A robust ECG denoising technique using variable frequency complex demodulation.
A robust ECG denoising technique using variable frequency complex demodulation.
复制标题
一种基于变频复解调的鲁棒ECG去噪技术。
DOI：
10.1016/j.cmpb.2020.105856
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发表时间：
2021-03
影响因子：
6.1
通讯作者：
Chon KH
中科院分区：
文献类型：
--
作者：
Hossain MB;Bashar SK;Lazaro J;Reljin N;Noh Y;Chon KH
心电图（ECG）被广泛用于检测和诊断心律失常，例如心房颤动。大多数基于计算机的自动心脏异常检测算法需要准确识别ECG分量，例如QRS波群，以便提供可靠的结果。然而，ECG通常被噪声和伪影污染，特别是如果它们是使用可穿戴传感器获得的，因此，准确的QRS波群的识别通常变得具有挑战性。大多数现有的去噪方法都是使用添加到干净ECG信号中的模拟噪声进行验证的，并且它们没有考虑真实的噪声ECG信号。此外，它们中的许多是依赖于模型和采样频率的，并且需要大量的计算时间。本文提出了一种新的心电图去噪技术，使用变频复解调（VFCDM）算法，它考虑了各种来源的噪声。我们使用子带分解的噪声污染的心电信号，使用VFCDM去除噪声成分，以便更好的质量可以重建的心电图。提出了一种自适应自动掩蔽方法，以保留QRS波群，同时去除不必要的噪声成分。最后，使用基于自动识别噪声污染严重程度的动态重建规则来重建ECG。通过自适应均值滤波去除基线漂移和平滑，进一步改善了ECG信号质量。对标准MIT-BIH心律失常数据库的评估结果表明，与文献中的研究相比，所提出的去噪技术提供了上级去噪性能。此外，所提出的方法进行了验证，使用现实生活中的噪声源收集的噪声应力测试数据库（NSTDB）和数据从臂带心电图设备，其中包含显着的肌肉伪影。可穿戴臂带ECG数据和NSTDB数据的结果表明，与最近现有的一些去噪算法相比，所提出的去噪方法在准确的QRS波群检测和信噪比（SNR）改善方面提供了显着更好的性能。详细的定性和定量分析表明，所提出的去噪方法已被强大的滤除各种噪声存在的心电图。降噪后的臂带ECG信号的QRS检测性能表明，所提出的降噪方法具有增加可用臂带ECG数据量的潜力，因此，具有所提出的降噪方法的臂带设备可用于心房颤动的长期监测。
Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation.
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影响因子：
3.9
作者：
Chiang, Hsin-Tien;Hsieh, Yi-Yen;Chien, Shao-Yi
通讯作者：
Chien, Shao-Yi
影响因子：
2.2
作者：
Christov, II;Daskalov, IK
通讯作者：
Daskalov, IK
影响因子：
5.3
作者：
Gokgoz, Ercan;Subasi, Abdulhamit
通讯作者：
Subasi, Abdulhamit
影响因子：
4.6
作者：
Lee, Jinseok;Reyes, Bersain A.;Chon, Ki H.
通讯作者：
Chon, Ki H.
影响因子：
4.6
作者：
Chon, Ki H.;Dash, Shishir;Ju, Kihwan
通讯作者：
Ju, Kihwan