A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram

Main Article Content

Lucas C. Lampier https://orcid.org/0000-0002-5141-9480
Yves L. Coelho https://orcid.org/0000-0001-8756-4316
Eliete M. O. Caldeira https://orcid.org/0000-0002-3742-0952
Teodiano F. Bastos-Filho https://orcid.org/0000-0002-1185-2773


This article describes the methodology used to train and test a Deep Neural Network (DNN) with Photoplethysmography (PPG) data performing a regression task to estimate the Respiratory Rate (RR). The DNN architecture is based on a model used to infer the heart rate (HR) from noisy PPG signals, which is optimized to the RR problem using genetic optimization. Two open-access datasets were used in the tests, the BIDMC and the CapnoBase. With the CapnoBase dataset, the DNN achieved a median error of 1.16 breaths/min, which is comparable with analytical methods in the literature, in which the best error found is 1.1 breaths/min (excluding the 8 % noisiest data). The BIDMC dataset seems to be more challenging, as the minimum median error of the literature’s methods is 2.3 breaths/min (excluding 6 % of the noisiest data), and the DNN based approach achieved a median error of 1.52 breaths/min with the whole dataset.
Abstract 154 | PDF (Español (España)) Downloads 59 PDF Downloads 44


[1] V. Ravichandran, B. Murugesan, V. Balakarthikeyan, K. Ram, S. P. Preejith, J. Joseph, and M. Sivaprakasam, “RespNet: A deep learning model for extraction of respiration from photoplethysmogram,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 5556–5559. [Online]. Available: https://doi.org/10.1109/EMBC.2019.8856301
[2] A. Floriano, L. Lampier, R. S. Rosa, E. Caldeira, and T. Bastos-Filho, “Remote vital sign monitoring in accidents,” Polytechnica, vol. 4, no. 1, pp. 26–32, Apr. 2021. [Online]. Available: https://doi.org/10.1007/s41050-020-00027-1
[3] D. J. Meredith, D. Clifton, P. Charlton, J. Brooks, C. W. Pugh, and L. Tarassenko, “Photoplethysmographic derivation of respiratory rate: a review of relevant physiology.” Journal of medical engineering & technology, vol. 36, no. 1, pp. 1–7, jan 2012. [Online]. Available: https://doi.org/10.3109/03091902.2011.638965
[4] M. A. Pimentel, A. E. Johnson, P. H. Charlton, D. Birrenkott, P. J. Watkinson, L. Tarassenko, and D. A. Clifton, “Toward a robust estimation of respiratory rate from pulse oximeters,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 8, pp. 1914–1923, 2017. [Online]. Available: https://doi.org/10.1109/TBME.2016.2613124
[5] S. G. F. L. Tarassenko, “A comparison of signal processing techniques for the extraction of breathing rate from the photoplethysmogram,” International Journal of Biological and Medical Sciences, vol. 1, no. 6, pp. 366–370, 2007. [Online]. Available: https://bit.ly/3Cprqqg
[6] W. Karlen, S. Raman, J. M. Ansermino, and G. A. Dumont, “Multiparameter respiratory rate estimation from the photoplethysmogram.” IEEE transactions on bio-medical engineering, vol. 60, no. 7, pp. 1946–1953, jul 2013. [Online]. Available: https://doi.org/10.1109/TBME.2013.2246160
[7] L. Nilsson, T. Goscinski, S. Kalman, L. G. Lindberg, and A. Johansson, “Combined photoplethysmographic monitoring of respiration rate and pulse: A comparison between different measurement sites in spontaneously breathing subjects,” Acta Anaesthesiologica Scandinavica, vol. 51, no. 9, pp. 1250–1257, 2007. [Online]. Available: https://doi.org/10.1111/j.1399-6576.2007.01375.x
[8] K. H. Shelley, A. A. Awad, R. G. Stout, and D. G. Silverman, “The use of joint time frequency analysis to quantify the effect of ventilation on the pulse oximeter waveform.” Journal of clinical monitoring and computing, vol. 20, no. 2, pp. 81–87, apr 2006. [Online]. Available: https://doi.org/10.1007/s10877-006-9010-7
[9] D. Biswas, L. Everson, M. Liu, M. Panwar, B.-E. Verhoef, S. Patki, C. H. Kim, A. Acharyya, C. Van Hoof, M. Konijnenburg, and N. Van Helleputte, “Cornet: Deep learning framework for ppg-based heart rate estimation and biometric identification in ambulant environment,” IEEE Transactions on Biomedical Circuits and Systems, vol. 13, no. 2, pp. 282–291, 2019. [Online]. Available: https://doi.org/10.1109/TBCAS.2019.2892297
[10] P. H. Charlton, D. A. Birrenkott, T. Bonnici, M. A. F. Pimentel, A. E. W. Johnson, J. Alastruey, L. Tarassenko, P. J. Watkinson, R. Beale, and D. A. Clifton, “Breathing rate estimation from the electrocardiogram and photoplethysmogram: A review,” IEEE Reviews in Biomedical Engineering, vol. 11, pp. 2–20, 2018. [Online]. Available: https://doi.org/10.1109/RBME.2017.2763681
[11] L. G. Lindberg, H. Ugnell, and P. A. Oberg, “Monitoring of respiratory and heart rates using a fibre-optic sensor,” Medical & Biological Engineering & Computing, vol. 30, no. 5, pp. 533–537, 1992. [Online]. Available: https://doi.org/10.1007/BF02457833
[12] K. V. Madhav, M. R. Ram, E. H. Krishna, K. N. Reddy, and K. A. Reddy, “Estimation of respiratory rate from principal components of photoplethysmographic signals,” Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010, no. December, pp. 311–314, 2010. [Online]. Available: https://doi.org/10.1109/IECBES.2010.5742251
[13] Y. Nam, Y. Kong, B. Reyes, N. Reljin, and K. H. Chon, “Monitoring of heart and breathing rates using dual cameras on a smartphone,” PLoS ONE, vol. 11, no. 3, mar 2016. [Online]. Available: https://doi.org/10.1371/journal.pone.0151013
[14] L. Nilsson, A. Johansson, and S. Kalman, “Monitoring of respiratory rate in postoperative care using a new photoplethysmographic technique,” Journal of Clinical Monitoring and Computing, vol. 16, no. 4, pp. 309–315, 2000. [Online]. Available: https://doi.org/10.1023/A:1011424732717
[15] W. Karlen, “CapnoBase IEEE TBME Respiratory Rate Benchmark,” 2021. [Online]. Available: https://doi.org/10.5683/SP2/NLB8IT
[16] S. Khreis, D. Ge, H. A. Rahman, and G. Carrault, “Breathing Rate Estimation Using Kalman Smoother with Electrocardiogram and Photoplethysmogram,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 3, pp. 893–904, 2020. [Online]. Available: https://doi.org/10.1109/TBME.2019.2923448
[17] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016, [Online]. Available: https://bit.ly/3Eh4Twb
[18] 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.” Physiological measurement, vol. 37, no. 4, pp. 610–626, apr 2016. [Online]. Available: https://doi.org/10.1088/0967-3334/37/4/610
[19] A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.” Circulation, vol. 101, no. 23, pp. e215–e220, jun 2000. [Online]. Available: https://doi.org/10.1161/01.cir.101.23.e215
[20] CapnoBase. (2020) Capnobase is a collaborative research project that provides an online database of respiratory signals and labels obtained from capnography, spirometry and pulse oximetry. [Online]. Available: https://bit.ly/3EjfHKm
[21] M. A. F. Pimentel, A. E. W. Johnson, P. H. Charlton, D. Birrenkott, P. J.Watkinson, L. Tarassenko, and D. A. Clifton, “Toward a robust estimation of respiratory rate from pulse oximeters,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 8, pp. 1914–1923, 2017. [Online]. Available: https://doi.org/10.1109/TBME.2016.2613124
[22] D. Makowski, T. Pham, Z. J. Lau, J. C. Brammer, F. Lespinasse, H. Pham, C. Schölzel, and S. H. A. Chen, “Neurokit2: A python toolbox for neurophysiological signal processing,” Behavior Research Methods, vol. 53, no. 4, pp. 1689–1696, Feb 2021. [Online]. Available: https://doi.org/10.3758/s13428-020-01516-y
[23] M. Elgendi, I. Norton, M. Brearley, D. Abbott, and D. Schuurmans, “Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions,” PLOS ONE, vol. 8, no. 10, pp. 1–11, 10 2013. [Online]. Available: https://doi.org/10.1371/journal.pone.0076585
[24] P. van Gent, H. Farah, N. van Nes, and B. van Arem, “Heartpy: A novel heart rate algorithm for the analysis of noisy signals,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 66, pp. 368–378, 2019. [Online]. Available: https://doi.org/10.1016/j.trf.2019.09.015
[25] F. Chollet, “Keras: Deep learning for humans,” GitHub. Inc, 2015. [Online]. Available: https://bit.ly/3dA0g57