Detection of Transient Signals in Lung Sounds: Local Approach Using a Markovian Tree with Frequency Selectivity

Authors:
Steven Le Cam;Christophe Collet;Fabien Salzenstein
Affiliations:
LSIIT, UMR CNRS 7005, Equipe MIV, Illkirch, France BP 10413 67412;LSIIT, UMR CNRS 7005, Equipe MIV, Illkirch, France BP 10413 67412;Laboratoire INESS, UMR CNRS 7163, Strasbourg, France 67037
Venue:
Journal of Signal Processing Systems
Year:
2011

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Abstract

We deal in this paper with the extraction of multiresolution statistical signatures for the characterization of transient signals in strongly noisy contexts. These short-time signals have sharp and highly variable frequency components. The time/frequency window to choose for our analysis is then a major issue. We have chosen the Wavelet Packet Transform (WPT) due to its ability to provide multiple windows analysis with different time/frequency resolutions. We propose a new oriented Hidden Markov Tree (HMT) dedicated to the tree structure of the WPT, which offers promising statistical characterization of time/frequency variations in a signal, by exploiting several time/frequency resolutions. This model is exploited in a Bayesian context for the segmentation of signals containing transient components. The chosen data likelihood is a Generalized Gaussian Distributions (GGD), well suited for the modeling of Wavelet Packet Coefficients (WPC) distributions. We demonstrate the efficiency of our method on synthetic signals with several Signal to Noise Ratio (SNR). Our application domain is related to biomedical signals, and more specifically for the detection of uprising abnormalities in pulmonary sounds. This original method shows remarkable ability to detect such sounds, which are usually buried in the normal lung noise and are often very difficult to perceive with the human earing.