2015; 25
Rodriguez-Ibanez (10.1016/j.bspc.2019.02.004_bib0005) 2011
Chatterjee (10.1016/j.bspc.2019.02.004_bib0110) 2016
Ernst (10.1016/j.bspc.2019.02.004_bib0065) 2015; 1
Bernal (10.1016/j.bspc.2019.02.004_bib0105) 2014
Bartula (10.1016/j.bspc.2019.02.004_bib0055) 2013
Lienhart (10.1016/j.bspc.2019.02.004_bib0125) 2003
Hodrick (10.1016/j.bspc.2019.02.004_bib0165) 1997; 29
Benitez (10.1016/j.bspc.2019.02.004_bib0155) 2000; 27
Se Dong (10.1016/j.bspc.2019.02.004_bib0040) 2010; 10
Lee (10.1016/j.bspc.2019.02.004_bib0035) 2015; 2015
Lindemann (10.1016/j.bspc.2019.02.004_bib0045) 2002; 27
AL-Khalidi (10.1016/j.bspc.2019.02.004_bib0020) 2011; 46
Longhi (10.1016/j.bspc.2019.02.004_bib0070) 2014; 1
Fei (10.1016/j.bspc.2019.02.004_bib0080) 2010; 57
Pereira (10.1016/j.bspc.2019.02.004_bib0075) 2015; 6
Balocchi (10.1016/j.bspc.2019.02.004_bib0140) 2004; 20
Suhr (10.1016/j.bspc.2019.02.004_bib0100) 2009
Microsoft Research (10.1016/j.bspc.2019.02.004_bib0090) 2011
Sahoo (10.1016/j.bspc.2019.02.004_bib0160) 2016; 25
10.1016/j.bspc.2019.02.004_bib0025
Royston (10.1016/j.bspc.2019.02.004_bib0170) 1982; 31
References_xml – volume: 8
  start-page: 30
  year: 2015
  end-page: 43
  ident: bib0015
  article-title: Ambient and unobtrusive cardiorespiratory monitoring techniques
  publication-title: IEEE Rev. Biomed. Eng.
– start-page: 593
  year: 1994
  end-page: 600
  ident: bib0130
  article-title: Good features to track
  publication-title: in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94
– volume: 81
  start-page: 45
  year: 2017
  end-page: 54
  ident: bib0135
  article-title: Derivation of respiration rate from ambulatory ECG and PPG using ensemble empirical mode decomposition: comparison and fusion
  publication-title: Comput. Biol. Med.
– volume: 06
  start-page: 20
  year: 2016
  end-page: 31
  ident: bib0115
  article-title: Markerless respiratory motion tracking using single depth camera
  publication-title: Open J. Med. Imag.
– start-page: 6055
  year: 2011
  end-page: 6058
  ident: bib0005
  article-title: Drowsiness detection by thoracic effort signal analysis in real driving environments
  publication-title: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
– volume: 46
  start-page: 147
  year: 2008
  end-page: 158
  ident: bib0145
  article-title: An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation
  publication-title: Med. Biol. Eng. Comput.
– volume: 1
  start-page: 81
  year: 2014
  end-page: 86
  ident: bib0070
  article-title: Respiratory rate detection algorithm based on RGB-D camera: theoretical background and experimental results
  publication-title: Healthc. Technol. Lett.
– start-page: 289
  year: 2015
  end-page: 392
  ident: bib0175
  article-title: The Two-Way Layout
– reference: M. Fernandez Chimeno, J. Ramos Castro, M.A. García Gonzalez, F. Guede Fernandez, M. Mateu Mateus, N. Rodriguez Ibañez, B. Bas Pujols, J.M. Alvarez Gomez, Respiratory Signal Extraction, WO/2018/121861 (2018), URL
– volume: 35
  start-page: 807
  year: 2014
  end-page: 831
  ident: bib0050
  article-title: Non-contact video-based vital sign monitoring using ambient light and auto-regressive models
  publication-title: Physiol. Meas.
– year: 2016
  ident: bib0110
  article-title: Real-time respiration rate measurement from thoracoabdominal movement with a consumer grade camera
  publication-title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society
– volume: 31
  start-page: 115
  year: 1982
  ident: bib0170
  article-title: An extension of Shapiro and Wilk's W test for normality to large samples
  publication-title: Appl. Stat.
– volume: 25
  start-page: 68
  year: 2016
  end-page: 75
  ident: bib0160
  article-title: De-noising of ECG signal and QRS detection using hilbert transform and adaptive thresholding
  publication-title: Procedia Technol.
– year: 2011
  ident: bib0090
  article-title: Kinect for Windows SDK beta
– volume: 12
  start-page: 13167
  year: 2012
  end-page: 13184
  ident: bib0030
  article-title: Development of a respiratory inductive plethysmography module supporting multiple sensors for wearable systems
  publication-title: Sensors
– volume: 27
  start-page: 135
  year: 2002
  end-page: 139
  ident: bib0045
  article-title: Nasal mucosal temperature during respiration
  publication-title: Clin. Otolaryngol. Allied Sci.
– volume: 20
  start-page: 171
  year: 2004
  end-page: 177
  ident: bib0140
  article-title: Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition
  publication-title: Chos Solitons Fractals
– volume: 79
  start-page: 373
  year: 2001
  end-page: 374
  ident: bib0095
  article-title: World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects
  publication-title: Bull. World Health Organ.
– volume: 6
  start-page: 4378
  year: 2015
  ident: bib0075
  article-title: Remote monitoring of breathing dynamics using infrared thermography
  publication-title: Biomed. Opt. Express
– start-page: 9
  year: 2009
  end-page: 18
  ident: bib0100
  article-title: Kanade-Lucas-Tomasi (KLT) Feature Tracker
  publication-title: Comput. Vis.
– start-page: 2672
  year: 2013
  end-page: 2675
  ident: bib0055
  article-title: Camera-based system for contactless monitoring of respiration
  publication-title: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), vol. 2013
– volume: 2015
  start-page: 1
  year: 2015
  end-page: 13
  ident: bib0035
  article-title: Noncontact detection and analysis of respiratory function using microwave doppler radar
  publication-title: J. Sens.
– volume: 27
  start-page: 379
  year: 2000
  end-page: 382
  ident: bib0155
  article-title: A new QRS detection algorithm based on the Hilbert transform
  publication-title: Comput. Cardiol.
– volume: 122
  start-page: 123
  year: 2000
  end-page: 129
  ident: bib0010
  article-title: Breathing pattern in humans: diversity and individuality
  publication-title: Respir. Physiol.
– reference: N. Rodriguez Ibanez, M. Fernandez Chimeno, J.J. Ramos Castro, M.A. Garcia Gonzalez, E. Montseny Masip, D. Bande Matinez, Method and System for Determining an Individual's State of Attention, US2011028857 (A1) (2014).
– reference: .
– volume: 16
  start-page: 996
  year: 2016
  ident: bib0060
  article-title: Microsoft Kinect visual and depth sensors for breathing and heart rate analysis
  publication-title: Sensors
– start-page: 297
  year: 2003
  end-page: 304
  ident: bib0125
  article-title: Empirical analysis of detection cascades of boosted classifiers for rapid object detection
  publication-title: Proceedings of the 25th DAGM Pattern Recognition Symposium
– volume: 46
  start-page: 523
  year: 2011
  end-page: 529
  ident: bib0020
  article-title: Respiration rate monitoring methods: a review
  publication-title: Pediatr. Pulmonol.
– start-page: 101
  year: 2014
  end-page: 104
  ident: bib0105
  article-title: Non contact monitoring of respiratory function via depth sensing
  publication-title: 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2014
– volume: 57
  start-page: 137
  year: 2004
  end-page: 154
  ident: bib0120
  article-title: Robust real-time face detection
  publication-title: Int. J. Comput. Vis.
– volume: 10
  start-page: 1732
  year: 2010
  end-page: 1739
  ident: bib0040
  article-title: Noncontact respiration rate measurement system using an ultrasonic proximity sensor
  publication-title: IEEE Sens. J.
– volume: 1
  start-page: 192
  year: 2015
  end-page: 195
  ident: bib0065
  article-title: Respiratory motion tracking using Microsoft's Kinect v2 camera
  publication-title: Curr. Direct. Biomed. Eng.
– volume: 25
  start-page: 141
  year: 2015
  end-page: 151
  ident: bib0180
  article-title: Understanding Bland–Altman analysis
  publication-title: Biochem. Med.
– start-page: 264
  year: 2015
  end-page: 267
  ident: bib0150
  article-title: RR time series comparison obtained by H7 polar sensors or by photoplethysmography using smartphones: breathing and devices influences
  publication-title: 6th European Conference of the International Federation for Medical and Biological Engineering SE–66, vol. 45 of IFMBE Proceedings
– volume: 57
  start-page: 988
  year: 2010
  end-page: 998
  ident: bib0080
  article-title: Thermistor at a distance: unobtrusive measurement of breathing
  publication-title: IEEE Trans. Biomed. Eng.
– volume: 29
  start-page: 1
  year: 1997
  ident: bib0165
  article-title: Postwar U.S. business cycles: an empirical investigation
  publication-title: J. Money Credit Bank.
– volume: 12
  start-page: 13167
  issue: 12
  year: 2012
  ident: 10.1016/j.bspc.2019.02.004_bib0030
  article-title: Development of a respiratory inductive plethysmography module supporting multiple sensors for wearable systems
  publication-title: Sensors
  doi: 10.3390/s121013167
– start-page: 101
  year: 2014
  ident: 10.1016/j.bspc.2019.02.004_bib0105
  article-title: Non contact monitoring of respiratory function via depth sensing
– ident: 10.1016/j.bspc.2019.02.004_bib0025
– start-page: 264
  year: 2015
  ident: 10.1016/j.bspc.2019.02.004_bib0150
  article-title: RR time series comparison obtained by H7 polar sensors or by photoplethysmography using smartphones: breathing and devices influences
– volume: 8
  start-page: 30
  year: 2015
  ident: 10.1016/j.bspc.2019.02.004_bib0015
  article-title: Ambient and unobtrusive cardiorespiratory monitoring techniques
  publication-title: IEEE Rev. Biomed. Eng.
  doi: 10.1109/RBME.2015.2414661
– start-page: 593
  year: 1994
  ident: 10.1016/j.bspc.2019.02.004_bib0130
  article-title: Good features to track
  publication-title: in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94
– volume: 46
  start-page: 523
  issue: 6
  year: 2011
  ident: 10.1016/j.bspc.2019.02.004_bib0020
  article-title: Respiration rate monitoring methods: a review
  publication-title: Pediatr. Pulmonol.
  doi: 10.1002/ppul.21416
– volume: 25
  start-page: 68
  year: 2016
  ident: 10.1016/j.bspc.2019.02.004_bib0160
  article-title: De-noising of ECG signal and QRS detection using hilbert transform and adaptive thresholding
  publication-title: Procedia Technol.
  doi: 10.1016/j.protcy.2016.08.082
– start-page: 297
  year: 2003
  ident: 10.1016/j.bspc.2019.02.004_bib0125
  article-title: Empirical analysis of detection cascades of boosted classifiers for rapid object detection
  publication-title: Proceedings of the 25th DAGM Pattern Recognition Symposium
  doi: 10.1007/978-3-540-45243-0_39
– volume: 27
  start-page: 135
  issue: 3
  year: 2002
  ident: 10.1016/j.bspc.2019.02.004_bib0045
  article-title: Nasal mucosal temperature during respiration
  publication-title: Clin. Otolaryngol. Allied Sci.
  doi: 10.1046/j.1365-2273.2002.00544.x
– year: 2011
  ident: 10.1016/j.bspc.2019.02.004_bib0090
– ident: 10.1016/j.bspc.2019.02.004_bib0085
– volume: 31
  start-page: 115
  issue: 2
  year: 1982
  ident: 10.1016/j.bspc.2019.02.004_bib0170
  article-title: An extension of Shapiro and Wilk's W test for normality to large samples
  publication-title: Appl. Stat.
  doi: 10.2307/2347973
– volume: 6
  start-page: 4378
  issue: 11
  year: 2015
  ident: 10.1016/j.bspc.2019.02.004_bib0075
  article-title: Remote monitoring of breathing dynamics using infrared thermography
  publication-title: Biomed. Opt. Express
  doi: 10.1364/BOE.6.004378
– volume: 81
  start-page: 45
  issue: September
  year: 2017
  ident: 10.1016/j.bspc.2019.02.004_bib0135
  article-title: Derivation of respiration rate from ambulatory ECG and PPG using ensemble empirical mode decomposition: comparison and fusion
  publication-title: Comput. Biol. Med.
  doi: 10.1016/j.compbiomed.2016.12.005
– start-page: 6055
  year: 2011
  ident: 10.1016/j.bspc.2019.02.004_bib0005
  article-title: Drowsiness detection by thoracic effort signal analysis in real driving environments
– volume: 27
  start-page: 379
  year: 2000
  ident: 10.1016/j.bspc.2019.02.004_bib0155
  article-title: A new QRS detection algorithm based on the Hilbert transform
  publication-title: Comput. Cardiol.
– volume: 2015
  start-page: 1
  issue: February
  year: 2015
  ident: 10.1016/j.bspc.2019.02.004_bib0035
  article-title: Noncontact detection and analysis of respiratory function using microwave doppler radar
  publication-title: J. Sens.
– start-page: 2672
  year: 2013
  ident: 10.1016/j.bspc.2019.02.004_bib0055
  article-title: Camera-based system for contactless monitoring of respiration
– volume: 46
  start-page: 147
  issue: 2
  year: 2008
  ident: 10.1016/j.bspc.2019.02.004_bib0145
  article-title: An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation
  publication-title: Med. Biol. Eng. Comput.
  doi: 10.1007/s11517-007-0302-y
– volume: 57
  start-page: 137
  issue: 2
  year: 2004
  ident: 10.1016/j.bspc.2019.02.004_bib0120
  article-title: Robust real-time face detection
  publication-title: Int. J. Comput. Vis.
  doi: 10.1023/B:VISI.0000013087.49260.fb
– volume: 1
  start-page: 81
  issue: 3
  year: 2014
  ident: 10.1016/j.bspc.2019.02.004_bib0070
  article-title: Respiratory rate detection algorithm based on RGB-D camera: theoretical background and experimental results
  publication-title: Healthc. Technol. Lett.
  doi: 10.1049/htl.2014.0063
– volume: 20
  start-page: 171
  issue: 1
  year: 2004
  ident: 10.1016/j.bspc.2019.02.004_bib0140
  article-title: Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition
  publication-title: Chos Solitons Fractals
  doi: 10.1016/S0960-0779(03)00441-7
– volume: 29
  start-page: 1
  issue: 1
  year: 1997
  ident: 10.1016/j.bspc.2019.02.004_bib0165
  article-title: Postwar U.S. business cycles: an empirical investigation
  publication-title: J. Money Credit Bank.
  doi: 10.2307/2953682
– volume: 35
  start-page: 807
  issue: 5
  year: 2014
  ident: 10.1016/j.bspc.2019.02.004_bib0050
  article-title: Non-contact video-based vital sign monitoring using ambient light and auto-regressive models
  publication-title: Physiol. Meas.
  doi: 10.1088/0967-3334/35/5/807
– year: 2016
  ident: 10.1016/j.bspc.2019.02.004_bib0110
  article-title: Real-time respiration rate measurement from thoracoabdominal movement with a consumer grade camera
– volume: 06
  start-page: 20
  issue: 01
  year: 2016
  ident: 10.1016/j.bspc.2019.02.004_bib0115
  article-title: Markerless respiratory motion tracking using single depth camera
  publication-title: Open J. Med. Imag.
  doi: 10.4236/ojmi.2016.61003
– volume: 16
  start-page: 996
  issue: 12
  year: 2016
  ident: 10.1016/j.bspc.2019.02.004_bib0060
  article-title: Microsoft Kinect visual and depth sensors for breathing and heart rate analysis
  publication-title: Sensors
  doi: 10.3390/s16070996
– start-page: 9
  issue: EEE6503
  year: 2009
  ident: 10.1016/j.bspc.2019.02.004_bib0100
  article-title: Kanade-Lucas-Tomasi (KLT) Feature Tracker
  publication-title: Comput. Vis.
– volume: 122
  start-page: 123
  issue: 2-3
  year: 2000
  ident: 10.1016/j.bspc.2019.02.004_bib0010
  article-title: Breathing pattern in humans: diversity and individuality
  publication-title: Respir. Physiol.
  doi: 10.1016/S0034-5687(00)00154-7
– volume: 10
  start-page: 1732
  issue: 11
  year: 2010
  ident: 10.1016/j.bspc.2019.02.004_bib0040
  article-title: Noncontact respiration rate measurement system using an ultrasonic proximity sensor
  publication-title: IEEE Sens. J.
  doi: 10.1109/JSEN.2010.2044239
– volume: 1
  start-page: 192
  issue: 1
  year: 2015
  ident: 10.1016/j.bspc.2019.02.004_bib0065
  article-title: Respiratory motion tracking using Microsoft's Kinect v2 camera
  publication-title: Curr. Direct. Biomed. Eng.
  doi: 10.1515/cdbme-2015-0048
– volume: 57
  start-page: 988
  issue: 4
  year: 2010
  ident: 10.1016/j.bspc.2019.02.004_bib0080
  article-title: Thermistor at a distance: unobtrusive measurement of breathing
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2009.2032415
– start-page: 289
  year: 2015
  ident: 10.1016/j.bspc.2019.02.004_bib0175
– volume: 25
  start-page: 141
  issue: 2
  year: 2015
  ident: 10.1016/j.bspc.2019.02.004_bib0180
  article-title: Understanding Bland–Altman analysis
  publication-title: Biochem. Med.
  doi: 10.11613/BM.2015.015
– volume: 79
  start-page: 373
  issue: 4
  year: 2001
  ident: 10.1016/j.bspc.2019.02.004_bib0095
  article-title: World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects
  publication-title: Bull. World Health Organ.
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Snippet •Assessment of respiratory rhythm obtained through video-based methods.•Three video-based methods were evaluated versus a gold standard: an RGB camera, a depth...
The aim of this work is to characterize the di erences in the respiratory rhythm obtained through three video based methods by comparing the obtained...
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StartPage 138
SubjectTerms Aparell respiratori
Camera
Depth
Enginyeria biomèdica
Mesurament
Proves funcionals
Pulmonary function tests
Respiració
Respiration - Measurement
Respiration rhythm
Respiration variability
Thermal
Video-based
Àrees temàtiques de la UPC
Title Comparison of video-based methods for respiration rhythm measurement
URI https://dx.doi.org/10.1016/j.bspc.2019.02.004
https://recercat.cat/handle/2072/352109
Volume 51
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