Heart Sound Denoising Based on CEEMDAN and Autocorrelation Function
To effectively remove noise from heart sound signals,a heart sound denoising algorithm based on CEEMDAN(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)and the autocorrelation function is proposed.First,the noisy heart sound signal is decomposed into Intrinsic Mode Function(IMF)components with different scale features through CEEMDAN.Next,the difference in properties between the noise and the heart sound autocorrelation functions is used to define the signal-to-noise threshold for the IMF components.Finally,mean filtering is applied to the IMF components dominated by noise,and the denoised signal is reconstructed by combining these with the IMF components dominated by heart sound.Experiments show that,at various noise levels,the proposed algorithm achieves the highest Signal-to-Noise Ratio(SNR)and the lowest Root Mean Square Error(RMSE)compared to wavelet soft and hard threshold denoising algorithms and the CEEMDAN denoising algorithm.It effectively removes noise while preserving the essential information in the heart sound signal.
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