Wheezing sounds detection using multivariate generalized gaussian distributions

  • Authors:
  • S. Le Cam;A. Belghith;Ch. Collet;F. Salzenstein

  • Affiliations:
  • LSIIT UMR CNRS 7005, Université Strasbourg 1 (ULP), France;LSIIT UMR CNRS 7005, Université Strasbourg 1 (ULP), France;LSIIT UMR CNRS 7005, Université Strasbourg 1 (ULP), France;Laboratoire INESS, UMR CNRS 7163, Université Strasbourg 1 (ULP), France

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

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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.