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[基于动态噪声估计的心音去噪]

[Heart sound denoising by dynamic noise estimation].

作者信息

Xu Chundong, Zhou Jing, Ying Dongwen, Xin Pengli

机构信息

School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, P.R.China.

School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, P.R.China;School of Electronic, Electronical, and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, P.R.China.

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Oct 25;37(5):775-785. doi: 10.7507/1001-5515.202002023.

Abstract

Denoising methods based on wavelet analysis and empirical mode decomposition cannot essentially track and eliminate noise, which usually cause distortion of heart sounds. Based on this problem, a heart sound denoising method based on improved minimum control recursive average and optimally modified log-spectral amplitude is proposed in this paper. The proposed method uses a short-time window to smoothly and dynamically track and estimate the minimum noise value. The noise estimation results are used to obtain the optimal spectrum gain function, and to minimize the noise by minimizing the difference between the clean heart sound and the estimated clean heart sound. In addition, combined with the subjective analysis of spectrum and the objective analysis of contribution to normal and abnormal heart sound classification system, we propose a more rigorous evaluation mechanism. The experimental results show that the proposed method effectively improves the time-frequency features, and obtains higher scores in the normal and abnormal heart sound classification systems. The proposed method can help medical workers to improve the accuracy of their diagnosis, and also has great reference value for the construction and application of computer-aided diagnosis system.

摘要

基于小波分析和经验模态分解的去噪方法本质上无法跟踪和消除噪声,这通常会导致心音失真。基于此问题,本文提出了一种基于改进的最小控制递归平均和最优修正对数谱幅度的心音去噪方法。该方法使用短时窗口来平滑动态地跟踪和估计最小噪声值。利用噪声估计结果获得最优谱增益函数,并通过最小化纯净心音与估计纯净心音之间的差异来最小化噪声。此外,结合频谱的主观分析以及对正常和异常心音分类系统贡献的客观分析,我们提出了一种更严格的评估机制。实验结果表明,该方法有效改善了时频特征,并且在正常和异常心音分类系统中获得了更高的分数。该方法有助于医护人员提高诊断准确性,对计算机辅助诊断系统的构建与应用也具有重要参考价值。

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