Background and Objective

The detection of a clean and undistorted foetal electrocardiogram (fECG) from non-invasive abdominal recordings is an open research issue. Several physiological and instrumental noise sources hamper this process, even after that powerful fECG extraction algorithms have been used. Wavelet denoising is widely used for the improvement of the SNR in biomedical signal processing. This work aims to systematically assess conventional and unconventional wavelet denoising approaches for the post-processing of fECG signals by providing evidence of their effectiveness in improving fECG SNR while preserving the morphology of the signal of interest.

Methods

The stationary wavelet transform (SWT) and the stationary wavelet packet transform (SWPT) were considered, due to their different granularity in the sub-band decomposition of the signal. Three thresholds from the literature, either conventional (Minimax and Universal) and unconventional, were selected. To this aim, the unconventional one was adapted for the first time to SWPT by trying different approaches. The decomposition depth was studied in relation to the characteristics of the fECG signal. Synthetic and real datasets, publicly available for benchmarking and research, were used for quantitative analysis in terms of noise reduction, foetal QRS detection performance and preservation of fECG morphology.

Results

The adoption of wavelet denoising approaches generally improved the SNR. Interestingly, the SWT methods outperformed the SWPT ones in morphology preservation (p<0.04) and SNR (p<0.0003), despite their coarser granularity in the sub-band analysis. Remarkably, the Han et al. threshold, adopted for the first time for fECG processing, provided the best quality improvement (p<0.003).

Conclusions

The findings of our systematic analysis suggest that particular care must be taken when selecting and using wavelet denoising for non-invasive fECG signal post-processing. In particular, despite the general noise reduction capability, signal morphology can be significantly altered on the basis of the parameterization of the wavelet methods. Remarkably, the adoption of a finer sub-band decomposition provided by the wavelet packet was not able to improve the quality of the processing.

中文翻译：

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小波降噪是无创胎儿心电图的一种后处理增强方法。

背景与目的

从无创腹部记录中检测出干净无畸变的胎儿心电图（fECG）是一个开放的研究问题。即使使用了功能强大的fECG提取算法，也有几种生理和仪器噪声源阻碍了该过程。小波去噪被广泛用于改善生物医学信号处理中的SNR。这项工作旨在通过提供fECG信号在提高fECG SNR的有效性的同时保留所关注信号形态的有效性的证据，来系统评估fECG信号后处理的常规和非常规小波去噪方法。

方法

考虑了平稳小波变换（SWT）和平稳小波包变换（SWPT），因为它们在信号的子带分解中具有不同的粒度。从文献中选择了三个阈值，即常规阈值（Minimax和Universal）和非常规阈值。为了这个目标，通过尝试不同的方法首次将非常规方法改编成SWPT。研究了与fECG信号特性有关的分解深度。公开可用以进行基准测试和研究的合成和真实数据集用于降噪，胎儿QRS检测性能和fECG形态保存方面的定量分析。

结果

小波去噪方法的采用通常改善了SNR。有趣的是，尽管在子带分析中粒度较小，但SWT方法在形态保存（p <0.04）和SNR（p <0.0003）方面优于SWPT方法。值得注意的是，Han等。fECG处理首次采用的阈值可提供最佳质量改进（p <0.003）。

结论

我们系统分析的结果表明，在选择和使用小波去噪进行非侵入性fECG信号后处理时，必须格外小心。特别是，尽管具有一般的降噪能力，但可以根据小波方法的参数化显着改变信号形态。值得注意的是，采用由小波包提供的更精细的子带分解并不能提高处理质量。