• Conf Proc IEEE Eng Med Biol Soc · Jan 2012

    Assessment of ICA algorithms for the analysis of crackles sounds.

    • N Castañeda-Villa, S Charleston-Villalobos, R González-Camarena, and T Aljama-Corrales.
    • Electrical Engineering Department, Universidad Autónoma Metropolitana, Mexico City 09340, Mexico. ncv@xanum.uam.mx
    • Conf Proc IEEE Eng Med Biol Soc. 2012 Jan 1;2012:605-8.

    AbstractBlind source separation by independent component analysis has been applied extensively in the biomedical field for extracting different contributing sources in a signal. Regarding lung sounds analysis to isolate the adventitious sounds from normal breathing sound is relevant. In this work the performance of FastICA, Infomax, JADE and TDSEP algorithms was assessed using different scenarios including simulated fine and coarse crackles embedded in recorded normal breathing sounds. Our results pointed out that Infomax obtained the minimum Amari index (0.10037) and the maximum signal to interference ratio (1.4578e+009). Afterwards, Infomax was applied to 25 channels of recorded normal breathing sound where simulated fine and coarse crackles were added including acoustic propagation effects. A robust blind crackle separation could improve previous results in generating an adventitious acoustic thoracic imaging.

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