基于CEEMDAN和小波熵的心音信号去噪算法研究

Research On Heart Sound Signal Denoisingalgorithm Based on the Combination of CEEMDAN and Wavelet Entropy

针对传统心音去噪算法可能丢失部分重要心音信息问题,提出了一种自适应噪声完备经验模态分解(CEEMDAN)和小波熵结合的心音信号去噪算法。算法通过CEEMDAN将心音信号自适应分解成多个本征模态函数(IMFs),基于各阶本征模态的能量分析判别信噪分界点,对含噪IMF分量采用小波熵自适应阈值去噪后,与信号IMF分量重构,得到去噪后的心音信号。仿真结果显示,在不同信噪比条件下,上述算法均能明显提高心音信号的信噪比,降低均方根误差,优于其它传统去噪算法,具有良好地抑制噪声能力。

The traditional heart sound denoising algorithm may lose part of important information while filtering out the noise.A heart sound signal denoising method based on the combination of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and wavelet entropy was proposed.The heart sound signals was were adaptively decomposed into multiple intrinsic modefunctions(IMFs)by CEEMDAN.Based on the energy,the boundary of signals and noises was determined.The wavelet entropy adaptive threshold denoising was adopted for the noisy IMFs.Finally,the denoised heart sound signals with the rest of the IMFs components was were reconstructed.The simulation results show that under different signal-to-noise ratio conditions,the algorithm in this paper can significantly improve the signal-to-noise ratio and root mean square error of the heart sound signal,is better than other traditional algorithms,and achieves a good noise suppression effect.