Department of Electrical Engineering, University of Biskra, Biskra, Algeria.
Laboratory of LESIA, University of Biskra, Biskra, Algeria.
Biomed Tech (Berl). 2022 Jun 1;67(4):237-247. doi: 10.1515/bmt-2022-0006. Print 2022 Aug 26.
Although fetal phonocardiogram (fPCG) signals have become a good indicator for discovered heart disease, they may be contaminated by various noises that reduce the signals quality and the final diagnosis decision. Moreover, the noise may cause the risk of the data to misunderstand the heart signal and to misinterpret it. The main objective of this paper is to effectively remove noise from the fPCG signal to make it clinically feasible. So, we proposed a novel noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), wavelet threshold and Crow Search Algorithm (CSA). This noise reduction method, named ICEEMDAN-DWT-CSA, has three major advantages. They were, (i) A better suppress of mode mixing and a minimized number of IMFs, (ii) A choice of wavelet corresponding to the study signal proven by the literature and (iii) Selection of the optimal threshold value. Firstly, the noisy fPCG signal is decomposed into Intrinsic Mode Functions (IMFs) by the (ICEEMDAN). Each noisy IMFs were decomposed by the Discrete Wavelet Transform (DWT). Then, the optimal threshold value using the (CSA) technique is selected and the thresholding function is carried out in the detail's coefficients. Secondly, each denoised (IMFs) is reconstructed by applying the Inverse Discrete Wavelet Transform (IDWT). Finally, all these denoised (IMFs) are combined to get the denoised fPCG signal. The performance of the proposed method has been evaluated by Signal to Noise Ratio (SNR), Mean Square Error (MSE) and the Correlation Coefficient (COR). The experiment gave a better result than some standard methods.
虽然胎儿心音图（fPCG）信号已成为发现心脏病的良好指标，但它们可能会受到各种噪声的污染，从而降低信号质量和最终诊断决策的准确性。此外，这些噪声可能导致数据误解心音并对其产生误判。本文的主要目的是有效去除 fPCG 信号中的噪声，使其在临床上可行。因此，我们提出了一种基于改进完全集合经验模态分解与自适应噪声（ICEEMDAN）、小波阈值和 Crow Search Algorithm（CSA）的新型降噪方法。这种降噪方法命名为 ICEEMDAN-DWT-CSA，具有三个主要优点。分别是：（i）更好地抑制模态混合和最小化 IMFs 的数量，（ii）选择与文献中研究信号对应的小波，（iii）选择最优阈值。首先，通过（ICEEMDAN）将有噪声的 fPCG 信号分解为固有模态函数（IMFs）。每个有噪声的 IMFs 都通过离散小波变换（DWT）进行分解。然后，使用（CSA）技术选择最优阈值，并在细节系数中执行阈值函数。其次，通过应用逆离散小波变换（IDWT）对每个去噪的（IMFs）进行重构。最后，将所有这些去噪的（IMFs）组合以获得去噪的 fPCG 信号。通过信噪比（SNR）、均方误差（MSE）和相关系数（COR）评估了所提出方法的性能。实验结果优于一些标准方法。
