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Phonocardiogram Signal Denoising Based on Non-negative Matrix Factorization and Adaptive Contour Representation Computation

Duong-Hung Pham 1, 2 Sylvain Meignen 1 Nafissa Dia 3 Julie Fontecave-Jallon 3 Bertrand Rivet 4
1 CVGI - Calcul des Variations, Géométrie, Image
LJK - Laboratoire Jean Kuntzmann
2 IRIT-MINDS - CoMputational imagINg anD viSion
IRIT - Institut de recherche en informatique de Toulouse
3 TIMC-IMAG-PRETA - Physiologie cardio-Respiratoire Expérimentale Théorique et Appliquée
TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525
4 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing
GIPSA-DIS - Département Images et Signal
Abstract : —This letter introduces a new technique for phono-cardiogram (PCG) signal denoising based non-negative matrix factorization (NMF) of its spectrogram and adaptive contour representation computation (ACRC) of its short-time Fourier transform (STFT). More precisely, NMFs on PCG and synchronous electrocardiogram (ECG) spectrograms are first used to filter out high-energy noises from PCG. Then, ACRC is performed on a low-pass filtered version of the STFT of the resulting signal to identify relevant time-frequency (TF) components which are subsequently used for signal retrieval. Numerical experiments conducted on a real database of noisy PCG signals (SiSEC2016) illustrate the superiority of the proposed method over state-of-the-art techniques.
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Duong-Hung Pham, Sylvain Meignen, Nafissa Dia, Julie Fontecave-Jallon, Bertrand Rivet. Phonocardiogram Signal Denoising Based on Non-negative Matrix Factorization and Adaptive Contour Representation Computation. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2018, 25 (10), pp.1475-1479. ⟨10.1109/LSP.2018.2865253⟩. ⟨hal-01855855⟩

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