Separation and Classification of Crackles and Bronchial Breath Sounds from Normal Breath Sounds Using Gaussian Mixture Model

  • Ali Haider
  • M. Daniyal Ashraf
  • M. Usama Azhar
  • Syed Osama Maruf
  • Mehdi Naqvi
  • Sajid Gul Khawaja
  • M. Usman Akram
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8835)


A computer aided diagnostic system capable of analyzing respiratory sounds can be very helpful in detection of pneumonia, asthma and tuberculosis as the Respiratory sound signal carries information about the underlying physiology of the lungs and is used to detect presence of adventitious lung sounds which are an indication of disease. Respiratory sound analysis helps in distinguishing normal respiratory sounds from abnormal respiratory sounds and this can be used to accurately diagnose respiratory diseases as is done by a medical specialist via auscultation. This process has subjective nature and that is why simple auscultation cannot be relied upon.In this paper we present a novel method for automated detection of crackles and bronchial breath sounds which when coupled together indicate presence and severity of Pneumonia. The proposed system consists of four modules i.e., pre-processing in which noise is filtered out, followed by feature extraction. The proposed system then performs classification to separate crackles and bronchial breath sounds from normal breath sounds.


Positive Predictive Value Gaussian Mixture Model Wavelet Packet Breath Sound Lung Sound 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    UNICEF, Pneumonia and Diarrhea Tackling the Deadliest Diseases of the World’s Poorest Children, UNICEF Division of Policy and Strategy, New York (June 2012)Google Scholar
  2. 2.
    Sovijarvi, A.R.A., Vanderschoot, J., Earis, J.E.: Standardization of computerized respiratory sound analysis. Eur. Respir. Rev. 10(77), 585 (2000)Google Scholar
  3. 3.
    Sovijrvi, A.R.A., Dalmasso, F., Vanderschoot, J., Malmberg, L.P., Righini, G., Stoneman, S.A.T.: Definition of terms for applications of respiratory sounds. Eur. Respir. Rev. 10(77), 597–610 (2000)Google Scholar
  4. 4.
    Epler, G.R., Carrington, C.B., Gaensler, E.A.: Crackles (rales) in the interstitial pulmonary diseases. Chest 73, 333–339 (1978)CrossRefGoogle Scholar
  5. 5.
    Ono, M., Arakawa, K., Mori, M., Sugimoto, T., Harashima, H.: Separation of fine crackles from vesicular sounds by a nonlinear digital filter. IEEE Trans. Biomed. Eng. 36(2), 286–291 (1989)CrossRefGoogle Scholar
  6. 6.
    Arakawa, K., Harashima, H., Ono, M., Mori, M.: Non-linear digital filters for extracting crackles from lung sounds. Front. Med. Biol. Eng. (3), 245–257 (1991)Google Scholar
  7. 7.
    Hadjileontiadis, L.J., Panas, S.M.: Nonlinear separation of crackles and squawks from vesicular sounds using third-order statistics. In: 18th International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 5, pp. 2217–2219 (1996)Google Scholar
  8. 8.
    Hadjileontiadis, L.J., Panas, S.M.: Separation of discontinuous adventitious sounds from vesicular sounds using a wavelet-based filter. IEEE Trans. Biomed. Eng. 44(12), 1269–1281 (1997)CrossRefGoogle Scholar
  9. 9.
    Tolias, Y.A., Hadjileontiadis, L.J., Panas, S.M.: Realtime separation of discontinuous adventitious sounds from vesicular sounds using a fuzzy rule-based filter. IEEE Trans. Inf. Technol. Biomed. 2(3), 204–215 (1998)CrossRefGoogle Scholar
  10. 10.
    Bahoura, M., Lu, X.: Separation of Crackles from Vesicular Sounds Using Wavelet Packet Transform (2006)Google Scholar
  11. 11.
    Bahoura, M., Lu, X.: An Automatic System For Crackles Detection And Classification. In: IEEE CCECE/CCGEI (2006)Google Scholar
  12. 12.
    Ayari, F., Ksouri, M., Alouani, A.: A new scheme for automatic classification of pathologic lung sounds. IJCSI International Journal of Computer Science Issues 9(4(1)) (2012)Google Scholar
  13. 13.
    Martinez-Hernandez, H.G., Aljama-Corrales, C.T., Gonzalez-Camarena, R., Charleston-Villalobos, V.S., Chi-Lem, G.: Computerized Classification of Normal and Abnormal Lung Sounds by Multivariate Linear Autoregressive Model. In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1-4 (2005)Google Scholar
  14. 14.
    Earis, J.E., Cheetham, B.M.G.: Current methods used for computerized respiratory sound analysis. Eur. Respir. Rev. 10(77), 586–590 (2000)Google Scholar
  15. 15.
    Klapuri, A., Davy, M.: Signal processing methods for music transcription, p. 8. Springer (2006) ISBN 978-0-387-30667-4Google Scholar
  16. 16.
    Ihara, S.: Information theory for continuous systems, p. 2. World Scientific (1993) ISBN 978-981-02-0985-8Google Scholar
  17. 17.
    Dodge, Y.: The Oxford Dictionary of Statistical Terms, OUP (2003) ISBN 0-19-920613-9Google Scholar
  18. 18.
    Pappas, P.A., De Puy, V.: An Overview of Non-parametric Tests in SAS: When, Why, and HowGoogle Scholar
  19. 19.
    Ansari, A.R., Bradley, R.A.: Rank-Sum Tests for Dispersions. Institute of Mathematical Statistics is collaborating with JSTOR to digitize, preserve, and extend access to The Annals of Mathematical StatisticsGoogle Scholar
  20. 20.
    Usman Akram, M., Tariq, A., Almas Anjum, M., Younus Javed, M.: Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy. Applied Optics 51(20(10)) (July 2012)Google Scholar
  21. 21.
    Bahoura, M., Pelletier, C.: Respiratory Sounds Classification using Cepstral Analysis and Gaussian Mixture Models. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco, CA, USA, September 1-5 (2004)Google Scholar
  22. 22.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley (2001)Google Scholar
  23. 23.
    Earis, J.E., Cheetham, B.M.G.: Future perspectives for respiratory sound research. Eur. Respir. Rev. 10(77), 641–646 (2000)Google Scholar
  24. 24.
    Sovijarvi, A.R.A., Malmberg, L.P., Charbonneau, G., Vanderschoot, J., Dalmasso, F., Sacco, C., Rossi, M., Earis, J.E.: Characteristics of breath sounds and adventitious respiratory sounds. Eur. Respir. Rev. 10(77), 591–596 (2000)Google Scholar
  25. 25.
    Gross, V., Fachinger, P., Penzel, T., Koehler, U., von Wichert, P., Vogelmeier, C.: Detection of bronchial breathing caused by pneumonia. Biomed. Tech (Berl) 47(6), 146–150 (2002)CrossRefGoogle Scholar
  26. 26.
    NHLBI Fact Book, Fiscal Year 2012, 38 p. (2012)Google Scholar
  27. 27.
    NHLBI Fact Book, Fiscal Year 2012, 51p. (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ali Haider
    • 1
  • M. Daniyal Ashraf
    • 1
  • M. Usama Azhar
    • 1
  • Syed Osama Maruf
    • 1
  • Mehdi Naqvi
    • 1
  • Sajid Gul Khawaja
    • 1
  • M. Usman Akram
    • 1
  1. 1.College of Electrical and Mechanical EngineeringNational University of Sciences and TechnologyPakistan

Personalised recommendations