A multichannel acoustic approach to define a pulmonary pathology as combined pulmonary fibrosis and emphysema syndrome.

Interstitial lung diseases (ILDs) have been increasing their relevance in loss of lives according to a recent world wide medical information. Idiopathic pulmonary fibrosis (IPF) and combined pulmonary fibrosis and emphysema syndrome (CPFES) belong to ILD class with the latter having a limited survival prognosis. In clinical environment high resolution computed tomography (HRCT) is used to detect CPFE; however, there is still controversy about the amount of emphysema observed in HRCT to declare CPFES. Consequently, to help in the diagnosis of CPFES to develop an alternative technique seems to be attractive. In this study, we propose a multichannel acoustic approach to discriminate between IPF and CPFES parameterizing the multichannel lung sounds information linearly and classifying it by neural networks (NN). The NN performance using different features provided values above 90% in the validation phase. Furthermore, to test the trained NN, the proposed approach was applied on new data from five patients 3 diagnosed by experts as CPFES and 2 with IPF. The univariate autoregressive model obtained the best classification followed by the feature vector formed by the percentile frequencies augmented by the total power of the acoustic information. Results indicate that multichannel acoustic analysis is promising to discern between these two ILDs.