NDLI: Imaging of simulated crackle sounds distribution on the chest

Content Provider	IEEE Xplore Digital Library
Author	Dorantes-Mendez, G. Charleston-Villalobos, S. Gonzalez-Camarena, R. Chi-Lem, G. Aljama-Corrales, T.
Copyright Year	2008
Description	Author affiliation: Electrical Engineering Department, Universidad Autónoma Metropolitana, Mexico City 09340, Mexico (Charleston-Villalobos, S.; Aljama-Corrales, T.) \|\| Health Science Department, Universidad Autónoma Metropolitana, Mexico City 09340, Mexico (Gonzalez-Camarena, R.) \|\| National Institute of Respiratory Diseases, Mexico City, Mexico (Chi-Lem, G.) \|\| Biomedical Engineering Program, Universidad Autónoma Metropolitana, Mexico City 09340, Mexico (Dorantes-Mendez, G.)
Abstract	Crackles sounds have been associated with several pulmonary pathologies and diverse algorithms have been proposed for extracting and counting them from the acquired lung sound. These tasks depend among other factors, of the relation between the magnitude of the crackle and the background lung sound. In this work, we explore multivariate signal processing to deal with the tasks and propose a new concept, the discontinuous adventitious sounds imaging. The image formation is founded on the results of two proposed methodologies that use an autoregressive (AR) model. In the first case, the AR coefficients feed an artificial neural network (ANN) to classify temporal acoustic information as healthy or sick and; in the second case, a time-variant AR (TVAR) model, obtained by the RLS algorithm, permits to detect changes in the TVAR coefficients to be associated with the number of crackles. For AR-ANN, the ratio of the temporal windows classified as sick to the classified as healthy is used as an index to form the adventitious image, while for TVAR-RLS, an estimation of the number of crackles is obtained to form the corresponding image. The results indicated that fine and coarse crackles could be detected and counted even with very low crackle magnitude so that the formation of a crackle distribution image was consistent.
Starting Page	4801
Ending Page	4804
File Size	474745
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4650287
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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