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%.
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Nakano, T., Nakajima, S. (2016). Detection of Airway Obstruction from Frequency Distribution Feature of Lung Sounds with Small Power of Abnormal Sounds. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. GEC 2015. Advances in Intelligent Systems and Computing, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-319-23207-2_36
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DOI: https://doi.org/10.1007/978-3-319-23207-2_36
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