Adventitious lung sounds imaging by ICA-TVAR scheme

Adventitious lung sounds (ALS) as crackles and wheezes are present in different lung alterations and their automated characterization and recognition have become relevant. In fact, recently their 2D spatial distribution (SD) imaging has been proposed to help diagnose of pulmonary diseases. In this work, independent component analysis (ICA) by infomax was used to find crackles sources and from them to apply a time variant autoregressive model (TVAR) to count and imaging the ALS. The proposed methodology was assessed on multichannel LS recordings by embedding simulated fine crackles with known SD in recorded normal breathing sounds. Afterwards, the adventitious image of two patients with fibrosis and emphysema were obtained and contrasted with the classical pulmonary auscultation provided by a pneumologist. The results showed that combining ICA and TVAR leads to a robust methodology to imaging ALS.