Analysis-synthesis of the phonocardiogram based on the matching pursuit method

Abstract

The matching pursuit method of Mallat and Zhang is applied to the analysis and synthesis of phonocardiograms (PCG's). The method is based on a classical Gabor wavelet or time-frequency atom which is the product of a sinusoid and a Gaussian window function. It decomposes a signal into a series of time-frequency atoms by an iterative process based on selecting the largest inner product of the signal (and the subsequent residues) with atoms from a redundant dictionary. The Gaussian window controls the envelope duration and time position of each atom; and the sinusoid represents the frequency. The method was applied to two sets of PCG's: one with very low-noise level and the other with 10% noise energy. Each data base includes 11 PCG's representing the normal and the pathological conditions of the heart. The normalized root-mean-square error (NRMSE) was computed between the original and the reconstructed signals. The results show that the matching pursuit method is very suitable to the transient and complex properties of the PCG's, as it yielded excellent NRMSE's around 2.2% for the two sets of 11 PCG's tested.

Detection of the valvular split within the second heart sound using the reassigned smoothed pseudo Wigner–Ville distribution

“…Xu et al  discussed the exponentially damped sinusoid model [19,20], the matching pursuit method [21,22], and the linear chirp model as modelling approaches of heart sounds. They found out that the transient nonlinear chirp signal they developed is the suitable model for the analysis–synthesis of the valvular heart sounds.…”

Signal processing of heart signals for the quantification of non-deterministic events

“…Another purpose is to filter out undesired signal components with either technical or physiological origin so that analysis of the relevant portion of the signal is facilitated. For example, some analyses have focused on attempting to identify valve abnormality [1,2], heart rate variability [3-5], to detect heart pathologies [6-10], to detect murmurs [11] and to detect and quantify cavitation in mechanical heart valve patients [12-20]. As can be seen from this brief list, signal processing algorithms of heart signals usually have the goal of isolating a random or non-deterministic component (such as a murmur or cavitation) from the comparatively loud backdrop of the beating of the heart itself.…”