Abstract

Laboratory animal research was always crucial for scientific breakthroughs in the fields of medicine and biology. Animal trials offer insights into various disease mechanisms, genetics, drug therapy and the effect of different external factors onto living organisms. However, conducting animal trials is highly controversial. To ensure high ethical standards, a number of directives have been adopted in the European Union, which seek to replace, reduce and refine animal trials. Hence, severity assessment plays an important role in today’s laboratory animal research. Currently, severity of trials is assessed by highly rater dependent scoring systems. In this paper, we propose a method for unobtrusive, automated and contactless measurement of respiratory rate (RR) and heart rate (HR). We were able to extract RR and HR with an high agreement between our method and a contact-based reference method. The Root Mean Squared Error (RMSE) averaged 0.32 ± 0.11 breaths/min for RR and 1.28 ± 0.62 beats/min for HR in rats, respectively. In mice, the RMSE averaged 1.42 ± 0.97 breaths/min for RR and 1.36 ± 0.87 beats/min, respectively. In the future, these parameters can be used for new, objective scoring systems, which are not susceptible to inter-rater variability

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2018 (5)

C. Häger, L. M. Keubler, S. R. Talbot, S. Biernot, N. Weegh, S. Buchheister, M. Buettner, S. Glage, and A. Bleich, “Running in the wheel: Defining individual severity levels in mice,” PLoS Biol. 16(10), e2006159 (2018).
[Crossref]

C. Cinque, M. Zinni, A. R. Zuena, C. Giuli, S. G. Alema, A. Catalani, P. Casolini, and R. Cozzolino, “Faecal corticosterone metabolite assessment in socially housed male and female Wistar rats,” Endocr. Connect. 7(2), 250–257 (2018).
[Crossref]

C. Pereira, J. Kunczik, L. Zieglowski, R. Tolba, A. Abdelrahman, D. Zechner, B. Vollmar, H. Janssen, T. Thum, and M. Czaplik, “Remote Welfare Monitoring of Rodents Using Thermal Imaging,” Sensors 18(11), 3653 (2018).
[Crossref]

K. Mutlu, J. E. Rabell, P. Martin del Olmo, and S. Haesler, “IR thermography-based monitoring of respiration phase without image segmentation,” J. Neurosci. Methods 301, 1–8 (2018).
[Crossref]

B. G. Vainer, “A Novel High-Resolution Method for the Respiration Rate and Breathing Waveforms Remote Monitoring,” Ann. Biomed. Eng. 46(7), 960–971 (2018).
[Crossref]

2017 (2)

A. Bleich and R. H. Tolba, “How can we assess their suffering? german research consortium aims at defining a severity assessment framework for laboratory animals,” Lab. Anim. 51(6), 667 (2017).
[Crossref]

M. R. Fernandes and A. R. Pedroso, “Animal experimentation: A look into ethics, welfare and alternative methods,” Rev. Assoc. Med. Bras. 63(11), 923–928 (2017).
[Crossref]

2016 (3)

C. González-Sánchez, J.-C. Fraile, J. Pérez-Turiel, E. Damm, J. Schneider, H. Zimmermann, D. Schmitt, and F. Ihmig, “Capacitive Sensing for Non-Invasive Breathing and Heart Monitoring in Non-Restrained, Non-Sedated Laboratory Mice,” Sensors 16(7), 1052 (2016).
[Crossref]

Y. Sun and N. Thakor, “Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging,” IEEE Trans. Biomed. Eng. 63(3), 463–477 (2016).
[Crossref]

J. E. Niemeyer, “Telemetry for small animal physiology,” Lab Anim 45(7), 255–257 (2016).
[Crossref]

2015 (1)

2014 (1)

P. Roelfsema and S. Treue, “Basic Neuroscience Research with Nonhuman Primates: A Small but Indispensable Component of Biomedical Research,” Neuron 82(6), 1200–1204 (2014).
[Crossref]

2013 (3)

E. A. Nunamaker, J. E. Artwohl, R. J. Anderson, and J. D. Fortman, “Endpoint refinement for total body irradiation of C57bl/6 mice,” Comp. Med. 63, 22–28 (2013).

E. O. Kehinde, “They See a Rat, We Seek a Cure for Diseases: The Current Status of Animal Experimentation in Medical Practice,” Med. Princ. Pract. 22(s1), 52–61 (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]

2012 (2)

B. G. Helwig, J. A. Ward, M. D. Blaha, and L. R. Leon, “Effect of intraperitoneal radiotelemetry instrumentation on voluntary wheel running and surgical recovery in mice,” J. Am. Assoc. for Lab. Animal Sci. JAALAS 51, 600–608 (2012).

N. Levy, “The Use of Animal as Models: Ethical Considerations,” Int. J. Stroke 7(5), 440–442 (2012).
[Crossref]

2011 (2)

R. Hajar, “Animal testing and medicine,” Hear. Views 12(1), 42 (2011).
[Crossref]

N. Cesarovic, P. Jirkof, A. Rettich, and M. Arras, “Implantation of Radiotelemetry Transmitters Yielding Data on ECG, Heart Rate, Core Body Temperature and Activity in Free-moving Laboratory Mice,” J. Visualized Exp. 57, e3260 (2011).
[Crossref]

2010 (1)

D. J. Langford, A. L. Bailey, M. L. Chanda, S. E. Clarke, T. E. Drummond, S. Echols, S. Glick, J. Ingrao, T. Klassen-Ross, M. L. LaCroix-Fralish, L. Matsumiya, R. E. Sorge, S. G. Sotocinal, J. M. Tabaka, D. Wong, A. M. J. M. van den Maagdenberg, M. D. Ferrari, K. D. Craig, and J. S. Mogil, “Coding of facial expressions of pain in the laboratory mouse,” Nat. Methods 7(6), 447–449 (2010).
[Crossref]

2007 (1)

S. Festing and R. Wilkinson, “The ethics of animal research. Talking Point on the use of animals in scientific research,” EMBO Rep. 8(6), 526–530 (2007).
[Crossref]

2006 (1)

S. Frantz, K. Hu, B. Bayer, S. Gerondakis, J. Strotmann, A. Adamek, G. Ertl, and J. Bauersachs, “Absence of NF-$\kappa$κb subunit p50 improves heart failure after myocardial infarction,” FASEB J. 20(11), 1918–1920 (2006).
[Crossref]

1981 (1)

M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
[Crossref]

Abdelrahman, A.

C. Pereira, J. Kunczik, L. Zieglowski, R. Tolba, A. Abdelrahman, D. Zechner, B. Vollmar, H. Janssen, T. Thum, and M. Czaplik, “Remote Welfare Monitoring of Rodents Using Thermal Imaging,” Sensors 18(11), 3653 (2018).
[Crossref]

Adamek, A.

S. Frantz, K. Hu, B. Bayer, S. Gerondakis, J. Strotmann, A. Adamek, G. Ertl, and J. Bauersachs, “Absence of NF-$\kappa$κb subunit p50 improves heart failure after myocardial infarction,” FASEB J. 20(11), 1918–1920 (2006).
[Crossref]

Alema, S. G.

C. Cinque, M. Zinni, A. R. Zuena, C. Giuli, S. G. Alema, A. Catalani, P. Casolini, and R. Cozzolino, “Faecal corticosterone metabolite assessment in socially housed male and female Wistar rats,” Endocr. Connect. 7(2), 250–257 (2018).
[Crossref]

Anderson, R. J.

E. A. Nunamaker, J. E. Artwohl, R. J. Anderson, and J. D. Fortman, “Endpoint refinement for total body irradiation of C57bl/6 mice,” Comp. Med. 63, 22–28 (2013).

Arras, M.

N. Cesarovic, P. Jirkof, A. Rettich, and M. Arras, “Implantation of Radiotelemetry Transmitters Yielding Data on ECG, Heart Rate, Core Body Temperature and Activity in Free-moving Laboratory Mice,” J. Visualized Exp. 57, e3260 (2011).
[Crossref]

Artwohl, J. E.

E. A. Nunamaker, J. E. Artwohl, R. J. Anderson, and J. D. Fortman, “Endpoint refinement for total body irradiation of C57bl/6 mice,” Comp. Med. 63, 22–28 (2013).

Bailey, A. L.

D. J. Langford, A. L. Bailey, M. L. Chanda, S. E. Clarke, T. E. Drummond, S. Echols, S. Glick, J. Ingrao, T. Klassen-Ross, M. L. LaCroix-Fralish, L. Matsumiya, R. E. Sorge, S. G. Sotocinal, J. M. Tabaka, D. Wong, A. M. J. M. van den Maagdenberg, M. D. Ferrari, K. D. Craig, and J. S. Mogil, “Coding of facial expressions of pain in the laboratory mouse,” Nat. Methods 7(6), 447–449 (2010).
[Crossref]

Balakrishnan, G.

G. Balakrishnan, F. Durand, and J. Guttag, “Detecting pulse from head motions in video,”, in 2013 IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2013).

Bauersachs, J.

S. Frantz, K. Hu, B. Bayer, S. Gerondakis, J. Strotmann, A. Adamek, G. Ertl, and J. Bauersachs, “Absence of NF-$\kappa$κb subunit p50 improves heart failure after myocardial infarction,” FASEB J. 20(11), 1918–1920 (2006).
[Crossref]

Bayer, B.

S. Frantz, K. Hu, B. Bayer, S. Gerondakis, J. Strotmann, A. Adamek, G. Ertl, and J. Bauersachs, “Absence of NF-$\kappa$κb subunit p50 improves heart failure after myocardial infarction,” FASEB J. 20(11), 1918–1920 (2006).
[Crossref]

Biernot, S.

C. Häger, L. M. Keubler, S. R. Talbot, S. Biernot, N. Weegh, S. Buchheister, M. Buettner, S. Glage, and A. Bleich, “Running in the wheel: Defining individual severity levels in mice,” PLoS Biol. 16(10), e2006159 (2018).
[Crossref]

Blackford, E. B.

D. J. McDuff, J. R. Estepp, A. M. Piasecki, and E. B. Blackford, A survey of remote optical photoplethysmographic imaging methods, in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (IEEE, Milan, 2015), pp. 6398–6404.

Blaha, M. D.

B. G. Helwig, J. A. Ward, M. D. Blaha, and L. R. Leon, “Effect of intraperitoneal radiotelemetry instrumentation on voluntary wheel running and surgical recovery in mice,” J. Am. Assoc. for Lab. Animal Sci. JAALAS 51, 600–608 (2012).

Bleich, A.

C. Häger, L. M. Keubler, S. R. Talbot, S. Biernot, N. Weegh, S. Buchheister, M. Buettner, S. Glage, and A. Bleich, “Running in the wheel: Defining individual severity levels in mice,” PLoS Biol. 16(10), e2006159 (2018).
[Crossref]

A. Bleich and R. H. Tolba, “How can we assess their suffering? german research consortium aims at defining a severity assessment framework for laboratory animals,” Lab. Anim. 51(6), 667 (2017).
[Crossref]

Bolles, R. C.

M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
[Crossref]

Buchheister, S.

C. Häger, L. M. Keubler, S. R. Talbot, S. Biernot, N. Weegh, S. Buchheister, M. Buettner, S. Glage, and A. Bleich, “Running in the wheel: Defining individual severity levels in mice,” PLoS Biol. 16(10), e2006159 (2018).
[Crossref]

Buettner, M.

C. Häger, L. M. Keubler, S. R. Talbot, S. Biernot, N. Weegh, S. Buchheister, M. Buettner, S. Glage, and A. Bleich, “Running in the wheel: Defining individual severity levels in mice,” PLoS Biol. 16(10), e2006159 (2018).
[Crossref]

Burch, R.

W. Russel and R. Burch, The principle of humane experimental technique (Methuen, London, 1959).

Casolini, P.

C. Cinque, M. Zinni, A. R. Zuena, C. Giuli, S. G. Alema, A. Catalani, P. Casolini, and R. Cozzolino, “Faecal corticosterone metabolite assessment in socially housed male and female Wistar rats,” Endocr. Connect. 7(2), 250–257 (2018).
[Crossref]

Catalani, A.

C. Cinque, M. Zinni, A. R. Zuena, C. Giuli, S. G. Alema, A. Catalani, P. Casolini, and R. Cozzolino, “Faecal corticosterone metabolite assessment in socially housed male and female Wistar rats,” Endocr. Connect. 7(2), 250–257 (2018).
[Crossref]

Cesarovic, N.

N. Cesarovic, P. Jirkof, A. Rettich, and M. Arras, “Implantation of Radiotelemetry Transmitters Yielding Data on ECG, Heart Rate, Core Body Temperature and Activity in Free-moving Laboratory Mice,” J. Visualized Exp. 57, e3260 (2011).
[Crossref]

Chanda, M. L.

D. J. Langford, A. L. Bailey, M. L. Chanda, S. E. Clarke, T. E. Drummond, S. Echols, S. Glick, J. Ingrao, T. Klassen-Ross, M. L. LaCroix-Fralish, L. Matsumiya, R. E. Sorge, S. G. Sotocinal, J. M. Tabaka, D. Wong, A. M. J. M. van den Maagdenberg, M. D. Ferrari, K. D. Craig, and J. S. Mogil, “Coding of facial expressions of pain in the laboratory mouse,” Nat. Methods 7(6), 447–449 (2010).
[Crossref]

Cinque, C.

C. Cinque, M. Zinni, A. R. Zuena, C. Giuli, S. G. Alema, A. Catalani, P. Casolini, and R. Cozzolino, “Faecal corticosterone metabolite assessment in socially housed male and female Wistar rats,” Endocr. Connect. 7(2), 250–257 (2018).
[Crossref]

Clarke, S. E.

D. J. Langford, A. L. Bailey, M. L. Chanda, S. E. Clarke, T. E. Drummond, S. Echols, S. Glick, J. Ingrao, T. Klassen-Ross, M. L. LaCroix-Fralish, L. Matsumiya, R. E. Sorge, S. G. Sotocinal, J. M. Tabaka, D. Wong, A. M. J. M. van den Maagdenberg, M. D. Ferrari, K. D. Craig, and J. S. Mogil, “Coding of facial expressions of pain in the laboratory mouse,” Nat. Methods 7(6), 447–449 (2010).
[Crossref]

Clifford, G. D.

P. E. McSharry and G. D. Clifford, “A comparison of nonlinear noise reduction and independent component analysis using a realistic dynamical model of the electrocardiogram,” in Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems II, D. Abbott, S. M. Bezrukov, A. Der, and A. Sanchez, eds. (SPIE, 2004).

Cozzolino, R.

C. Cinque, M. Zinni, A. R. Zuena, C. Giuli, S. G. Alema, A. Catalani, P. Casolini, and R. Cozzolino, “Faecal corticosterone metabolite assessment in socially housed male and female Wistar rats,” Endocr. Connect. 7(2), 250–257 (2018).
[Crossref]

Craig, K. D.

D. J. Langford, A. L. Bailey, M. L. Chanda, S. E. Clarke, T. E. Drummond, S. Echols, S. Glick, J. Ingrao, T. Klassen-Ross, M. L. LaCroix-Fralish, L. Matsumiya, R. E. Sorge, S. G. Sotocinal, J. M. Tabaka, D. Wong, A. M. J. M. van den Maagdenberg, M. D. Ferrari, K. D. Craig, and J. S. Mogil, “Coding of facial expressions of pain in the laboratory mouse,” Nat. Methods 7(6), 447–449 (2010).
[Crossref]

Czaplik, M.

C. Pereira, J. Kunczik, L. Zieglowski, R. Tolba, A. Abdelrahman, D. Zechner, B. Vollmar, H. Janssen, T. Thum, and M. Czaplik, “Remote Welfare Monitoring of Rodents Using Thermal Imaging,” Sensors 18(11), 3653 (2018).
[Crossref]

Damm, E.

C. González-Sánchez, J.-C. Fraile, J. Pérez-Turiel, E. Damm, J. Schneider, H. Zimmermann, D. Schmitt, and F. Ihmig, “Capacitive Sensing for Non-Invasive Breathing and Heart Monitoring in Non-Restrained, Non-Sedated Laboratory Mice,” Sensors 16(7), 1052 (2016).
[Crossref]

Drummond, T. E.

D. J. Langford, A. L. Bailey, M. L. Chanda, S. E. Clarke, T. E. Drummond, S. Echols, S. Glick, J. Ingrao, T. Klassen-Ross, M. L. LaCroix-Fralish, L. Matsumiya, R. E. Sorge, S. G. Sotocinal, J. M. Tabaka, D. Wong, A. M. J. M. van den Maagdenberg, M. D. Ferrari, K. D. Craig, and J. S. Mogil, “Coding of facial expressions of pain in the laboratory mouse,” Nat. Methods 7(6), 447–449 (2010).
[Crossref]

Durand, F.

G. Balakrishnan, F. Durand, and J. Guttag, “Detecting pulse from head motions in video,”, in 2013 IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2013).

Echols, S.

D. J. Langford, A. L. Bailey, M. L. Chanda, S. E. Clarke, T. E. Drummond, S. Echols, S. Glick, J. Ingrao, T. Klassen-Ross, M. L. LaCroix-Fralish, L. Matsumiya, R. E. Sorge, S. G. Sotocinal, J. M. Tabaka, D. Wong, A. M. J. M. van den Maagdenberg, M. D. Ferrari, K. D. Craig, and J. S. Mogil, “Coding of facial expressions of pain in the laboratory mouse,” Nat. Methods 7(6), 447–449 (2010).
[Crossref]

Ertl, G.

S. Frantz, K. Hu, B. Bayer, S. Gerondakis, J. Strotmann, A. Adamek, G. Ertl, and J. Bauersachs, “Absence of NF-$\kappa$κb subunit p50 improves heart failure after myocardial infarction,” FASEB J. 20(11), 1918–1920 (2006).
[Crossref]

Estepp, J. R.

D. J. McDuff, J. R. Estepp, A. M. Piasecki, and E. B. Blackford, A survey of remote optical photoplethysmographic imaging methods, in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (IEEE, Milan, 2015), pp. 6398–6404.

Fernandes, M. R.

M. R. Fernandes and A. R. Pedroso, “Animal experimentation: A look into ethics, welfare and alternative methods,” Rev. Assoc. Med. Bras. 63(11), 923–928 (2017).
[Crossref]

Ferrari, M. D.

D. J. Langford, A. L. Bailey, M. L. Chanda, S. E. Clarke, T. E. Drummond, S. Echols, S. Glick, J. Ingrao, T. Klassen-Ross, M. L. LaCroix-Fralish, L. Matsumiya, R. E. Sorge, S. G. Sotocinal, J. M. Tabaka, D. Wong, A. M. J. M. van den Maagdenberg, M. D. Ferrari, K. D. Craig, and J. S. Mogil, “Coding of facial expressions of pain in the laboratory mouse,” Nat. Methods 7(6), 447–449 (2010).
[Crossref]

Festing, S.

S. Festing and R. Wilkinson, “The ethics of animal research. Talking Point on the use of animals in scientific research,” EMBO Rep. 8(6), 526–530 (2007).
[Crossref]

Fischler, M. A.

M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
[Crossref]

Fortman, J. D.

E. A. Nunamaker, J. E. Artwohl, R. J. Anderson, and J. D. Fortman, “Endpoint refinement for total body irradiation of C57bl/6 mice,” Comp. Med. 63, 22–28 (2013).

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Supplementary Material (1)

NameDescription
» Visualization 1       Performance of the signal fusion algorithm on real video data. Positions of distinctive feature points are tracked over time, using the KLT algorithm. The positional (X,Y) signals are projected onto the direction of their largest movement, using a pr

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Figures (12)

Fig. 1.
Fig. 1. Graphical representation of the measurement setup
Fig. 2.
Fig. 2. Probability density function (PDF) for the set of maximum spectral power of each pixel $P_{\textrm {max}}$ (blue bar graph). An inverse gamma distribution (red line graph), fitted to the set, is used to compute its mean value $\mu _P$ and standard deviation $\sigma _P$. The sum of both moments represents the spectral noise floor $N$ (dashed line).
Fig. 3.
Fig. 3. Results of the pulsatility analysis overlayed onto grayscale image of animal for better visual reference (M3)
Fig. 4.
Fig. 4. Schematic overview of the signal fusion algorithm (Visualization 1)
Fig. 5.
Fig. 5. Signal spectrum contaminated by harmonics of respiratory frequency
Fig. 6.
Fig. 6. Spike removal from $S$
Fig. 7.
Fig. 7. After removing the breath related spikes, the signal spectrogram unveils the HR
Fig. 8.
Fig. 8. Results of the animal detection, using the pulsatility analysis for selected animals. Animals in top row are mice. The bottom row contains rats.
Fig. 9.
Fig. 9. Exemplary Respiratory Rate (RR) tracking comparison between the presented algorithm and the reference signal
Fig. 10.
Fig. 10. Statistical analysis between extracted and reference respiration rate. The plots comprise the data of all rats.
Fig. 11.
Fig. 11. Exemplary Heart Rate (HR) tracking comparison between the presented algorithm and the reference signal
Fig. 12.
Fig. 12. Statistical analysis between extracted and reference heart rate. The plots comprise the data of all rats.

Tables (5)

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Table 1. Summarized data set description.

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Table 2. Results for respiratory rate estimation in videos from rats.

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Table 3. Results for respiratory rate estimation in videos from mice.

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Table 4. Results for heart rate estimation in videos from rats.

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Table 5. Results for heart rate estimation in videos from mice.

Equations (7)

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P max , i , j = max f ( P i , j ( f ) ) s . t . : f [ f B,min , f B,max ] .
N = ( μ P + σ P ) ( f B,min f B,max ) ,
S N R i , j = f max ϵ f max + ϵ P i , j ( f ) d f N .
R R raw [ k ] = 60 T k T k 1
R R clean = [ R R raw [ k ] | R R raw ( k ) [ R R min , R R max ] | Δ R R raw [ k ] Δ T ( k ) | < Δ R R max ]
W a b s , k [ j ] = | S [ j ] S ~ [ j ] | j ( T k , T k 1 ] F s .
W m a g , k [ j ] = W a b s , k [ j ] min ( W a b s , k ) max ( W a b s , k ) min ( W a b s , k ) .