IEEE J Biomed Health Inform. 2018 May;22(3):775-784. doi: 10.1109/JBHI.2017.2667685. Epub 2017 Feb 13.
The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information, which are useful for recognition of heart diseases. The unwanted signals are produced mainly by lungs and surrounding environment. In this paper, a novel heart sound denoising technique has been introduced based on a combined framework of wavelet packet transform and singular value decomposition (SVD). The most informative node of the wavelet tree is selected on the criteria of mutual information measurement. Next, the coefficient corresponding to the selected node is processed by the SVD technique to suppress noisy component from heart sound signal. To justify the efficacy of the proposed technique, several experiments have been conducted with heart sound dataset, including normal and pathological cases at different signal to noise ratios. The significance of the method is validated by statistical analysis of the results. The biological information preserved in denoised heart sound signal is evaluated by the k-means clustering algorithm. The overall results show that the proposed method is superior than the baseline methods.
心音解释中主要遇到的困难是噪声干扰。污染的噪声掩盖了相关信息，这些信息对于识别心脏病是有用的。不需要的信号主要由肺部和周围环境产生。在本文中，我们提出了一种基于小波包变换和奇异值分解（SVD）相结合框架的新型心音去噪技术。在互信息测量的标准下，选择了小波树中最具信息量的节点。接下来，用 SVD 技术处理对应于所选节点的系数，以抑制心音信号中的噪声分量。为了验证所提出技术的有效性，我们使用心音数据集进行了几项实验，包括不同信噪比的正常和病理情况。通过对结果的统计分析验证了该方法的意义。通过 k-均值聚类算法评估去噪心音信号中保存的生物信息。整体结果表明，该方法优于基线方法。
