Acoustic thoracic image of crackle sounds using linear and nonlinear processing techniques.

Medical & Biological Engineering & Computing
S Charleston-VillalobosT Aljama-Corrales

Abstract

In this study, a novel approach is proposed, the imaging of crackle sounds distribution on the thorax based on processing techniques that could contend with the detection and count of crackles; hence, the normalized fractal dimension (NFD), the univariate AR modeling combined with a supervised neural network (UAR-SNN), and the time-variant autoregressive (TVAR) model were assessed. The proposed processing schemes were tested inserting simulated crackles in normal lung sounds acquired by a multichannel system on the posterior thoracic surface. In order to evaluate the robustness of the processing schemes, different scenarios were created by manipulating the number of crackles, the type of crackles, the spatial distribution, and the signal to noise ratio (SNR) at different pulmonary regions. The results indicate that TVAR scheme showed the best performance, compared with NFD and UAR-SNN schemes, for detecting and counting simulated crackles with an average specificity very close to 100%, and average sensitivity of 98 ± 7.5% even with overlapped crackles and with SNR corresponding to a scaling factor as low as 1.5. Finally, the performance of the TVAR scheme was tested against a human expert using simulated and real acoustic infor...Continue Reading

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Citations

Feb 1, 2014·Computers in Biology and Medicine·S Charleston-VillalobosT Aljama-Corrales
Apr 18, 2013·Methods of Information in Medicine·S Charleston-VillalobosT Aljama-Corrales
Dec 10, 2014·Australasian Physical & Engineering Sciences in Medicine·Pooria Jafari Moghadam FardSassan Saber
Jun 25, 2013·Computer Methods and Programs in Biomedicine·Ipek SenYasemin P Kahya
Dec 14, 2018·Frontiers in Neuroscience·Yuling LuoXuemei Ding

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