Automated extraction of fine and coarse crackles by independent component analysis
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In this work further research was achieved on the separation by Independent Components (IC) of simulated abnormal breathing sounds (ABS) sources immersed in normal breathing sounds. This study considers only ABS discontinuous sounds, known as crackles, and includes fine and coarse types for both inspiratory and expiratory phases. Additionally, we develop a novel proposal to automated characterization of the IC associated with crackles. We analyzed the efficiency of three independent component analysis algorithms, i.e. FastICA, Infomax and TDSEP, through the Amari index, the signal to interference ratio, and the total relative distortion index. In the simulated multichannel signal scenarios, the performance indexes showed that Infomax is the best algorithm to solve the problem of blind source separation, supporting the results found in previous efforts. Finally, the presence of crackles in the IC obtained by Infomax was determined through their kurtosis and skewness, whereas the type of crackle was found by their characterization via the spectrogram of selected IC. Results indicate that the proposed methodology is able to adequately extract the crackle sources and identify the respiratory cycle phase in which they appear. Also, we managed to estimate the type and number of existing crackles in each source. In conclusion, our methodology can provide quantitative information on the clinical relevance of crackles in respiratory patients.
KeywordsBlind source separation Crackles Kurtosis Skewness Spectrogram
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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