Abstract
Respiratory sounds contain significant information on physiology and pathology of the lung and the airways. Its analysis provides vital information of the present condition of the lungs. Pulmonary disease is a major cause of ill-health throughout the world. The frequency spectrum and the amplitude of sound, i.e. tracheal or lung sounds without adventitious sound components (wheeze), may reflect airway dimension and other their pathologic changes (airways obstruction). Wheezes may have acoustic features indicating not only the presence of abnormality in the respiratory system but also the severity and locations of airway obstruction most frequently found in asthma and also found in smoker but not all smokers have airway obstruction. The significance of this study is to develop a classification system to distinguish between normal and smoker from respiratory sounds. 15 smokers and 15 non-smokers are recruited to collect respiratory sounds using Wireless Digital Stethoscope. The performance analysis of the K-Nearest Neighbor (k-NN) classifier, which uses entropy as the suitable feature, revealed that the classification accuracy on non-smokers and smokers are 89.33% and 78.67% respectively.
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Abdullah, N.S., Lam, C.K., Sundaraj, K., Palaniappan, R. (2017). Classification of Respiratory Sounds in Smokers and Non-smokers using k-NN Classifier. In: Ibrahim, F., Cheong, J., Usman, J., Ahmad, M., Razman, R., Selvanayagam, V. (eds) 3rd International Conference on Movement, Health and Exercise. MoHE 2016. IFMBE Proceedings, vol 58. Springer, Singapore. https://doi.org/10.1007/978-981-10-3737-5_15
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DOI: https://doi.org/10.1007/978-981-10-3737-5_15
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