Review: Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform

Authors:
L. Hamza Cherif;S. M. Debbal;F. Bereksi-Reguig
Affiliations:
Genie-Biomedical Laboratory (GBM), Department of Electronics, Faculty of Science Engineering, University of Aboubekr Belkaid Tlemcen, BP119, Tlemcen 13000, Algeria;Genie-Biomedical Laboratory (GBM), Department of Electronics, Faculty of Science Engineering, University of Aboubekr Belkaid Tlemcen, BP119, Tlemcen 13000, Algeria;Genie-Biomedical Laboratory (GBM), Department of Electronics, Faculty of Science Engineering, University of Aboubekr Belkaid Tlemcen, BP119, Tlemcen 13000, Algeria
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

The phonocardiogram signal (PCG) can be utilized more efficiently by medical doctors when they are displayed visually, rather through a conventional stethoscope. This signal provides clinician with valuable diagnostic and prognostic information. Although the PCG signal analysis by auscultation is convenient as clinical tool, heart sound signals are so complex and non-stationary that they have a great difficulty to analyze in time or frequency domain. We have studied the extraction of features out of heart sounds in time-frequency (TF) domain for recognition of heart sounds through TF analysis. This article highlights the importance of the choice of wavelet analyzing wavelet and its order in the phonocardiogram signal analysis using the two versions of the wavelet transform: the discrete wavelet transform (DWT) and the packet wavelet transform (PWT). This analysis is based on the application of a large number of orthogonal and bi-orthogonal wavelets and whenever you measure the value of the average difference (in absolute value) between the original signal and the synthesis signal obtained by multiresolution analysis (AM). The performance of the discrete wavelet transform (DWT) and the packet wavelet transform (PWT) in the PCG signal analysis are evaluated and discussed in this paper. The results we obtain show the clinical usefulness of our extraction methods for recognition of heart sounds (or PCG signal).