Probabilistic Estimation of Respiratory Rate from Wearable Sensors

  • Marco A. F. Pimentel
  • Peter H. Charlton
  • David A. Clifton
Part of the Smart Sensors, Measurement and Instrumentation book series (SSMI, volume 15)


Respiration rate (RR) is a physiological parameter that is typically used in clinical settings for monitoring patient condition. Consequently, it is measured in a wide range of clinical scenarios, notably absent from which is measurement using wearable sensors. With increasing numbers of patients being monitored via wearable sensors, as described below, there is an urgent need to be able to estimate RR from such sensors in a robust manner. In this chapter, we describe a novel technique for measuring RR using waveform data acquired from wearable sensors.

The technique derives RR from a physiological signal which is routinely acquired by many mobile sensors: the photoplethysmogram (PPG). Each RR measurement from the proposed method is accompanied by a confidence measure, providing estimates of clinical quality that will allow the system to, for example, only report RR values when they exceed some probabilistic level of certainty. The goal of this method is to improve upon existing methods, which simply report RR values without probabilistic estimation, and which therefore suffer the lack of robustness that prevents their use in clinical practice.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Addison, P.S., Watson, J.N., Mestek, M.L., Mecca, R.S.: Developing an algorithm for pulse oximetry derived respiratory rate (RR(oxi)): a healthy volunteer study. Journal of Clinical Monitoring and Computing 26(1), 45–51 (2012)CrossRefGoogle Scholar
  2. 2.
    Addison, P.S., Watson, J.N., Mestek, M.L., Ochs, J.P., Uribe, A.A., Bergese, S.D.: Pulse oximetry-derived respiratory rate in general care floor patients. Journal of Clinical Monitoring and Computing 29(1), 113–120 (2015)CrossRefGoogle Scholar
  3. 3.
    Bailon, R., Sornmo, L., Laguna, P.: ECG-Derived Respiratory Frequency Estimation. In: Clifford, G.D., Azuaje, F., McSharry, P.E. (eds.) Advanced Methods and Tools for ECG Data Analysis, ch. 8, pp. 215–244. Artech House, London (2006)Google Scholar
  4. 4.
    Bates, A., Ling, M.J., Mann, J., Arvind, D.K.: Respiratory rate and flow waveform estimation from tri-axial accelerometer data. In: 2010 International Conference on Body Sensor Networks, pp. 144–150. IEEE, Singapore (2010)CrossRefGoogle Scholar
  5. 5.
    Cretikos, M.A., Bellomo, R., Hillman, K., Chen, J., Finfer, S., Flabouris, A.: Respiratory rate: the neglected vital sign. The Medical Journal of Australia 188(11), 657–659 (2008)Google Scholar
  6. 6.
    Farrohknia, N., Castrén, M., Ehrenberg, A., Lind, L., Oredsson, S., Jonsson, H., Asplund, K., Göransson, K.E.: Emergency department triage scales and their components: a systematic review of the scientific evidence. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 19(1), 42 (2011)CrossRefGoogle Scholar
  7. 7.
    Fleming, S.G., Tarassenko, L.: A comparison of signal processing techniques for the extraction of breathing rate from the photoplethysmogram. International Journal of Biological and Medical Sciences 2(4), 232–236 (2007)Google Scholar
  8. 8.
    Garde, A., Karlen, W., Ansermino, J.M., Dumont, G.A.: Estimating respiratory and heart rates from the correntropy spectral density of the photoplethysmogram. PloS One 9(1), e86427 (2014)CrossRefGoogle Scholar
  9. 9.
    Goldhaber, S.Z., Visani, L., De Rosa, M.: Acute pulmonary embolism: Clinical outcomes in the International Cooperative Pulmonary Embolism Registry (ICOPER). Lancet 353(2182), 1386–1389 (1999)CrossRefGoogle Scholar
  10. 10.
    Jin, A., Yin, B., Morren, G., Duric, H., Aarts, R.M.: Performance evaluation of a tri-axial accelerometry-based respiration monitoring for ambient assisted living. In: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5677–5680 (2009)Google Scholar
  11. 11.
    Karlen, W., Raman, S., Ansermino, J.M., Dumont, G.A.: Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Transactions on Biomedical Engineering 60(7), 1946–1953 (2013)CrossRefGoogle Scholar
  12. 12.
    Karlen, W., Turner, M., Cooke, E., Dumont, G., Ansermino, J.M.: Capnobase: Signal database and tools to collect, share and annotate respiratory signals. In: Annual Meeting of the Society for Technology in Anesthesia (STA), West Palm Beach (2010)Google Scholar
  13. 13.
    Khalil, A., Kelen, G., Rothman, R.E.: A simple screening tool for identification of community-acquired pneumonia in an inner city emergency department. Emergency Medicine Journal 24(5), 336–338 (2007)CrossRefGoogle Scholar
  14. 14.
    Knaus, W.A., Wagner, D.P., Draper, E.A., Zimmerman, J.E., Bergner, M., Bastos, P.G., Sirio, C.A., Murphy, D.J., Lotring, T., Damiano, A.: The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100(6), 1619–1636 (1991)CrossRefGoogle Scholar
  15. 15.
    Larsen, P.D., Tzeng, Y.C., Sin, P.Y.W., Galletly, D.C.: Respiratory sinus arrhythmia in conscious humans during spontaneous respiration. Respiratory Physiology & Neurobiology 174(1-2), 111–118 (2010)CrossRefGoogle Scholar
  16. 16.
    Li, B.N., Dong, M.C., Vai, M.I.: On an automatic delineator for arterial blood pressure waveforms. Biomedical Signal Processing and Control 5(1), 76–81 (2010)CrossRefGoogle Scholar
  17. 17.
    Li, J., Jin, J., Chen, X., Sun, W., Guo, P.: Comparison of respiratory-induced variations in photoplethysmographic signals. Physiological Measurement 31(3), 415 (2010)CrossRefGoogle Scholar
  18. 18.
    Li, Q., Clifford, G.D.: Dynamic time warping and machine learning for signal quality assessment of pulsatile signals. Physiological Measurement 33(9), 1491 (2012)CrossRefGoogle Scholar
  19. 19.
    Mackay, D.J.C.: Introduction to gaussian processes. In: NATO ASI Series F Computer and Systems Sciences, vol. 168, pp. 133–166 (1998)Google Scholar
  20. 20.
    Mason, L.: Signal Processing Methods for Non-Invasive Respiration Monitoring. PhD thesis, University of Oxford (2002)Google Scholar
  21. 21.
    Meredith, D.J., Clifton, D., Charlton, P., Brooks, J., Pugh, C.W., Tarassenko, L.: Photoplethysmographic derivation of respiratory rate: a review of relevant physiology. J. Med. Eng. Technol. 36(1), 1–7 (2012)CrossRefGoogle Scholar
  22. 22.
    Moll, J.M., Wright, V.: An objective clinical study of chest expansion. Annals of the Rheumatic Diseases 31(1), 1–8 (1972)CrossRefGoogle Scholar
  23. 23.
    Nilsson, L., Goscinski, T., Johansson, A., Lindberg, L.-G., Kalman, S.: Age and gender do not influence the ability to detect respiration by photoplethysmography. Journal of Clinical Monitoring and Computing 20(6), 431–436 (2006)CrossRefGoogle Scholar
  24. 24.
    O’Brien, I.A., O’Hare, P., Corrall, R.J.: Heart rate variability in healthy subjects: effect of age and the derivation of normal ranges for tests of autonomic function. British Heart Journal 57(1), 109–110 (1986)CrossRefGoogle Scholar
  25. 25.
    Orphanidou, C., Fleming, S., Shah, S.A., Tarassenko, L.: Data fusion for estimating respiratory rate from a single-lead ECG. Biomedical Signal Processing and Control 8(1), 98–105 (2013)CrossRefGoogle Scholar
  26. 26.
    Pan, J., Tompkins, W.J.: A real-time qrs detection algorithm. IEEE Transactions on Biomedical Engineering 32(3), 230–236 (1985)CrossRefGoogle Scholar
  27. 27.
    Pantelopoulos, A., Bourbakis, N.G.: Prognosis-a wearable health-monitoring system for people at risk: methodology and modeling. IEEE Transactions on Information Technology in Biomedicine 14(3), 613–621 (2010)CrossRefGoogle Scholar
  28. 28.
    Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press (2006)Google Scholar
  29. 29.
    Royal College of Physicians. National Early Warning Score (NEWS): Standardising the assessment of acute-illness severity in the NHS. Technical Report July, Report of a working party. RCP, London (2012)Google Scholar
  30. 30.
    Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 71(2), 319–392 (2009)CrossRefMATHMathSciNetGoogle Scholar
  31. 31.
    Saeed, M., Villarroel, M., Reisner, A.T., Clifford, G., Lehman, L., Moody, G., Heldt, T., Kyaw, T.H., Moody, B., Mark, R.G.: Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access intensive care unit database. Critical Care Medicine 39, 952–960 (2011)CrossRefGoogle Scholar
  32. 32.
    Shelley, K.H., Awad, A.A., Stout, R.G., Silverman, D.G.: The use of joint time frequency analysis to quantify the effect of ventilation on the pulse oximeter waveform. Journal of Clinical Monitoring and Computing 20(2), 81–87 (2006)CrossRefGoogle Scholar
  33. 33.
    Sivia, D.S., Skilling, J.: Data Analysis: A Bayesian Tutorial. Oxford University Press (2006)Google Scholar
  34. 34.
    The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Critical Care Medicine 20(6), 864–874 (1992)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marco A. F. Pimentel
    • 1
  • Peter H. Charlton
    • 1
    • 2
  • David A. Clifton
    • 1
  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of Oxford, Roosevelt DriveOxfordUK
  2. 2.Department of Biomedical EngineeringKing’s College LondonLondonUK

Personalised recommendations