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首页> 外文期刊>Signal Processing Letters, IEEE >Phonocardiogram Signal Denoising Based on Nonnegative Matrix Factorization and Adaptive Contour Representation Computation
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Phonocardiogram Signal Denoising Based on Nonnegative Matrix Factorization and Adaptive Contour Representation Computation

机译:基于非负矩阵分解和自适应轮廓表示计算的心电图信号降噪

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摘要

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.
机译:这封信介绍了一种基于其频谱图的非负矩阵分解(NMF)和其短时傅立叶变换(STFT)的自适应轮廓表示计算(ACRC)的心电图(PCG)信号降噪新技术。更准确地说,首先使用PCG上的NMF和同步心电图频谱图来过滤PCG产生的高能噪声。然后,对所得信号的STFT的低通滤波版本执行ACRC,以识别随后用于信号检索的相关时频分量。在嘈杂的PCG信号的真实数据库(信号分离评估运动(SiSEC2016))上进行的数值实验说明了该方法相对于最新技术的优越性。

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