International Conference on Genetic and Evolutionary Computing

GEC 2015: Genetic and Evolutionary Computing pp 355-363 | Cite as

Detection of Airway Obstruction from Frequency Distribution Feature of Lung Sounds with Small Power of Abnormal Sounds

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 388)

Abstract

We propose a new method to detect airway obstruction from a lung sound record with power of which abnormal sounds is much smaller than power of normal sound s. One of traditional methods to detect airway obstruction is FEV1% (forced expiratory volume 1 sec percentage) using a spirometry. But it bothers a patient too much. Some methods were proposed recently to detect abnormal sounds because an airway obstruction sometimes makes abnormal sounds such as wheeze or rhonchi or else. But it is not available for cases with small power of abnormal sounds. The correlation coefficient between our proposed value and FEV1% was -.592. And the AUC value of the proposed method with 70% threshold of FEV1% was 0.833. The proposed method could detect airway obstruction with sensitivity=0.8 and specificity = 0.78 FEV1%.

Keywords

Bronchial asthma Airway constriction FEV1% Fourier transform Wheeze Diagnosis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ortiz, G.: Asthma Diagnosis and Management: A Review of the Updated National Asthma Education and Prevention Program Treatment Guidelines. The Internet Journal of Academic Physician Assistants 6(2) (2008)Google Scholar
  2. 2.
    Antônio, J.: Not all that wheezes is asthma! J Bras. Pneumol. 39(4), 518–520 (2013)CrossRefGoogle Scholar
  3. 3.
    Beck, R., Dickson, U., et al.: Histamine Challenge in Young Children Using Computerized Lung Sounds Analysis. Chest 102, 759–763 (1992)CrossRefGoogle Scholar
  4. 4.
    Malmberg, L.P., Sovijarvi, A.R.A., et al.: Challenges in Frequency Spectra of Breath Sounds During Histamine Challenge Test in Adult Asthmatics and Healthy Control Subjects. Chest 105, 122–132 (1994)CrossRefGoogle Scholar
  5. 5.
    Schreur, H.J.W., Vanderschoot, J., et al.: The effect of methacholine-induced acute airway narrowing on lung sounds in normal and asthmatic subjects. Eur. Respir. J. 8, 257–265 (1995)CrossRefGoogle Scholar
  6. 6.
    Palaniappan, R., Sundaraj, K., Ahamed, N.U.: Machine learning in lung sound analysis. Biocybernetric And Biological Engineering 33, 129–135 (2013)CrossRefGoogle Scholar
  7. 7.
    Palaniappan, R., Sundaraj, K., Ahamed, N.U., Arjunan, A., Sundaraj, S.: Computer-based Respiratry Sound Analysis: A Systematic Review. IETE Technical Review 30(3), 248–258 (2013)CrossRefGoogle Scholar
  8. 8.
    Riella, R.J., Nohama, P., Maia, J.M.: Method for automatic detection of wheezing in lung sounds. Brazilian Journal of Medical and Biological Research 42, 674–684 (2009)CrossRefGoogle Scholar
  9. 9.
    Chen, M.-Y., Chou, C.-H.: Applying Cybernetic Technology to Diagnose Human Pulmonary Sounds. Journal of Medical Systems 38(6), 1–10 (2014)MathSciNetGoogle Scholar
  10. 10.
    Bahoura, M., Hubin, M.: Automatic wheeze detection using wavelet packets. In: Conference on Medical and Biological Engineering and Computing VIII Mediterranean, Limassol, Cyprus, pp. 14–17, June 1998Google Scholar
  11. 11.
    Bahoura, M., Lu, X.: Separation of crackles from respiratory sounds using wavelet packet transform. In: The 31st International Conference on Acoustics, Speech, and Signal Processing (ICASSP-06), Toulouse, France, vol. II, pp. 1076–1079, May 2006Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Mitsubishi Electric Information Systems CorporationTokyoJapan
  2. 2.Grad School of EngneeringOsaka City UniversityOsakaJapan

Personalised recommendations