Abstract

Continuous monitoring of respiration is essential for early detection of critical illness. Current methods require sensors attached to the body and/or are not robust to subject motion. Alternative camera-based solutions have been presented using motion vectors and remote photoplethysmography. In this work, we present a non-contact camera-based method to detect respiration, which can operate in both visible and dark lighting conditions by detecting the respiratory-induced colour differences of the skin. We make use of the close similarity between skin colour variations caused by the beating of the heart and those caused by respiration, leading to a much improved signal quality compared to single-channel approaches. Essentially, we propose to find the linear combination of colour channels which suppresses the distortions best in a frequency band including pulse rate, and subsequently we use this same linear combination to extract the respiratory signal in a lower frequency band. Evaluation results obtained from recordings on healthy subjects which perform challenging scenarios, including motion, show that respiration can be accurately detected over the entire range of respiratory frequencies, with a correlation coefficient of 0.96 in visible light and 0.98 in infrared, compared to 0.86 with the best-performing non-contact benchmark algorithm. Furthermore, evaluation on a set of videos recorded in a Neonatal Intensive Care Unit (NICU) shows that this technique looks promising as a future alternative to current contact-sensors showing a correlation coefficient of 0.87.

© 2016 Optical Society of America

Full Article  |  PDF Article
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References

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    [Crossref] [PubMed]

2016 (2)

P. H. Charlton, T. Bonnici, L. Tarassenko, D. A. Clifton, R. Beale, and P. J. Watkinson, “An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram,” Physiol. Meas. 37(4), 610–626 (2016).
[Crossref] [PubMed]

A. Sikdar, S. K. Behera, and D. P. Dogra, “Computer vision guided human pulse rate estimation: A review,” IEEE Rev. Biomed. Eng. 9, 91–105 (2016).
[Crossref]

2015 (3)

W. Karlen, A. Garde, D. Myers, C. Scheffer, J. Ansermino, and G. Dumont, “Estimation of respiratory rate from photoplethysmographic imaging videos compared to pulse oximetry,” IEEE J. Biomed. Health Inform. 19(4), 1331–1338 (2015).
[Crossref] [PubMed]

A. Heinrich, F. van Heesch, B. Puvvula, and M. Rocque, “Video based actigraphy and breathing monitoring from the bedside table of shared beds,” J. Ambient. Intell. Humaniz. Comput. 6(1), 107–120 (2015).
[Crossref]

M. van Gastel, S. Stuijk, and G. de Haan, “Motion robust remote-PPG in infrared,” IEEE Trans. Biomed. Eng. 62(5), 1425–1433 (2015).

2014 (1)

G. de Haan and A. van Leest, “Improved motion robustness of remote-PPG by using the blood volume pulse signature,” Physiol. Meas. 35(9), 1913–1926 (2014).
[Crossref] [PubMed]

2013 (4)

G. de Haan and V. Jeanne, “Robust pulse-rate from chrominance-based rPPG,” IEEE Trans. Biomed. Eng. 60(10), 2878–2886 (2013).
[Crossref] [PubMed]

F. Bousefsaf, C. Maaoui, and A. Pruski, “Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate,” Biomed. Signal Process. Control 8(6), 568–574 (2013).
[Crossref]

F. Zhao, M. Li, Y. Qian, and J. Z. Tsien, “Remote measurements of heart and respiration rates for telemedicine,” PLOS ONE 8(10), e71384 (2013).
[Crossref] [PubMed]

L. M. Nilsson, “Respiration signals from photoplethysmography,” Anesth. Analg. 117(4), 859–865 (2013).
[Crossref] [PubMed]

2012 (1)

P. S. Addison, J. N. Watson, M. L. Mestek, and R. S. Mecca, “Developing an algorithm for pulse oximetry derived respiratory rate (rroxi): a healthy volunteer study,” J. Clin. Monit. Comput. 26(1), 45–51 (2012).
[Crossref] [PubMed]

2011 (3)

M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Advancements in noncontact, multiparameter physiological measurements using a webcam,” IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011).
[Crossref]

Y. Sun, S. Hu, V. Azorin-Peris, S. Greenwald, J. Chambers, and Y. Zhu, “Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise,” J. Biomed. Opt. 16(7), 077010 (2011).
[Crossref] [PubMed]

F. Q. Al-Khalidi, R. Saatchi, D. Burke, H. Elphick, and S. Tan, “Respiration rate monitoring methods: a review,” Pediatr. Pulmonol. 46(6), 523–529 (2011).
[Crossref] [PubMed]

2010 (3)

C. Seymour, J. Kahn, C. Cooke, T. Watkins, S. Heckbert, and T. Rea, “Prediction of critical illness during out-of-hospital emergency care,” J. Am. Med. Assoc. 304(7), 747–754 (2010).
[Crossref]

J. Fei and I. Pavlidis, “Thermistor at a distance: unobtrusive measurement of breathing,” IEEE Trans. Biomed. Eng. 57(4), 988–998 (2010).
[Crossref]

J. Li, J. Jin, X. Chen, W. Sun, and P. Guo, “Comparison of respiratory-induced variations in photoplethysmographic signals,” Physiol. Meas. 31(3), 415–425 (2010).
[Crossref] [PubMed]

2009 (1)

K. H. Chon, S. Dash, and K. Ju, “Estimation of respiratory rate from photoplethysmogram data using time–frequency spectral estimation,” IEEE Trans. Biomed. Eng. 56(8), 2054–2063 (2009).
[Crossref] [PubMed]

2008 (1)

2007 (1)

S. G. Fleming and L. Tarassenko, “A comparison of signal processing techniques for the extraction of breathing rate from the photoplethysmogram,” Int. J. Biol. Med. Sci. 2(4), 232–236 (2007).

2004 (1)

P. Leonard, N. R. Grubb, P. S. Addison, D. Clifton, and J. N. Watson, “An algorithm for the detection of individual breaths from the pulse oximeter waveform,” J. Clin. Monit. Comput. 18(5–6), 309–312 (2004).
[Crossref]

2003 (1)

L. Nilsson, A. Johansson, and S. Kalman, “Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure,” Med. Biol. Eng. Comput. 41(3), 249–254 (2003).
[Crossref] [PubMed]

2002 (1)

M. Hülsbusch and V. Blazek, “Contactless mapping of rhythmical phenomena in tissue perfusion using PPGI,” Proc. SPIE 4683, 110–117 (2002).
[Crossref]

1999 (1)

A. Johansson and P. Öberg, “Estimation of respiratory volumes from the photoplethysmographic signal. part i: experimental results,” Med. Biol. Eng. Comput. 37(1), 42–47 (1999).
[Crossref] [PubMed]

1992 (1)

L.-G. Lindberg, H. Ugnell, and P. Öberg, “Monitoring of respiratory and heart rates using a fibre-optic sensor,” Med. Biol. Eng. Comput. 30(5), 533–537 (1992).

1983 (1)

E. Tur, M. Tur, H. I. Maibach, and R. H. Guy, “Basal perfusion of the cutaneous microcirculation: measurements as a function of anatomic position,” J. Invest. Dermatol. 81(5), 442–446 (1983).

Addison, P. S.

P. S. Addison, J. N. Watson, M. L. Mestek, and R. S. Mecca, “Developing an algorithm for pulse oximetry derived respiratory rate (rroxi): a healthy volunteer study,” J. Clin. Monit. Comput. 26(1), 45–51 (2012).
[Crossref] [PubMed]

P. Leonard, N. R. Grubb, P. S. Addison, D. Clifton, and J. N. Watson, “An algorithm for the detection of individual breaths from the pulse oximeter waveform,” J. Clin. Monit. Comput. 18(5–6), 309–312 (2004).
[Crossref]

Adib, F.

F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller, “Smart homes that monitor breathing and heart rate,” in “Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems,” pp. 837–846 (2015).

Al-Khalidi, F. Q.

F. Q. Al-Khalidi, R. Saatchi, D. Burke, H. Elphick, and S. Tan, “Respiration rate monitoring methods: a review,” Pediatr. Pulmonol. 46(6), 523–529 (2011).
[Crossref] [PubMed]

Ansermino, J.

W. Karlen, A. Garde, D. Myers, C. Scheffer, J. Ansermino, and G. Dumont, “Estimation of respiratory rate from photoplethysmographic imaging videos compared to pulse oximetry,” IEEE J. Biomed. Health Inform. 19(4), 1331–1338 (2015).
[Crossref] [PubMed]

Azorin-Peris, V.

Y. Sun, S. Hu, V. Azorin-Peris, S. Greenwald, J. Chambers, and Y. Zhu, “Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise,” J. Biomed. Opt. 16(7), 077010 (2011).
[Crossref] [PubMed]

Bartula, M.

M. Bartula, T. Tigges, and J. Muehlsteff, “Camera-based system for contactless monitoring of respiration,” in “Engineering in Medicine and Biology Society, 2013 35th Annual International Conference of the IEEE,” pp. 2672–2675 (2013).

Beale, R.

P. H. Charlton, T. Bonnici, L. Tarassenko, D. A. Clifton, R. Beale, and P. J. Watkinson, “An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram,” Physiol. Meas. 37(4), 610–626 (2016).
[Crossref] [PubMed]

Behera, S. K.

A. Sikdar, S. K. Behera, and D. P. Dogra, “Computer vision guided human pulse rate estimation: A review,” IEEE Rev. Biomed. Eng. 9, 91–105 (2016).
[Crossref]

Blazek, V.

M. Hülsbusch and V. Blazek, “Contactless mapping of rhythmical phenomena in tissue perfusion using PPGI,” Proc. SPIE 4683, 110–117 (2002).
[Crossref]

Bonnici, T.

P. H. Charlton, T. Bonnici, L. Tarassenko, D. A. Clifton, R. Beale, and P. J. Watkinson, “An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram,” Physiol. Meas. 37(4), 610–626 (2016).
[Crossref] [PubMed]

Bousefsaf, F.

F. Bousefsaf, C. Maaoui, and A. Pruski, “Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate,” Biomed. Signal Process. Control 8(6), 568–574 (2013).
[Crossref]

Burke, D.

F. Q. Al-Khalidi, R. Saatchi, D. Burke, H. Elphick, and S. Tan, “Respiration rate monitoring methods: a review,” Pediatr. Pulmonol. 46(6), 523–529 (2011).
[Crossref] [PubMed]

Caro, C. G.

C. G. Caro, The mechanics of the circulation (Cambridge University Press, 2012).

Chambers, J.

Y. Sun, S. Hu, V. Azorin-Peris, S. Greenwald, J. Chambers, and Y. Zhu, “Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise,” J. Biomed. Opt. 16(7), 077010 (2011).
[Crossref] [PubMed]

Charlton, P. H.

P. H. Charlton, T. Bonnici, L. Tarassenko, D. A. Clifton, R. Beale, and P. J. Watkinson, “An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram,” Physiol. Meas. 37(4), 610–626 (2016).
[Crossref] [PubMed]

Chen, X.

J. Li, J. Jin, X. Chen, W. Sun, and P. Guo, “Comparison of respiratory-induced variations in photoplethysmographic signals,” Physiol. Meas. 31(3), 415–425 (2010).
[Crossref] [PubMed]

Chon, K. H.

K. H. Chon, S. Dash, and K. Ju, “Estimation of respiratory rate from photoplethysmogram data using time–frequency spectral estimation,” IEEE Trans. Biomed. Eng. 56(8), 2054–2063 (2009).
[Crossref] [PubMed]

Clifton, D.

P. Leonard, N. R. Grubb, P. S. Addison, D. Clifton, and J. N. Watson, “An algorithm for the detection of individual breaths from the pulse oximeter waveform,” J. Clin. Monit. Comput. 18(5–6), 309–312 (2004).
[Crossref]

Clifton, D. A.

P. H. Charlton, T. Bonnici, L. Tarassenko, D. A. Clifton, R. Beale, and P. J. Watkinson, “An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram,” Physiol. Meas. 37(4), 610–626 (2016).
[Crossref] [PubMed]

Cooke, C.

C. Seymour, J. Kahn, C. Cooke, T. Watkins, S. Heckbert, and T. Rea, “Prediction of critical illness during out-of-hospital emergency care,” J. Am. Med. Assoc. 304(7), 747–754 (2010).
[Crossref]

Corral Martinez, L. F.

L. F. Corral Martinez, G. Paez, and M. Strojnik, “Optimal wavelength selection for noncontact reflection photoplethysmography,” 22nd Congress of the International Commission for Optics8011, 801191 (2011).

Dash, S.

K. H. Chon, S. Dash, and K. Ju, “Estimation of respiratory rate from photoplethysmogram data using time–frequency spectral estimation,” IEEE Trans. Biomed. Eng. 56(8), 2054–2063 (2009).
[Crossref] [PubMed]

de Haan, G.

M. van Gastel, S. Stuijk, and G. de Haan, “Motion robust remote-PPG in infrared,” IEEE Trans. Biomed. Eng. 62(5), 1425–1433 (2015).

G. de Haan and A. van Leest, “Improved motion robustness of remote-PPG by using the blood volume pulse signature,” Physiol. Meas. 35(9), 1913–1926 (2014).
[Crossref] [PubMed]

G. de Haan and V. Jeanne, “Robust pulse-rate from chrominance-based rPPG,” IEEE Trans. Biomed. Eng. 60(10), 2878–2886 (2013).
[Crossref] [PubMed]

Dogra, D. P.

A. Sikdar, S. K. Behera, and D. P. Dogra, “Computer vision guided human pulse rate estimation: A review,” IEEE Rev. Biomed. Eng. 9, 91–105 (2016).
[Crossref]

Dumont, G.

W. Karlen, A. Garde, D. Myers, C. Scheffer, J. Ansermino, and G. Dumont, “Estimation of respiratory rate from photoplethysmographic imaging videos compared to pulse oximetry,” IEEE J. Biomed. Health Inform. 19(4), 1331–1338 (2015).
[Crossref] [PubMed]

Elphick, H.

F. Q. Al-Khalidi, R. Saatchi, D. Burke, H. Elphick, and S. Tan, “Respiration rate monitoring methods: a review,” Pediatr. Pulmonol. 46(6), 523–529 (2011).
[Crossref] [PubMed]

Fei, J.

J. Fei and I. Pavlidis, “Thermistor at a distance: unobtrusive measurement of breathing,” IEEE Trans. Biomed. Eng. 57(4), 988–998 (2010).
[Crossref]

Fleming, S. G.

S. G. Fleming and L. Tarassenko, “A comparison of signal processing techniques for the extraction of breathing rate from the photoplethysmogram,” Int. J. Biol. Med. Sci. 2(4), 232–236 (2007).

Garde, A.

W. Karlen, A. Garde, D. Myers, C. Scheffer, J. Ansermino, and G. Dumont, “Estimation of respiratory rate from photoplethysmographic imaging videos compared to pulse oximetry,” IEEE J. Biomed. Health Inform. 19(4), 1331–1338 (2015).
[Crossref] [PubMed]

Greenwald, S.

Y. Sun, S. Hu, V. Azorin-Peris, S. Greenwald, J. Chambers, and Y. Zhu, “Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise,” J. Biomed. Opt. 16(7), 077010 (2011).
[Crossref] [PubMed]

Greneker, E.

E. Greneker, “Radar sensing of heartbeat and respiration at a distance with applications of the technology,” in “Radar 97 (Conf”. Publ. No. 449),” pp. 150–154 (1997).

Grubb, N. R.

P. Leonard, N. R. Grubb, P. S. Addison, D. Clifton, and J. N. Watson, “An algorithm for the detection of individual breaths from the pulse oximeter waveform,” J. Clin. Monit. Comput. 18(5–6), 309–312 (2004).
[Crossref]

Guo, P.

J. Li, J. Jin, X. Chen, W. Sun, and P. Guo, “Comparison of respiratory-induced variations in photoplethysmographic signals,” Physiol. Meas. 31(3), 415–425 (2010).
[Crossref] [PubMed]

Guy, R. H.

E. Tur, M. Tur, H. I. Maibach, and R. H. Guy, “Basal perfusion of the cutaneous microcirculation: measurements as a function of anatomic position,” J. Invest. Dermatol. 81(5), 442–446 (1983).

Heckbert, S.

C. Seymour, J. Kahn, C. Cooke, T. Watkins, S. Heckbert, and T. Rea, “Prediction of critical illness during out-of-hospital emergency care,” J. Am. Med. Assoc. 304(7), 747–754 (2010).
[Crossref]

Heinrich, A.

A. Heinrich, F. van Heesch, B. Puvvula, and M. Rocque, “Video based actigraphy and breathing monitoring from the bedside table of shared beds,” J. Ambient. Intell. Humaniz. Comput. 6(1), 107–120 (2015).
[Crossref]

Hu, S.

Y. Sun, S. Hu, V. Azorin-Peris, S. Greenwald, J. Chambers, and Y. Zhu, “Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise,” J. Biomed. Opt. 16(7), 077010 (2011).
[Crossref] [PubMed]

Hülsbusch, M.

M. Hülsbusch and V. Blazek, “Contactless mapping of rhythmical phenomena in tissue perfusion using PPGI,” Proc. SPIE 4683, 110–117 (2002).
[Crossref]

Jeanne, V.

G. de Haan and V. Jeanne, “Robust pulse-rate from chrominance-based rPPG,” IEEE Trans. Biomed. Eng. 60(10), 2878–2886 (2013).
[Crossref] [PubMed]

Jin, J.

J. Li, J. Jin, X. Chen, W. Sun, and P. Guo, “Comparison of respiratory-induced variations in photoplethysmographic signals,” Physiol. Meas. 31(3), 415–425 (2010).
[Crossref] [PubMed]

Johansson, A.

L. Nilsson, A. Johansson, and S. Kalman, “Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure,” Med. Biol. Eng. Comput. 41(3), 249–254 (2003).
[Crossref] [PubMed]

A. Johansson and P. Öberg, “Estimation of respiratory volumes from the photoplethysmographic signal. part i: experimental results,” Med. Biol. Eng. Comput. 37(1), 42–47 (1999).
[Crossref] [PubMed]

Ju, K.

K. H. Chon, S. Dash, and K. Ju, “Estimation of respiratory rate from photoplethysmogram data using time–frequency spectral estimation,” IEEE Trans. Biomed. Eng. 56(8), 2054–2063 (2009).
[Crossref] [PubMed]

Kabelac, Z.

F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller, “Smart homes that monitor breathing and heart rate,” in “Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems,” pp. 837–846 (2015).

Kahn, J.

C. Seymour, J. Kahn, C. Cooke, T. Watkins, S. Heckbert, and T. Rea, “Prediction of critical illness during out-of-hospital emergency care,” J. Am. Med. Assoc. 304(7), 747–754 (2010).
[Crossref]

Kalman, S.

L. Nilsson, A. Johansson, and S. Kalman, “Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure,” Med. Biol. Eng. Comput. 41(3), 249–254 (2003).
[Crossref] [PubMed]

Kanade, T.

C. Tomasi and T. Kanade, “Detection and tracking of point features,” Tech. rep., Int. J. Comput. Vision (1991).

Karlen, W.

W. Karlen, A. Garde, D. Myers, C. Scheffer, J. Ansermino, and G. Dumont, “Estimation of respiratory rate from photoplethysmographic imaging videos compared to pulse oximetry,” IEEE J. Biomed. Health Inform. 19(4), 1331–1338 (2015).
[Crossref] [PubMed]

Katabi, D.

F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller, “Smart homes that monitor breathing and heart rate,” in “Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems,” pp. 837–846 (2015).

Kocejko, T.

M. Lewandowska, J. Ruminski, T. Kocejko, and J. Nowak, “Measuring pulse rate with a webcam - a non-contact method for evaluating cardiac activity,” in “Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on” pp. 405–410 (2011).
[Crossref] [PubMed]

Leonard, P.

P. Leonard, N. R. Grubb, P. S. Addison, D. Clifton, and J. N. Watson, “An algorithm for the detection of individual breaths from the pulse oximeter waveform,” J. Clin. Monit. Comput. 18(5–6), 309–312 (2004).
[Crossref]

Lewandowska, M.

M. Lewandowska, J. Ruminski, T. Kocejko, and J. Nowak, “Measuring pulse rate with a webcam - a non-contact method for evaluating cardiac activity,” in “Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on” pp. 405–410 (2011).
[Crossref] [PubMed]

Li, J.

J. Li, J. Jin, X. Chen, W. Sun, and P. Guo, “Comparison of respiratory-induced variations in photoplethysmographic signals,” Physiol. Meas. 31(3), 415–425 (2010).
[Crossref] [PubMed]

Li, M.

F. Zhao, M. Li, Y. Qian, and J. Z. Tsien, “Remote measurements of heart and respiration rates for telemedicine,” PLOS ONE 8(10), e71384 (2013).
[Crossref] [PubMed]

Lindberg, L.-G.

L.-G. Lindberg, H. Ugnell, and P. Öberg, “Monitoring of respiratory and heart rates using a fibre-optic sensor,” Med. Biol. Eng. Comput. 30(5), 533–537 (1992).

Maaoui, C.

F. Bousefsaf, C. Maaoui, and A. Pruski, “Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate,” Biomed. Signal Process. Control 8(6), 568–574 (2013).
[Crossref]

Maibach, H. I.

E. Tur, M. Tur, H. I. Maibach, and R. H. Guy, “Basal perfusion of the cutaneous microcirculation: measurements as a function of anatomic position,” J. Invest. Dermatol. 81(5), 442–446 (1983).

Mao, H.

F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller, “Smart homes that monitor breathing and heart rate,” in “Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems,” pp. 837–846 (2015).

McDuff, D. J.

M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Advancements in noncontact, multiparameter physiological measurements using a webcam,” IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011).
[Crossref]

Mecca, R. S.

P. S. Addison, J. N. Watson, M. L. Mestek, and R. S. Mecca, “Developing an algorithm for pulse oximetry derived respiratory rate (rroxi): a healthy volunteer study,” J. Clin. Monit. Comput. 26(1), 45–51 (2012).
[Crossref] [PubMed]

Mestek, M. L.

P. S. Addison, J. N. Watson, M. L. Mestek, and R. S. Mecca, “Developing an algorithm for pulse oximetry derived respiratory rate (rroxi): a healthy volunteer study,” J. Clin. Monit. Comput. 26(1), 45–51 (2012).
[Crossref] [PubMed]

Miller, R. C.

F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller, “Smart homes that monitor breathing and heart rate,” in “Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems,” pp. 837–846 (2015).

Muehlsteff, J.

M. Bartula, T. Tigges, and J. Muehlsteff, “Camera-based system for contactless monitoring of respiration,” in “Engineering in Medicine and Biology Society, 2013 35th Annual International Conference of the IEEE,” pp. 2672–2675 (2013).

Myers, D.

W. Karlen, A. Garde, D. Myers, C. Scheffer, J. Ansermino, and G. Dumont, “Estimation of respiratory rate from photoplethysmographic imaging videos compared to pulse oximetry,” IEEE J. Biomed. Health Inform. 19(4), 1331–1338 (2015).
[Crossref] [PubMed]

Nelson, J. S.

Nilsson, L.

L. Nilsson, A. Johansson, and S. Kalman, “Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure,” Med. Biol. Eng. Comput. 41(3), 249–254 (2003).
[Crossref] [PubMed]

Nilsson, L. M.

L. M. Nilsson, “Respiration signals from photoplethysmography,” Anesth. Analg. 117(4), 859–865 (2013).
[Crossref] [PubMed]

Nowak, J.

M. Lewandowska, J. Ruminski, T. Kocejko, and J. Nowak, “Measuring pulse rate with a webcam - a non-contact method for evaluating cardiac activity,” in “Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on” pp. 405–410 (2011).
[Crossref] [PubMed]

Öberg, P.

A. Johansson and P. Öberg, “Estimation of respiratory volumes from the photoplethysmographic signal. part i: experimental results,” Med. Biol. Eng. Comput. 37(1), 42–47 (1999).
[Crossref] [PubMed]

L.-G. Lindberg, H. Ugnell, and P. Öberg, “Monitoring of respiratory and heart rates using a fibre-optic sensor,” Med. Biol. Eng. Comput. 30(5), 533–537 (1992).

Paez, G.

L. F. Corral Martinez, G. Paez, and M. Strojnik, “Optimal wavelength selection for noncontact reflection photoplethysmography,” 22nd Congress of the International Commission for Optics8011, 801191 (2011).

Pavlidis, I.

J. Fei and I. Pavlidis, “Thermistor at a distance: unobtrusive measurement of breathing,” IEEE Trans. Biomed. Eng. 57(4), 988–998 (2010).
[Crossref]

Picard, R. W.

M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Advancements in noncontact, multiparameter physiological measurements using a webcam,” IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011).
[Crossref]

Poh, M.-Z.

M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Advancements in noncontact, multiparameter physiological measurements using a webcam,” IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011).
[Crossref]

Pruski, A.

F. Bousefsaf, C. Maaoui, and A. Pruski, “Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate,” Biomed. Signal Process. Control 8(6), 568–574 (2013).
[Crossref]

Puvvula, B.

A. Heinrich, F. van Heesch, B. Puvvula, and M. Rocque, “Video based actigraphy and breathing monitoring from the bedside table of shared beds,” J. Ambient. Intell. Humaniz. Comput. 6(1), 107–120 (2015).
[Crossref]

Qian, Y.

F. Zhao, M. Li, Y. Qian, and J. Z. Tsien, “Remote measurements of heart and respiration rates for telemedicine,” PLOS ONE 8(10), e71384 (2013).
[Crossref] [PubMed]

Rea, T.

C. Seymour, J. Kahn, C. Cooke, T. Watkins, S. Heckbert, and T. Rea, “Prediction of critical illness during out-of-hospital emergency care,” J. Am. Med. Assoc. 304(7), 747–754 (2010).
[Crossref]

Rocque, M.

A. Heinrich, F. van Heesch, B. Puvvula, and M. Rocque, “Video based actigraphy and breathing monitoring from the bedside table of shared beds,” J. Ambient. Intell. Humaniz. Comput. 6(1), 107–120 (2015).
[Crossref]

Ruminski, J.

M. Lewandowska, J. Ruminski, T. Kocejko, and J. Nowak, “Measuring pulse rate with a webcam - a non-contact method for evaluating cardiac activity,” in “Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on” pp. 405–410 (2011).
[Crossref] [PubMed]

Saatchi, R.

F. Q. Al-Khalidi, R. Saatchi, D. Burke, H. Elphick, and S. Tan, “Respiration rate monitoring methods: a review,” Pediatr. Pulmonol. 46(6), 523–529 (2011).
[Crossref] [PubMed]

Scheffer, C.

W. Karlen, A. Garde, D. Myers, C. Scheffer, J. Ansermino, and G. Dumont, “Estimation of respiratory rate from photoplethysmographic imaging videos compared to pulse oximetry,” IEEE J. Biomed. Health Inform. 19(4), 1331–1338 (2015).
[Crossref] [PubMed]

Seymour, C.

C. Seymour, J. Kahn, C. Cooke, T. Watkins, S. Heckbert, and T. Rea, “Prediction of critical illness during out-of-hospital emergency care,” J. Am. Med. Assoc. 304(7), 747–754 (2010).
[Crossref]

Shi, J.

J. Shi and C. Tomasi, “Good features to track,” in “Computer Vision and Pattern Recognition (CVPR). Proceedings CVPR’94., 1994 IEEE Computer Society Conference on,” pp. 593–600 (1994).

Sikdar, A.

A. Sikdar, S. K. Behera, and D. P. Dogra, “Computer vision guided human pulse rate estimation: A review,” IEEE Rev. Biomed. Eng. 9, 91–105 (2016).
[Crossref]

Silvestri, L. A.

L. A. Silvestri, Saunders comprehensive review for the NCLEX-PN® examination (Elsevier Health Sciences, 2015).
[Crossref] [PubMed]

Strojnik, M.

L. F. Corral Martinez, G. Paez, and M. Strojnik, “Optimal wavelength selection for noncontact reflection photoplethysmography,” 22nd Congress of the International Commission for Optics8011, 801191 (2011).

Stuijk, S.

M. van Gastel, S. Stuijk, and G. de Haan, “Motion robust remote-PPG in infrared,” IEEE Trans. Biomed. Eng. 62(5), 1425–1433 (2015).

Sun, W.

J. Li, J. Jin, X. Chen, W. Sun, and P. Guo, “Comparison of respiratory-induced variations in photoplethysmographic signals,” Physiol. Meas. 31(3), 415–425 (2010).
[Crossref] [PubMed]

Sun, Y.

Y. Sun, S. Hu, V. Azorin-Peris, S. Greenwald, J. Chambers, and Y. Zhu, “Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise,” J. Biomed. Opt. 16(7), 077010 (2011).
[Crossref] [PubMed]

Svaasand, L. O.

Tan, S.

F. Q. Al-Khalidi, R. Saatchi, D. Burke, H. Elphick, and S. Tan, “Respiration rate monitoring methods: a review,” Pediatr. Pulmonol. 46(6), 523–529 (2011).
[Crossref] [PubMed]

Tarassenko, L.

P. H. Charlton, T. Bonnici, L. Tarassenko, D. A. Clifton, R. Beale, and P. J. Watkinson, “An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram,” Physiol. Meas. 37(4), 610–626 (2016).
[Crossref] [PubMed]

S. G. Fleming and L. Tarassenko, “A comparison of signal processing techniques for the extraction of breathing rate from the photoplethysmogram,” Int. J. Biol. Med. Sci. 2(4), 232–236 (2007).

Tigges, T.

M. Bartula, T. Tigges, and J. Muehlsteff, “Camera-based system for contactless monitoring of respiration,” in “Engineering in Medicine and Biology Society, 2013 35th Annual International Conference of the IEEE,” pp. 2672–2675 (2013).

Tomasi, C.

C. Tomasi and T. Kanade, “Detection and tracking of point features,” Tech. rep., Int. J. Comput. Vision (1991).

J. Shi and C. Tomasi, “Good features to track,” in “Computer Vision and Pattern Recognition (CVPR). Proceedings CVPR’94., 1994 IEEE Computer Society Conference on,” pp. 593–600 (1994).

Tsien, J. Z.

F. Zhao, M. Li, Y. Qian, and J. Z. Tsien, “Remote measurements of heart and respiration rates for telemedicine,” PLOS ONE 8(10), e71384 (2013).
[Crossref] [PubMed]

Tur, E.

E. Tur, M. Tur, H. I. Maibach, and R. H. Guy, “Basal perfusion of the cutaneous microcirculation: measurements as a function of anatomic position,” J. Invest. Dermatol. 81(5), 442–446 (1983).

Tur, M.

E. Tur, M. Tur, H. I. Maibach, and R. H. Guy, “Basal perfusion of the cutaneous microcirculation: measurements as a function of anatomic position,” J. Invest. Dermatol. 81(5), 442–446 (1983).

Ugnell, H.

L.-G. Lindberg, H. Ugnell, and P. Öberg, “Monitoring of respiratory and heart rates using a fibre-optic sensor,” Med. Biol. Eng. Comput. 30(5), 533–537 (1992).

van Gastel, M.

M. van Gastel, S. Stuijk, and G. de Haan, “Motion robust remote-PPG in infrared,” IEEE Trans. Biomed. Eng. 62(5), 1425–1433 (2015).

van Heesch, F.

A. Heinrich, F. van Heesch, B. Puvvula, and M. Rocque, “Video based actigraphy and breathing monitoring from the bedside table of shared beds,” J. Ambient. Intell. Humaniz. Comput. 6(1), 107–120 (2015).
[Crossref]

van Leest, A.

G. de Haan and A. van Leest, “Improved motion robustness of remote-PPG by using the blood volume pulse signature,” Physiol. Meas. 35(9), 1913–1926 (2014).
[Crossref] [PubMed]

Verkruysse, W.

Watkins, T.

C. Seymour, J. Kahn, C. Cooke, T. Watkins, S. Heckbert, and T. Rea, “Prediction of critical illness during out-of-hospital emergency care,” J. Am. Med. Assoc. 304(7), 747–754 (2010).
[Crossref]

Watkinson, P. J.

P. H. Charlton, T. Bonnici, L. Tarassenko, D. A. Clifton, R. Beale, and P. J. Watkinson, “An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram,” Physiol. Meas. 37(4), 610–626 (2016).
[Crossref] [PubMed]

Watson, J. N.

P. S. Addison, J. N. Watson, M. L. Mestek, and R. S. Mecca, “Developing an algorithm for pulse oximetry derived respiratory rate (rroxi): a healthy volunteer study,” J. Clin. Monit. Comput. 26(1), 45–51 (2012).
[Crossref] [PubMed]

P. Leonard, N. R. Grubb, P. S. Addison, D. Clifton, and J. N. Watson, “An algorithm for the detection of individual breaths from the pulse oximeter waveform,” J. Clin. Monit. Comput. 18(5–6), 309–312 (2004).
[Crossref]

Zhao, F.

F. Zhao, M. Li, Y. Qian, and J. Z. Tsien, “Remote measurements of heart and respiration rates for telemedicine,” PLOS ONE 8(10), e71384 (2013).
[Crossref] [PubMed]

Zhu, Y.

Y. Sun, S. Hu, V. Azorin-Peris, S. Greenwald, J. Chambers, and Y. Zhu, “Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise,” J. Biomed. Opt. 16(7), 077010 (2011).
[Crossref] [PubMed]

Anesth. Analg. (1)

L. M. Nilsson, “Respiration signals from photoplethysmography,” Anesth. Analg. 117(4), 859–865 (2013).
[Crossref] [PubMed]

Biomed. Signal Process. Control (1)

F. Bousefsaf, C. Maaoui, and A. Pruski, “Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate,” Biomed. Signal Process. Control 8(6), 568–574 (2013).
[Crossref]

IEEE J. Biomed. Health Inform. (1)

W. Karlen, A. Garde, D. Myers, C. Scheffer, J. Ansermino, and G. Dumont, “Estimation of respiratory rate from photoplethysmographic imaging videos compared to pulse oximetry,” IEEE J. Biomed. Health Inform. 19(4), 1331–1338 (2015).
[Crossref] [PubMed]

IEEE Rev. Biomed. Eng. (1)

A. Sikdar, S. K. Behera, and D. P. Dogra, “Computer vision guided human pulse rate estimation: A review,” IEEE Rev. Biomed. Eng. 9, 91–105 (2016).
[Crossref]

IEEE Trans. Biomed. Eng. (5)

K. H. Chon, S. Dash, and K. Ju, “Estimation of respiratory rate from photoplethysmogram data using time–frequency spectral estimation,” IEEE Trans. Biomed. Eng. 56(8), 2054–2063 (2009).
[Crossref] [PubMed]

M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Advancements in noncontact, multiparameter physiological measurements using a webcam,” IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011).
[Crossref]

M. van Gastel, S. Stuijk, and G. de Haan, “Motion robust remote-PPG in infrared,” IEEE Trans. Biomed. Eng. 62(5), 1425–1433 (2015).

G. de Haan and V. Jeanne, “Robust pulse-rate from chrominance-based rPPG,” IEEE Trans. Biomed. Eng. 60(10), 2878–2886 (2013).
[Crossref] [PubMed]

J. Fei and I. Pavlidis, “Thermistor at a distance: unobtrusive measurement of breathing,” IEEE Trans. Biomed. Eng. 57(4), 988–998 (2010).
[Crossref]

Int. J. Biol. Med. Sci. (1)

S. G. Fleming and L. Tarassenko, “A comparison of signal processing techniques for the extraction of breathing rate from the photoplethysmogram,” Int. J. Biol. Med. Sci. 2(4), 232–236 (2007).

J. Am. Med. Assoc. (1)

C. Seymour, J. Kahn, C. Cooke, T. Watkins, S. Heckbert, and T. Rea, “Prediction of critical illness during out-of-hospital emergency care,” J. Am. Med. Assoc. 304(7), 747–754 (2010).
[Crossref]

J. Ambient. Intell. Humaniz. Comput. (1)

A. Heinrich, F. van Heesch, B. Puvvula, and M. Rocque, “Video based actigraphy and breathing monitoring from the bedside table of shared beds,” J. Ambient. Intell. Humaniz. Comput. 6(1), 107–120 (2015).
[Crossref]

J. Biomed. Opt. (1)

Y. Sun, S. Hu, V. Azorin-Peris, S. Greenwald, J. Chambers, and Y. Zhu, “Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise,” J. Biomed. Opt. 16(7), 077010 (2011).
[Crossref] [PubMed]

J. Clin. Monit. Comput. (2)

P. Leonard, N. R. Grubb, P. S. Addison, D. Clifton, and J. N. Watson, “An algorithm for the detection of individual breaths from the pulse oximeter waveform,” J. Clin. Monit. Comput. 18(5–6), 309–312 (2004).
[Crossref]

P. S. Addison, J. N. Watson, M. L. Mestek, and R. S. Mecca, “Developing an algorithm for pulse oximetry derived respiratory rate (rroxi): a healthy volunteer study,” J. Clin. Monit. Comput. 26(1), 45–51 (2012).
[Crossref] [PubMed]

J. Invest. Dermatol. (1)

E. Tur, M. Tur, H. I. Maibach, and R. H. Guy, “Basal perfusion of the cutaneous microcirculation: measurements as a function of anatomic position,” J. Invest. Dermatol. 81(5), 442–446 (1983).

Med. Biol. Eng. Comput. (3)

L.-G. Lindberg, H. Ugnell, and P. Öberg, “Monitoring of respiratory and heart rates using a fibre-optic sensor,” Med. Biol. Eng. Comput. 30(5), 533–537 (1992).

A. Johansson and P. Öberg, “Estimation of respiratory volumes from the photoplethysmographic signal. part i: experimental results,” Med. Biol. Eng. Comput. 37(1), 42–47 (1999).
[Crossref] [PubMed]

L. Nilsson, A. Johansson, and S. Kalman, “Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure,” Med. Biol. Eng. Comput. 41(3), 249–254 (2003).
[Crossref] [PubMed]

Opt. Express (1)

Pediatr. Pulmonol. (1)

F. Q. Al-Khalidi, R. Saatchi, D. Burke, H. Elphick, and S. Tan, “Respiration rate monitoring methods: a review,” Pediatr. Pulmonol. 46(6), 523–529 (2011).
[Crossref] [PubMed]

Physiol. Meas. (3)

P. H. Charlton, T. Bonnici, L. Tarassenko, D. A. Clifton, R. Beale, and P. J. Watkinson, “An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram,” Physiol. Meas. 37(4), 610–626 (2016).
[Crossref] [PubMed]

J. Li, J. Jin, X. Chen, W. Sun, and P. Guo, “Comparison of respiratory-induced variations in photoplethysmographic signals,” Physiol. Meas. 31(3), 415–425 (2010).
[Crossref] [PubMed]

G. de Haan and A. van Leest, “Improved motion robustness of remote-PPG by using the blood volume pulse signature,” Physiol. Meas. 35(9), 1913–1926 (2014).
[Crossref] [PubMed]

PLOS ONE (1)

F. Zhao, M. Li, Y. Qian, and J. Z. Tsien, “Remote measurements of heart and respiration rates for telemedicine,” PLOS ONE 8(10), e71384 (2013).
[Crossref] [PubMed]

Proc. SPIE (1)

M. Hülsbusch and V. Blazek, “Contactless mapping of rhythmical phenomena in tissue perfusion using PPGI,” Proc. SPIE 4683, 110–117 (2002).
[Crossref]

Other (9)

L. F. Corral Martinez, G. Paez, and M. Strojnik, “Optimal wavelength selection for noncontact reflection photoplethysmography,” 22nd Congress of the International Commission for Optics8011, 801191 (2011).

C. Tomasi and T. Kanade, “Detection and tracking of point features,” Tech. rep., Int. J. Comput. Vision (1991).

J. Shi and C. Tomasi, “Good features to track,” in “Computer Vision and Pattern Recognition (CVPR). Proceedings CVPR’94., 1994 IEEE Computer Society Conference on,” pp. 593–600 (1994).

L. A. Silvestri, Saunders comprehensive review for the NCLEX-PN® examination (Elsevier Health Sciences, 2015).
[Crossref] [PubMed]

M. Lewandowska, J. Ruminski, T. Kocejko, and J. Nowak, “Measuring pulse rate with a webcam - a non-contact method for evaluating cardiac activity,” in “Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on” pp. 405–410 (2011).
[Crossref] [PubMed]

C. G. Caro, The mechanics of the circulation (Cambridge University Press, 2012).

E. Greneker, “Radar sensing of heartbeat and respiration at a distance with applications of the technology,” in “Radar 97 (Conf”. Publ. No. 449),” pp. 150–154 (1997).

M. Bartula, T. Tigges, and J. Muehlsteff, “Camera-based system for contactless monitoring of respiration,” in “Engineering in Medicine and Biology Society, 2013 35th Annual International Conference of the IEEE,” pp. 2672–2675 (2013).

F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller, “Smart homes that monitor breathing and heart rate,” in “Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems,” pp. 837–846 (2015).

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Figures (9)

Fig. 1
Fig. 1 Respiration modulates the PPG signal in three ways; 1) RIFV is a synchronization of heart rate with respiratory rate, 2) RIIV is a change in the baseline signal due to intrathoracic pressure variation, and 3) RIAV is a change in pulse strength caused by a decrease in cardiac output.
Fig. 2
Fig. 2 a) The measured absolute PPG spectrum of Corral [23] and the derived relative PPG spectrum, scaled to 1 for their peak locations. b) The absorption spectrum oxyhemoglobin (HbO2) and hemoglobin (Hb) [24]. Since venous blood has a different ratio of HbO2 and Hb compared to arterial blood and these chromophores have different absorption spectra, also the venous and arterial blood have a different absorption spectrum.
Fig. 3
Fig. 3 Overview of the proposed framework for robust respiration detection from remote PPG. 1) The manually initialized bounding-box indicating the face is tracked over time and divided into equally-sized subregions. 2) The weights for each (sub)region are calculated. From this collection of weights, the best are selected based on the SNR values of the pulse signals. 3) The extracted respiratory signal is scaled based on the ratio of respiratory and pulse energies.
Fig. 4
Fig. 4 Overview of the experimental setup used for the creation of the dataset.
Fig. 5
Fig. 5 Overview of breathing patterns for guided breathing scenarios in both visible light and infrared.
Fig. 6
Fig. 6 Results from the different breathing scenarios in visible light conditions for both proposed methods. The spectrograms are calculated with a window-size of 8 seconds.
Fig. 7
Fig. 7 Correlation and Bland-Altman plots for guided breathing: (a-b) in visible light, (c-d) in infrared. The black lines in the correlation plots indicate the linear relationship y=x.
Fig. 8
Fig. 8 Both proposed rPPG-based methods show large agreement with the reference ECG-derived respiratory signal. The snapshots on the right illustrate the four different viewpoint and distances included in the NICU dataset.
Fig. 9
Fig. 9 Correlation and Bland-Altman plots for spontaneous breathing. The black lines in the correlation plots indicate the linear relationship y=x.

Tables (2)

Tables Icon

Table 1 Results from the guided breathing scenarios in both visible and infrared lighting conditions.

Tables Icon

Table 2 Results from spontaneous breathing scenarios recorded in a NICU under visible light conditions.

Equations (11)

Equations on this page are rendered with MathJax. Learn more.

C = Δ V Δ P .
P P G ( w ) = ρ s ( w ) I ( w ) R P P G ( w )
C ¯ i t t + 1 = C ¯ i t + 1 C ¯ i t = ( R ¯ i t t + 1 G ¯ i t t + 1 B ¯ i t t + 1 ) = ( R i t + 1 ( x + d x , y + d y ) R i t ( x , y ) R i t + 1 ( x + d x , y + d y ) + R i t ( x , y ) G i t + 1 ( x + d x , y + d y ) G i t ( x , y ) G i t + 1 ( x + d x , y + d y ) + G i t ( x , y ) B i t + 1 ( x + d x , y + d y ) B i t ( x , y ) B i t + 1 ( x + d x , y + d y ) + B i t ( x , y ) ) ,
C n , i t t + l = ( R ¯ i t t + l G ¯ i t t + l B ¯ i t t + l ) = ( i = 0 l 1 R ¯ i t + i t + i + 1 i = 0 l 1 G ¯ i t + i t + i + 1 i = 0 l 1 B ¯ i t + i t + i + 1 ) .
S = W C n ,
W CHRO = 1 6 α 2 20 α + 20 [ 2 α , 2 α 4 , α ] .
α = σ ( X s ) σ ( Y s ) , with X s = [ + 0.77 , 0.51 , 0 ] C n and Y s = [ + 0.77 , + 0.51 , 0.77 ] C n
W PBV = k P bv Q 1 , with Q = C n C n T ,
P s e l e c t e d t t + l = P e i g e n t t + l , P m e a n t t + l | P e i g e n t t + l , P m e a n t t + l | × P e i g e n t t + l ,
SNR = 10 log 10 ( f = 40 240 ( U t ( f ) S f ( f ) ) 2 f = 40 240 ( 1 U t ( f ) S f ( f ) ) 2 ) ,
k = f = 10 40 S ( f ) f = P R m a r g i n P R + m a r g i n S ( f ) , with S f = ( S ) ,

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