An Adaptive Method for Classification of Noisy Respiratory Sounds

Respiratory sounds (RSs) contain essential information about the physiology and pathology of lungs and airways obstruction. Therefore, RS understanding has a critical role in diagnosing respiratory patients. However, the external noise in the respiratory sound signal is a large restriction for this study. In this paper, we propose a method to classify noisy respiratory signals. Firstly, four adaptive filtering algorithms (RLS, LMS, NLMS, and Kalman) are applied and evaluated for noise reduction. Then, we extract features of filtered sounds, using Mel Frequency Cepstral Coefficient. Finally, the SVM method is used to classify respiratory sounds. We also conducted experiments on a dataset consisting of 1980 breath events, collected from 16 healthy volunteers. The obtained results show that, the investigated methods, SVM and Kalman achieves the highest accuracy of 95.5%.