基于小波变换和经验模式分解的心音信号研究

针对经验模式分解（EMD）中的端点效应问题，本研究提出先用小波去除噪声干扰,再用EMD方法提取心音信号的特征。对于EMD的端点延拓，采用一种新的自适应波形匹配端点延拓方法。通过小波去噪，克服了直接运用EMD分解时无用频率分量带来的干扰，有效地减少EMD的分解层数，自适应波形匹配延拓方法充分考虑了心音信号的内在规律与端点处的变化趋势，较之传统的延拓方法更加合理。用所提出的方法对心音信号进行EMD分解，并用双阈值法对分解后的信号进行第一心音(S1)第二心音(S2)的定位分析，通过对40例心音信号定位分析，S1和S2的检出率分别达到97.05%和97.12%。表明该分析方法能够有效地抑制端点效应，提高EMD分解的准确性和时效性，为后续心音的分析提供准确的参考信息。

Research on Heart Sounds Signal Based on Wavelet Transform and Empirical Mode Decomposition

In order to solve the problem of end effect in empirical mode decomposition(EMD),this paper proposed method of extracting the features of heart sounds signal through empirical mode decomposition after wavelet denoising.A new method of data extending based on self-adaptive waveform matching was established for EMD.With wavelet denoising,the influence of useless frequency component on the later decomposition was decreased,thus effectively reduced the decomposition layers of the EMD.The self-adaptive waveform matching extending method considered both inner characteristics of signal and variation tendency of endpoint,hence it is more reasonable compared with the conventional extending method.Decompose 40 cases of heart sounds signal with the method,the location of the first and second heart sound was identified by explicit double threshold,the detection rate of S1 was up to 97.05%,and that of S2 was 97.12%.The result indicated that the proposed method could solve the end issue effectively,the speed and accuracy of the EMD were improved.