Automated classification of lung sound signals based on empirical mode decomposition

Chronic obstructive pulmonary disease (COPD) is a chronic non-reversible lung disease. Other acute respiratory illness due to infections are termed as non-chronic. In general, the pulmonologist carries preliminary screening by accessing lung sounds. In this paper, we propose a methodology to automatically classify lung sounds associated with non-chronic and chronic categories. To accomplish the task, at first, the empirical mode decomposition (EMD) is applied to lung sound signals to obtain intrinsic mode functions (IMFs). Considering the availability of IMFs in entire dataset and by using a hybrid strategy, first four IMFs have been selected for feature extraction purpose. The IMFs are then further processed to construct two-dimensional (2D) and higher-dimensional (HD) phase space representation (PSR). The feature space includes the 95% confidence ellipse area from 2D-PSR and interquartile range (IQR), mean, median, standard deviation, skewness and kurtosis of Euclidian distances computed from HD-PSR. The process is carried out for the first four IMFs corresponding to the non-chronic and chronic categories of the lung sounds. Neighborhood component analysis is used to select best performing features. The computed and selected features depict a significant ability to discriminate the two categories of lung sound signals. To perform classification, we use ensemble classifiers. Simulation outcomes on ICBHI 2017 lung sound dataset show the ability of the proposed method in effectively classifying non-chronic and chronic lung sound signals. Ensemble of Bagged tree provides the highest classification accuracy of 97.14% over feature space constituted by 10D-PSR of fourth IMF.