Wavelet-based bowel sounds denoising, segmentation and characterization

The general framework of this communication is phonoenterography. The ultimate goal is the development of a clinical diagnostic tool based on abdominal sound monitoring. Bowel sounds are recorded using several microphones. Unsupervised data processing should lead to diagnosis assessment. We address here the early stages of data processing, i.e., denoising, segmentation and characterization of detected events. The denoising algorithm is based on former work by Coifman and Wickerhauser [1] and Hadjileontiadis et al. [2], [3]. Their wavelet-based algorithm is revisited, allowing to significantly reduce the computational burden. Sound segmentation and event characterization are based on the wavelet representation of the phonoenterogram. Real data processing examples are given.