Skip to main content
SpringerLink
Log in
Book cover

International Conference on Nonlinear Speech Processing

NOLISP 2013: Advances in Nonlinear Speech Processing pp 128–135Cite as

Towards a Low-Complex Breathing Monitoring System Based on Acoustic Signals

  • Conference paper

  • 1067 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7911))

Abstract

Monitoring the breathing is required in many applications of medical and health fields, but it can be used also in new game applications, for example. In this work, an automatic system for monitoring the breathing is presented. The system uses the acoustic signal recorded by a standard microphone placed in the area of the nostrils. The system is based on a low-complex signal parameterization performed on non-overlapped frames. From this parameterization, a reduced set of real parameters is obtained frame-to-frame. These parameters feed a classifier that performs a classification in three stages: inspiration, transition or retention and expiration providing a fine monitoring of the respiration process. As all of those algorithms are of low complexity and the auxiliary equipment required could only be a standard microphone from a conventional Bluetooth Headset, the system could be able to run in a smartphone device.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   72.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Pranayama -yoga breathing, https://play.google.com/store/apps/details?id=pranayama.home

  2. Breath health tester pro, https://play.google.com/store/apps/details?id=org.app.breath_rate

  3. Relax: Stress anxiety relief, https://play.google.com/store/apps/details?id=com.saagara.relax

  4. Breathng for life, https://play.google.com/store/apps/details?id=com.soniq.breathingforlife1

  5. Badra, L.J., Cooke, W.H., Hoag, J.B., et al.: Respiratory modulation of human autonomic rhythms. American J. of Physiology-Heart and Circulatory Physiology 280(6), H2674–H2688 (2001)

    Google Scholar 

  6. Cacioppo, J.T., Berntson, G.G., Larsen, J.T., Poehlmann, K.M., Ito, T.: The psychophysiology of emotion. Handbook of Emotions 2, 173–191 (2000)

    Google Scholar 

  7. Corbishley, P., Rodriguez-Villegas, E.: A nanopower bandpass filter for detection of an acoustic signal in a wearable breathing detector. IEEE Transactions on Biomedical Circuits and Systems 1(3), 163–171 (2007)

    Article  Google Scholar 

  8. Corbishley, P., Rodríguez-Villegas, E.: Breathing detection: Towards a miniaturized, wearable, battery-operated monitoring system. IEEE Transactions on Biomedical Engineering 55(1), 196–204 (2008)

    Article  Google Scholar 

  9. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification and scene analysis, 2nd edn. (1995)

    Google Scholar 

  10. Ferreira, J.B., Plentz, R.D.M., Stein, C., Casali, K.R., Arena, R., Lago, P.D.: Inspiratory muscle training reduces blood pressure and sympathetic activity in hypertensive patients: A randomized controlled trial. Int. J. Cardiol. (2011)

    Google Scholar 

  11. Folke, M., Cernerud, L., Ekström, M., Hök, B.: Critical review of non-invasive respiratory monitoring in medical care. Medical and Biological Engineering and Computing 41(4), 377–383 (2003)

    Article  Google Scholar 

  12. Goldman, L.J.: Nasal airflow and thoracoabdominal motion in children using infrared thermographic video processing. Pediatr. Pulmonol. (2012)

    Google Scholar 

  13. Haag, A., Goronzy, S., Schaich, P., Williams, J.: Emotion recognition using bio-sensors: First steps towards an automatic system. Affective Dialogue Systems, 36–48 (2004)

    Google Scholar 

  14. Hanawa, D., Morimoto, T., Tearda, S., et al.: Nasal cavity detection in facial thermal image for non-contact measurement of breathing, 586–590 (2012)

    Google Scholar 

  15. Harper, V.P., Pasterkamp, H., Kiyokawa, H., Wodicka, G.R.: Modeling and measurement of flow effects on tracheal sounds. IEEE Trans. on Biomedical Engineering 50(1), 1–10 (2003)

    Article  Google Scholar 

  16. Hossain, I., Moussavi, Z.: Relationship between airflow and normal lung sounds 2, 1120–1122 (2002)

    Google Scholar 

  17. Hossain, I., Moussavi, Z.: Respiratory airflow estimation by acoustical means 2, 1476–1477 (2002)

    Google Scholar 

  18. Fukunaga, K.: Introduction to statistical pattern recognition. Academic Pr. (1990)

    Google Scholar 

  19. Koide, T., Yamakawa, S., Hanawa, D., Oguchi, K.: Breathing detection by far infrared (FIR) imaging in a home health care system, 206 (2009)

    Google Scholar 

  20. Linden, W., Moseley, J.: The efficacy of behavioral treatments for hypertension. Appl. Psychophysiol Biofeedback 31(1), 51–63 (2006)

    Article  Google Scholar 

  21. McGrady, A.: The effects of biofeedback in diabetes and essential hypertension. Cleve Clin. J. Med. 77(suppl. 3), S68–S71 (2010)

    Google Scholar 

  22. Mikosch, P., Hadrawa, T., Laubreiter, K., et al.: Effectiveness of respiratory-sinus-arrhythmia biofeedback on state-anxiety in patients undergoing coronary angiography. J. Adv. Nurs. 66(5), 1101–1110 (2010)

    Article  Google Scholar 

  23. Moore, S.: Tools & toys: Calm in your palm. IEEE Spectrum 43(3), 60 (2006)

    Article  Google Scholar 

  24. Mourya, M., Mahajan, A.S., Singh, N.P., Jain, A.K.: Effect of slow-and fast-breathing exercises on autonomic functions in patients with essential hypertension. The Journal of Alternative and Complementary Medicine 15(7), 711–717 (2009)

    Article  Google Scholar 

  25. Pokrovskii, V.M., Polischuk, L.: On the conscious control of the human heart. Journal of Integrative Neuroscience 11(2), 213–223 (2012)

    Article  Google Scholar 

  26. Rafferty, G.F., Gardner, W.: Control of the respiratory cycle in conscious humans. J. Appl. Physiol. 81(4), 1744–1753 (1996)

    Google Scholar 

  27. Rainville, P., Bechara, A., Naqvi, N., Damasio, A.: Basic emotions are associated with distinct patterns of cardiorespiratory activity. International Journal of Psychophysiology 61(1), 5–18 (2006)

    Article  Google Scholar 

  28. Sharma, M.: RESPeRATE: Nonpharmacological treatment of hypertension. Cardiol. Rev. 19(2), 47–51 (2011)

    Article  Google Scholar 

  29. Shykoff, B.E., Ploysongsang, Y., Chang, H.: Airflow and normal lung sounds. American Journal of Respiratory and Critical Care Medicine 137(4), 872–876 (1988)

    Article  Google Scholar 

  30. Stock, M., Kontrisova, K., Dieckmann, K., Bogner, J., Poetter, R., Georg, D.: Development and application of a real-time monitoring and feedback system for deep inspiration breath hold based on external marker tracking. Med. Phys. 33(8), 2868–2877 (2006)

    Article  Google Scholar 

  31. Tsai, P., Chang, N., Chang, W., Lee, P., Wang, M.: Blood pressure biofeedback exerts intermediate-term effects on blood pressure and pressure reactivity in individuals with mild hypertension: A randomized controlled study. The J. of Alternative and Complementary Medicine 13(5), 547–554 (2007)

    Article  Google Scholar 

  32. Van Gestel, A.J.R., Kohler, M., Steier, J., Teschler, S., Russi, E.W., Teschler, H.: The effects of controlled breathing during pulmonary rehabilitation in patients with COPD. Respiration 83(2), 115–124 (2012)

    Article  Google Scholar 

  33. Yap, Y.L., Moussavi, Z.: Acoustic airflow estimation from tracheal sound power 2, 1073–1076 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Digital Technologies Group, University of Vic, Sagrada Família 7, 08500, Vic, Catalonia, Spain

    Pere Martíć-Puig, Jordi Solé-Casals, Gerard Masferrer & Esteve Gallego-Jutglà

Authors
  1. Pere Martíć-Puig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jordi Solé-Casals
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gerard Masferrer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Esteve Gallego-Jutglà
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. TCTS Lab, University of Mons, 31, Bouldevard Bolez, 7000, Mons, Belgium

    Thomas Drugman

  2. TCTS Lab, University of Mons, 31, Boulevard Dolez, 7000, Mons, Belgium

    Thierry Dutoit

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Martíć-Puig, P., Solé-Casals, J., Masferrer, G., Gallego-Jutglà, E. (2013). Towards a Low-Complex Breathing Monitoring System Based on Acoustic Signals. In: Drugman, T., Dutoit, T. (eds) Advances in Nonlinear Speech Processing. NOLISP 2013. Lecture Notes in Computer Science(), vol 7911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38847-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38847-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38846-0

  • Online ISBN: 978-3-642-38847-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation