Lung sound decomposition using recurrent fuzzy wavelet network

Abstract

Lung abnormalities and respiratory diseases increase as side effects of urban life and development. Therefore, understanding the lung dynamics and its changes during the presence of abnormalities are critical in order to design more reliable tools for the early diagnosis and screening of lung pathology. The aim of this paper is to show the ability of recurrent fuzzy wavelet network (RFWN) to use as a reliable decomposer for lung sound (LS) signals. Since LSs have more dependency to their past states, we have considered recurrent connections in the model in which the ability of fuzzy structure in constructing a representative model was improved. Also, given the utility of wavelet neural network (WNN) as a powerful tool for time-frequency representation, we have adopted them in the consequent parts of the fuzzy rules. Furthermore, WNNs have the multi-resolution analysis (MRA) capability, and our proposed model exploits this characteristic to build an interpretable decomposition approach. Lung sound signals which captured by a multichannel data acquisition system decomposed by RFWN and then support vector machine utilized for classifying subjects using the features extracted from each decomposed line. As results show, a meaningful separability between healthy and non-healthy groups and also COPD and asthma diseases were achieved. In Addition, the recurrent structure could better model the time-dependent behavior of the lung sounds, and it improved the average accuracy of the classification. In particular, a classification accuracy of 95% was achieved using our proposed methodology when three different categorizes are considered.