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Due to the subjectivity involved cur- rently in pulmonary auscultation process and its d iagnostic to evaluate the condition of respira- tory airways, this work pretends to evaluate the p erformance of clustering algorithms such as k- means and DBSCAN to perform a computational analysis of lung sounds aiming to visualize a representation of such sounds that highlights the presence of crackles and the energy associated with them. In order to achieve that goal, Wavelet analysis techniques were used in contrast to tra- ditional frequency analysis given the similarity b etween the typical waveform for a crackle and the wavelet sym4. Once the lung sound signal with isolated crackles is obtained, the cluster- ing process groups crackles in regions of high d ensity and provides visualization that might be useful for the diagnostic made by an expert. Evaluation suggests that k-means groups crackle more effective than DBSCAN in terms of gener- ated clusters.

Application of Clustering Techniques for Lung Sounds to Improve Interpretability and Detection of Crackles