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The detection of crackles based on mathematical morphology in spectrogram analysis

Issue title: Papers from the 3rd International Conference on Biomedical Engineering and Technology (iCBEB 2014), 25–28 September 2014, Beijing, China

Subtitle:

Guest editors: Edward J. Ciaccio

Article type: Research Article

Authors: Zhang, Kexina; b | Wang, Xuefengc | Han, Fangfanga | Zhao, Honga; *

Affiliations: [a] Northeastern University, Shenyang, Liaoning, China | [b] Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, China | [c] The First Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, China

Correspondence: [*] Corresponding author: Hong Zhao, 308 Admin Building, Neusoft Park, #2 Xinxiu st. Hunnan dist. Shenyang, Liaoning 110179, China. Tel.: +86 024 8366 1111; E-mail:[email protected]

Keywords: Crackles, mathematical morphology, wavelets, lung sounds, spectrogram

DOI: 10.3233/THC-150986

Journal: Technology and Health Care, vol. 23, no. s2, pp. S489-S494, 2015

Published: 2015
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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.

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