Automated Detection of Pulmonary Diseases From Lung Sound Signals Using Fixed-Boundary-Based Empirical Wavelet Transform

In this letter, a promising method is proposed to automatically detect pulmonary diseases (PDs) from lung sound (LS) signals. The modes of the LS signal are evaluated using empirical wavelet transform with fixed boundary points. The time-domain (Shannon entropy) and frequency-domain (peak amplitude and peak frequency) features have been extracted from each mode. The classifiers, such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LGBM), have been chosen to detect PDs using the features of LS signals automatically. The performance of the proposed method has been evaluated using LS signals obtained from a publicly available database. The detection accuracy values, such as 80.35, 83.27, 99.34, and 77.13%, have been obtained using the LGBM classifier with fivefold cross validation for normal versus asthma, normal versus pneumonia, normal versus chronic obstructive pulmonary disease (COPD), and normal versus pneumonia versus asthma versus COPD classification schemes. For the normal versus pneumonia versus asthma classification scheme, the proposed method has achieved an accuracy value of 84.76%, which is higher than that of the existing approaches using LS signals.