Kexin Zhang1,2, Xuefeng Wang3, Fangfang Han1, Hong Zhao1. 1. Northeastern University, Shenyang, Liaoning, China. 2. Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, China. 3. The First Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, China.
Abstract
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.
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.