Elimination of vesicular sounds from pulmonary crackle waveforms.

Pulmonary crackles and their parameters are very useful in the diagnosis of pulmonary disorders. A new automatic method has been proposed for the elimination of background vesicular sound from crackle signal with a view to introduce minimum distortion to crackle parameters. A region of interest is designated and a distortion metric based on the correlation between raw and filtered waveforms in that region is defined. Filter cut-off frequency is estimated based on the distortion metric. To reduce computational cost, a regression analysis is also realized which predicts a new fitted cut-off frequency from the estimated cut-off frequency. As a comparison basis, wavelet filtering is also applied on the same data. The algorithm is validated on simulated crackles superimposed on recorded vesicular sound with results indicating that filtering is achieved with minimal distortion of crackle parameters. The algorithm is also applied on real crackles from subjects with various respiratory disorders. The results show the extent of the effect of vesicular sound on crackle parameters, emphasizing the significance of proper filtering in crackle studies.