Wavelet denoising and segmentation for non-stationary signals: a reinterpretation of an iterative algorithm and application to phonoenterography

摘要

This communication deals with wavelet-based denoising techniques of non-stationary signals, in order to extractudinformative events. The practical application concerns physiological bowel sounds processing, with a view to medicaluddiagnosis and monitoring. This work continues and develops a recent publication placed in the same frameworkud[14].udThe method for separating the stationary part from the non-stationary part of a signal presented by Hadjileontiadisudet al. [15, 14] stems from a denoising algorithm introduced by Coifman and Wickerhauser [6, 7]. This methodudinvolves two user-tuned parameters. We propose a novel version of this algorithm, based on a fixed-point interpretation.udThis modification allows to eliminate one of the parameters and to determine an inferior limit for theudsecond, depending on the probability distribution of the wavelet coefficients. This revisited version also improvesudsignificantly the computational efficiency. We present the results and compare them with other denoising algorithms,udboth on simulated signals and on real bowel sounds.