DocumentCode
3512841
Title
Wheezing sounds detection using multivariate generalized gaussian distributions
Author
Le Cam, S. ; Belghith, A. ; Collet, Ch ; Salzenstein, F.
Author_Institution
LSIIT, Univ. Strasbourg 1 (ULP), Strasbourg
fYear
2009
fDate
19-24 April 2009
Firstpage
541
Lastpage
544
Abstract
A wheeze is a continuous, coarse, whistling sound produced in the respiratory airways during breathing, commonly experienced by persons suffering from asthma. In this paper, we present a new method for the detection of wheezing sounds in the normal breathing sounds. In our study we perform an accurate statistical analysis of breathing signals. We suggest a modeling for wheezing and normal sounds in the wavelet packet domain using generalized Gaussian distributions. Our detection method is based on a specific multimodal Markovian modeling proposed in a Bayesian framework. We cope with the multidimensional aspect of the generalized Gaussian distribution by using the theory of copulas. Experimental results are given in detail in this paper.
Keywords
Gaussian distribution; Markov processes; acoustic signal processing; belief networks; medical computing; statistical analysis; wavelet transforms; Bayesian framework; breathing signals; multimodal Markovian modeling; multivariate generalized Gaussian distributions; normal breathing sounds; respiratory airways; statistical analysis; wavelet packet domain; wheezing sounds detection; Cepstral analysis; Frequency; Gaussian distribution; Hidden Markov models; Image analysis; Probability density function; Shape; Statistical analysis; Wavelet domain; Wavelet packets; Adventitious Respiratory Sounds; Copulas; Data Fusion; Generalized Gaussian Distribution; Hidden Markov Chain; Theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location
Taipei
ISSN
1520-6149
Print_ISBN
978-1-4244-2353-8
Electronic_ISBN
1520-6149
Type
conf
DOI
10.1109/ICASSP.2009.4959640
Filename
4959640
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