Modeling of pulmonary crackles using wavelet networks.

In this study, wavelet networks are used to model pulmonary crackles with a view to extract features for the classification analysis of crackles obtained from subjects with a wide spectrum of pulmonary disorders. Crackles are very common adventitious sounds which are transient in character and whose characteristics, such as type, number of occurrence and pitch, convey information regarding the type and severity of the pulmonary disease. Crackles generally start with a sharp deflection and continue with a damped and progressively wider sinusoidal wave. In this study, due to the capability of time-frequency representation of wavelet functions, wavelet network (WN) is employed to characterize crackles, and the parameters acquired from wavelet nodes are used to distinguish them into two clinical classes, i.e. fine and coarse. For this purpose, a wavelet function (complex Morlet) in the first node is trained to fit the crackles and the second wavelet node is tuned to represent the error of the first node. Both of the nodes are, then, trained to minimize the total representative error. The five parameters of the WN node, i.e. scaling, time-shifting, frequency and two weight factors of sinus and cosines components are used as features in the classification analysis of crackles. The crackle information is strongly represented by the first wavelet node, therefore, the parameters belonging to the first node are used in the classification of crackles.