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Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review

Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 58)

Abstract

Wheezes are acoustic, adventitious, continues and high pitch pulmonary sounds produce due to airway obstruction, these sounds mostly exist in pneumonia and asthma patients. Artificial intelligence techniques have been extensively used for wheeze sound analysis to diagnose patient. The available literature has not yet been reviewed. In this article most recent and relevant 12 studies, from different databases related to artificial inelegance techniques for wheeze detection has been selected for detailed review. It has been noticed that now trend is going to increase in this area, for personal assistance and continues monitoring of patient health. The literature reveals that 1) wheezes signals have enough information for the classification of patients according to disease severity level and type of disease, 2) significant work is required for identification of severity level of airway obstruction and pathology differentiation.

Keywords

Wheeze Wheeze Sounds Respiratory Sounds Airway Obstruction Wheeze Analysis 

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References

  1. 1.
    WHO., W. H. O.Google Scholar
  2. 2.
    Meslier, N., Charbonneau, G., and Racineux, J. L. (1995) Wheezes, Eur Respir J 8, 1942-1948.Google Scholar
  3. 3.
    Palaniappan, R., Sundaraj, K., and Ahamed, N. U. (2013) Machine learning in lung sound analysis: A systematic review, Biocybernetics and Biomedical Engineering 33, 129-135.Google Scholar
  4. 4.
    Taplidou, S. A., and Hadjileontiadis, L. J. (2010) Analysis of wheezes using wavelet higher order spectral features, IEEE Trans Biomed Eng 57, 1596-1610.Google Scholar
  5. 5.
    Wisniewski, M., and Zielinski, T. P. (2015) Joint application of audio spectral envelope and tonality index in an e-asthma monitoring system, IEEE J Biomed Health Inform 19, 1009-1018.Google Scholar
  6. 6.
    Taplidou, S. A., and Hadjileontiadis, L. J. (2007) Nonlinear analysis of wheezes using wavelet bicoherence, Comput Biol Med 37, 563-570.Google Scholar
  7. 7.
    Bahoura, M. (2009) Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes, Comput Biol Med 39, 824-843.Google Scholar
  8. 8.
    Bahoura, M., and Pelletier, C. (2004) Respiratory sounds classification using Gaussian mixture models, Electrical and Computer Engineering, 2-5.Google Scholar
  9. 9.
    Bahoura, M., and Pelletier, C. (2003) New parameters for respiratory sound classification, In Canadian Conference on Electrical and Computer Engineering, 2003. IEEE CCECE 2003., pp 1457-1460 vol.1453.Google Scholar
  10. 10.
    Mazić, I., Bonković, M., and Džaja, B. (2015) Two-level coarse-to-fine classification algorithm for asthma wheezing recognition in children’s respiratory sounds, Biomedical Signal Processing and Control 21, 105-118.Google Scholar
  11. 11.
    Oud, M., Dooijes, E. H., and van der Zee, J. S. (2000) Asthmatic airways obstruction assessment based on detailed analysis of respiratory sound spectra, IEEE Trans Biomed Eng 47, 1450-1455.Google Scholar
  12. 12.
    Oud, M. (2003) Lung function interpolation by means of neural-network-supported analysis of respiration sounds, Med Eng Phys 25, 309-316.Google Scholar
  13. 13.
    Wisniewski, M., and Zielinski, T. P. (2012) MRMR-based feature selection for automatic asthma wheezes recognition, In Signals and Electronic Systems (ICSES), 2012 International Conference on, pp 1-5, IEEE.Google Scholar
  14. 14.
    Wisniewski, M., and Zielinski, T. P. (2012) Tonality detection methods for wheezes recognition system, In 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP), pp 472-475.Google Scholar
  15. 15.
    Wisniewski, M., and Zielinski, T. P. (2011) Application of Tonal Index to pulmonary wheezes detection in asthma monitoring, In Signal Processing Conference, 2011 19th European, pp 1544-1548, IEEE.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.School of Mechatronic EngineeringUniversiti Malaysia Perlis (UniMAP)ArauMalaysia
  2. 2.Faculty of Electronics and Computer EngineeringUniversiti Teknikal Malaysia Melaka (UTeM)Durian TunggalMalaysia
  3. 3.School of Electronics Engineering (SENSE)Vellore Institute of Technology (VIT)VelloreIndia
  4. 4.Department of AnesthesiologyHospital Tengku Ampuan Rahimah (HTAR)KlangMalaysia
  5. 5.Faculty of Manufacturing EngineeringUniversiti Malaysia PahangPekanMalaysia

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