You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly.
Go to headerGo to navigationGo to searchGo to contentsGo to footer
In content section. Select this link to jump to navigation

The detection of crackles based on mathematical morphology in spectrogram analysis


BACKGROUND: Crackles are very common abnormal breath sounds in the lung and can be used to diagnose pulmonary diseases.

OBJECTIVE: In this study, a method is proposed for the detection of adventitious transient sounds from normal breath sounds.

METHODS: This method automatically recognizes crackles based on the extraction and analysis of spectral information from digitally recorded lung sounds. Various mathematical morphology feature sets were extracted through wavelet spectrogram analysis on pulmonary signals. In order to evaluate the effects of different wavelets types on crackle detection, different wavelets were tested.

RESULTS: The results showed that the proposed method achieved an 86% accuracy in the detection of crackles.

CONCLUSIONS: The spectrograms of the crackles in the lung exhibit irregular ellipse image features. For lung sound analysis, this is a useful feature that can be used for the immediate recognition and analysis of crackles.