PCG Signal Denoising and Selection Optimal Level and Order - Rouis, Mohamed;Benhassine, Nasser edinne
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The recorded phonocardiogram PCG signal is often contaminated by different types of noises that can be seen in the frequency band of PCG signal, which may change the characteristics of this signal. Wavelet transform has become an essential tool for many applications but its effectiveness is influenced by main parameters. Determination of mother wavelet function and decomposition level (DL) is important key factors to demonstrate the advantages of wavelet denoising . So, selection of optimal mother wavelet with DL is a main challenge to current algorithms . This work proposes a new approach for…mehr

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Produktbeschreibung
The recorded phonocardiogram PCG signal is often contaminated by different types of noises that can be seen in the frequency band of PCG signal, which may change the characteristics of this signal. Wavelet transform has become an essential tool for many applications but its effectiveness is influenced by main parameters. Determination of mother wavelet function and decomposition level (DL) is important key factors to demonstrate the advantages of wavelet denoising . So, selection of optimal mother wavelet with DL is a main challenge to current algorithms . This work proposes a new approach for finding an optimal DL and optimal mother wavelet for PCG signals denoising according to four criteria which are: signal-to-noise ratio (SNR), mean square error (MSE), percentage root-mean-square difference (PRD) and the structure similarity index measure (SSIM) . Our approach is designed to tackle the problems of noise and variability caused by PCG acquisition in a real clinical environmentfor different categories of patients.
Autorenporträt
Mohamed Rouis Had his Ph.D. degree in electronic engineering in 2019 from the University of Biskra. Field of research signal processing. Nasser edinne Benhassine Had his Ph.D. degree in Exact Science and Informatics in 2019 from theUniversity of Djelfa. Field of research image Processing and machines learning.