Automated Severity Detection Of Chronic Obstructive Pulmonary Disease Using Lung Sounds

Lung sound is a non-invasive diagnostic tool for assessing several respiratory disorders, such as chronic obstructive pulmonary disorder (COPD). Due to the severe implications of COPD, it is essential to distinguish different severity levels of COPD. In this paper, we have utilized 12 channel lung sound data from RespiratoryDatabase@TR to distinguish two extreme severity levels of COPD namely, COPD-0 (lesser risk) and COPD-4 (very severe level). In this study, we have proposed an efficient COPD severity classification framework using variational mode decomposition (VMD); which involves four major stages: (a) preprocessing, (b) signal decomposition using VMD and feature extraction from the decomposed modes, (c) feature selection and ranking, (d) classification using machine learning (ML) classifiers. Employing this proposed technique, we have achieved high classification measures of 97.43%, 100%,93.75% for accuracy, specificity, and sensitivity respectively, which is superior to the only existing work presented by Altan et al.