An integrated automated system for crackles extraction and classification

• Published in: Biomedical signal processing and control Vol. 3; no. 3; pp. 244 - 254
• Main Authors: Lu, Xiaoguang, Bahoura, Mohammed
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2008
• Subjects: Crackles; Feature extraction; Fractal dimension; Gaussian mixture models; Lung sounds; Prony model; Vector quantization; Wavelet packet transform; Crackles; Fractal dimension; Vector quantization; Wavelet packet transform; Feature extraction; Gaussian mixture models; Prony model; Lung sounds
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2008.04.003

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 (WPST–NST); (2) detection of crackles by fractal dimension (FD); (3) classification of crackles based on Gaussian mixture models (GMM). The WPST–NST 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 WPST–NST 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.