Classification of Normal, Asthma and COPD Subjects Using Multichannel Lung Sound Signals

Lung sounds carry valuable information regarding the lungs pathology. The collaboration of advanced digital signal processing method and machine learning framework has immense potential to diagnose the lungs status using lung sound signals. In this paper, we have classified normal, asthma, and COPD subjects using their posterior lung sound signals, which was unexplored so far. Asthma and COPD diseases have common manifestations, for which the classification of these diseases in a single platform is like a conundrum. We have collected lung sound signals from 60 subjects (20 normal, 20 asthma, and 20 COPD) using a novel 4-channel data acquisition system. A feature extraction scheme is proposed to extract features from the subbands of the power spectral density (PSD), and fed to the artificial neural network (ANN) classifier for the 3-class classification. The changes in the posterior lung sound signals are targeted for feature extraction, which does not depend on the presence of wheeze or any other marker. The proposed multichannel based multiclass classification system achieves reasonable classification accuracy which is much higher than the theoretical and empirical chance levels, when the information of all the four channels are utilized together.