An integrated automated system for crackles extraction and classification
This paper presents an integrated automated system for crackles recognition. This system comprises three serial modules with following functions: (1) separation of crackles from vesicular sounds using a wavelet packet filter (WPSTNST); (2) detection of crackles by fractal dimension (FD); (3) classification of crackles based on Gaussian mixture models (GMM). The WPSTNST filter incorporates a multi-resolution decomposition of the original respiratory signal and an entropy-based best basis selection of the coefficients. Two thresholds are defined, in time and frequency domains respectively, to separate the crackles from the respiratory sounds. Then, a denoising filter is applied to the discontinuous output of WPSTNST and a crackle-peak-detector (CPD) localizes the individual crackles by means of their fractal dimension. After that, three feature parameters, including the Gaussian bandwidth (GBW), the peak frequency (PF) and the maximal deflection width (MDW), of the crackles are extracted. Finally, crackles are classified into fine crackles (FC) and coarse crackles (CC) using Gaussian mixture models.
crackles fractal dimension gaussian mixture models lung sounds wavelet packet transform feature extraction prony model vector quantization