Phonocardiogram Signal Denoising Based on Nonnegative Matrix Factorization and Adaptive Contour Representation Computation
This letter introduces a new technique for phonocardiogram (PCG) signal denoising based on nonnegative 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 electroc...
Gespeichert in:
Veröffentlicht in: | IEEE signal processing letters 2018-10, Vol.25 (10), p.1475-1479 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | This letter introduces a new technique for phonocardiogram (PCG) signal denoising based on nonnegative 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 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 components that are subsequently used for signal retrieval. Numerical experiments conducted on a real database of noisy PCG signals, Signal Separation Evaluation Campaign (SiSEC2016), illustrate the superiority of the proposed method over state-of-the-art techniques. |
---|---|
ISSN: | 1070-9908 1558-2361 |
DOI: | 10.1109/LSP.2018.2865253 |