基于改进经验模态分解与能量包络的S1/S2提取

An Improved Empirical Mode Decomposition Algorithm for Phonocardiogram Signal De-noising and Its Application in S1/S2 Extraction

本文提出了一种改进型的经验模态分解算法用于心音图(PCG)信号去噪,结合PCG的规则平均Shannon能量包络算法,可有效提取PCG中的S1/S2成分。首先,通过小波变换和经验模态分解结合算法对PCG信号进行滤波预处理;然后,提取预处理后PCG信号的固有模函数(IMF)时域、频域特性及能量包络;最后,结合信号的Shannon能量包络和IMF相关特性准确定位出S1和S2。运用该方法对30例PCG信号进行测试,得到S1/S2成分的综合识别率达99.75%。实验结果表明,本文算法运用于S1/S2成分提取具有较好的效果,为进一步研究心音身份识别奠定基础。

In this paper, an improved empirical mode decomposition （EMD） algorithm for phonocardiogram （PCG） signal de-noising is proposed. Based on PCG signal processing theory, the S1/$2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. Firstly, by applying EMD-Wavelet algorithm for pre processing, the PCG signal was well filtered. Then, the filtered PCG signal was saved and applied in the following processing steps. Secondly, time domain features, frequency domain features and energy envelope of the each intrinsic mode function＇s （IMF） were computed. Based on the time frequency domain features of PCG＇s IMF components which were extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components were pinpointed accurately. Meanwhile, a detecting fixed method, which was based on the time domain processing, was proposed to amend the detection results. Finally, to test the performance of the algo- rithm proposed in this paper, a series of experiments was contrived. The experiments with thirty samples were tested for validating the effectiveness of the new method. Results of test experiments revealed that the accuracy for recog- nizing S1/S2 components was as high as 99.75 %. Comparing the results of the method proposed in this paper with those of traditional algorithm, the detection accuracy was increased by 5.56 %. The detection results showed that the algorithm described in this paper was effective and accurate. The work described in this paper will be utilized in the further studying on identity recognition.