一种改进的经验模态分解-小波算法心音信号去噪及其在一、二心音提取中的应用

Haozhou Sun, Wei Chen, Jing-yan Gong
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引用次数: 25

摘要

本文提出了一种改进的emd -小波算法用于心音图信号去噪。基于PCG信号处理理论,结合改进的emd -小波算法和Shannon能量包络算法提取S1/S2分量。采用小波变换算法和经验模态分解(EMD)进行预处理,对PCG信号进行了较好的滤波。基于EMD算法提取的PCG IMF分量的时频域特征,结合PCG的能量包络,精确定位出S1/S2分量。30个样本的实验结果表明,该算法对S1/S2分量的识别准确率高达99.74%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved empirical mode decomposition-wavelet algorithm for phonocardiogram signal denoising and its application in the first and second heart sound extraction
In this paper, an improved EMD-Wavelet algorithm for PCG (Phonocardiogram) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. By applying the wavelet transform algorithm and EMD (Empirical Mode Decomposition) for pre-procession, the PCG signal is well filtered. Based on the time frequency domain features of PCG's IMF components which is extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components are pinpointed accurately. Experiments of thirty samples illustrate the proposed algorithm, which reveals that the accuracy for recognition of S1/S2 components is as high as 99.74%.
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