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)


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%.


Bronchial asthma Airway constriction FEV1% Fourier transform Wheeze Diagnosis 


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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

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