An Automated System for the Classification of Bronchiolitis and Bronchiectasis Diseases using Lung Sound Analysis

The main goal of this paper is to develop a classification model and a technique to identify bronchiolitis and bronchiectasis using lung sound analysis. In this paper, we develop a methodology to automatically identify lung disease through an intelligent system. ICBHI lungs sound database was used for this study. A total of 64 lung recordings, selected from three pulmonary classes namely normal, bronchiectasis and bronchiolitis were used for this purpose. To accomplish the task, we first split all the recorded signals into four parts to increase the number of input data. Discrete wavelet transform was used to denoise and segment the pulmonological data. Mel frequency cepstral coefficients were then computed from the cleaned signal. After extensive experimentation with various classifiers, the highest recognition rate of 99.6% was found by using K-Nearest Neighbors.

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