Time - frequency and time - scale analysis, decomposition and classification of adventitious pulmonary sounds

Abstract

Pulmonary diseases a ect the quality of life and disturb the patients throughout their life. Due to some disadvantages of auscultation with a traditional stethoscope, computerized lung sound analysis has become a necessity. In this thesis, novel nondyadic overcomplete wavelet based methods are proposed to decompose, detect and classify primary indicators (crackle and wheeze) of pulmonary diseases using various machine learning algorithms. Crackle (explosive and discontinuous), wheeze (musical and continuous) and normal lung sounds are classi ed using Rational Dilation Wavelet Transform based extracted features and compared with related works. It is shown that the proposed method is more successful and faster than its competitors. Moreover, in an ensemble learning scheme it is shown that the optimal representations of signal of interest can be achieved employing the proposed method. Resonance based decomposition using Tunable Q-factor Wavelet Transform and Morphological Component Analysis techniques are proposed to decompose adventitious lung sounds and to localize crackles successfully. The proposed method is compared with related works on adventitious lung sound decomposition and is shown to perform better than other methods in terms of root mean square error, crackle localization accuracy and visual validation. Within class problem in wheeze type classi cation is explored using nondyadic wavelet based features and adaptive peak energy ratio metric. It is shown that either using xed parameter settings in wavelet transform or xed time-frequency (TF) based features, the optimum representation and high performance can not be achieved. After repetitive experiments, it is shown that by using the proposed novel wavelet based methods, optimum and better TF and time-scale representation can be achieved.