Bioinstrumentation and Clinical Engineering Research Group - GIBIC, Bioengineering Department, Engineering Faculty, Universidad de Antioquia UdeA Calle 70 No. 52-21, Medellín 050010, Colombia; LAAS-CNRS, Université de Toulouse CNRS 7 avenue du Colonel Roche, Toulouse 31400, France.
Bioinstrumentation and Clinical Engineering Research Group - GIBIC, Bioengineering Department, Engineering Faculty, Universidad de Antioquia UdeA Calle 70 No. 52-21, Medellín 050010, Colombia.
Comput Methods Programs Biomed. 2019 Dec;182:105034. doi: 10.1016/j.cmpb.2019.105034. Epub 2019 Aug 12.
Outpatient vital signs monitoring has a key role in medical diagnosis and treatment. However, ambulatory vital signs monitoring has great challenges to overcome, being the most important, the reduction of noise and Motion Artifacts, which hide essential information, particularly in Electrocardiographic signals. Despite efforts being made to reduce these artifacts, a comparative performance assessment of proposed techniques does not exist to the best of our knowledge and there are no enhancement level measurements obtained by the signals in the artifacts reduction. This article presents a new method based on Ensemble Average for the performance comparison of reported techniques for the processing and reduction of noise and artifacts in Electrocardiographic signals.
The comparison was done using a dataset composed by six synthetic noised Electrocardiographic signals and six real one acquired from healthy volunteers that intentionally introduced Motion Artifacts. Several techniques that have reported positive results in the enhancement of Electrocardiographic signals were applied to this dataset to compare their performance in the reduction of Motion Artifacts. The Signal-to-Noise Ratio and the Ensemble Average as a distortion measurement were used to compare the performance of algorithms to produce an enhanced signal.
In agreement to previous reports, all studied methods show a significant improvement of the Signal-to-Noise Ratio. Concerning the distortion of the waveform, although all methods caused high distortion on the enhanced signal waveform, the Wavelet-ICA method showed the best performance. The percentage of signal distortion introduced by denoising techniques was evaluated through the proposed Ensemble Average Electrocardiographic method.
It was found that the proposed method based on Ensemble Average offers a complementary way to measure the performance of denoising techniques when considering the introduced distortion in the waveform segments once the artifact reduction process was applied and not only the change in the Signal-to-Noise Ratio.
门诊生命体征监测在医疗诊断和治疗中起着关键作用。然而，动态生命体征监测面临着巨大的挑战，其中最重要的是减少噪声和运动伪影，这些伪影隐藏了重要信息，尤其是在心电图信号中。尽管已经做出了努力来减少这些伪影，但据我们所知，还没有对提出的技术进行比较性能评估，也没有通过信号在减少伪影方面获得的增强水平测量。本文提出了一种基于集合平均的新方法，用于对处理和减少心电图信号中的噪声和伪影的报告技术进行性能比较。
使用由六个合成噪声心电图信号和六个从健康志愿者那里获得的真实心电图信号组成的数据集进行比较，这些真实心电图信号故意引入了运动伪影。将几种在心电图信号增强方面报告有积极结果的技术应用于该数据集，以比较它们在减少运动伪影方面的性能。使用信噪比和集合平均作为失真测量来比较算法产生增强信号的性能。
与之前的报告一致，所有研究的方法都显示出信噪比的显著提高。关于波形的失真，尽管所有方法都对增强信号的波形造成了高失真，但小波-ICA 方法表现最好。通过提出的集合平均心电图方法评估了去噪技术引入的信号失真百分比。
发现基于集合平均的提出方法提供了一种补充方法，用于在应用伪影减少过程后考虑到引入的波形段失真，而不仅仅是信噪比的变化，来衡量去噪技术的性能。
