Baldazzi Giulia, Sulas Eleonora, Urru Monica, Tumbarello Roberto, Raffo Luigi, Pani Danilo
DIEE, Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d'Armi, 09122 Cagliari, Italy; DIBRIS, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, 16145 Genoa, Italy.
DIEE, Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d'Armi, 09122 Cagliari, Italy.
Comput Methods Programs Biomed. 2020 Oct;195:105558. doi: 10.1016/j.cmpb.2020.105558. Epub 2020 May 26.
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.
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.
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).
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.
从无创腹部记录中检测出清晰且未失真的胎儿心电图（fECG）是一个开放性研究问题。即便使用了强大的fECG提取算法，多种生理和仪器噪声源仍会阻碍这一过程。小波去噪在生物医学信号处理中被广泛用于提高信噪比（SNR）。本研究旨在系统评估传统和非传统小波去噪方法对fECG信号进行后处理的效果，通过提供证据证明其在提高fECG SNR同时保留感兴趣信号形态方面的有效性。
考虑到平稳小波变换（SWT）和平稳小波包变换（SWPT）在信号子带分解中的不同粒度，选择了文献中的三个阈值，包括传统阈值（极小极大阈值和通用阈值）和非传统阈值。为此，首次通过尝试不同方法将非传统阈值应用于SWPT。结合fECG信号的特征研究了分解深度。使用公开可用的用于基准测试和研究的合成数据集和真实数据集，从降噪、胎儿QRS检测性能和fECG形态保留方面进行定量分析。
采用小波去噪方法通常能提高SNR。有趣的是，尽管SWT方法在子带分析中的粒度较粗，但在形态保留（p<0.04）和SNR（p<0.0003）方面优于SWPT方法。值得注意的是，首次用于fECG处理的Han等人的阈值带来了最佳的质量提升（p<0.003）。
我们系统分析的结果表明，在为无创fECG信号后处理选择和使用小波去噪时必须格外小心。特别是，尽管一般具有降噪能力，但信号形态可能会因小波方法的参数设置而发生显著改变。值得注意的是，小波包提供的更精细子带分解并不能提高处理质量。
