WHEEZING SOUNDS DETECTION USING MULTIVARIATE GENERALIZED GAUSSIAN DISTRIBUTIONS

被引：17

作者：

Le Cam, S. ^{[1
]}

Belghith, A. ^{[1
]}

Collet, Ch. ^{[1
]}

Salzenstein, F. ^{[2
]}

机构：

[1] Univ Strasbourg, ULP, CNRS, LSIIT,UMR 7005, F-67070 Strasbourg, France

[2] Univ Strasbourg, ULP, CNRS, Lab INESS ,UMR 7163, F-67070 Strasbourg, France

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS | 2009年

关键词：

Adventitious Respiratory Sounds; Data Fusion; Hidden Markov Chain; Generalized Gaussian Distribution; Copulas Theory; HIDDEN MARKOV-MODELS; SIGNAL;

D O I：

10.1109/ICASSP.2009.4959640

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：541 / +

页数：2

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