Hossain Md-Billal, Bashar Syed Khairul, Lazaro Jesus, Reljin Natasa, Noh Yeonsik, Chon Ki H
Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Unit 3247 Storrs, CT 06269-3247, USA.
Aragon Institute for Engineering Research, University of Zaragoza, Spain.
Comput Methods Programs Biomed. 2021 Mar;200:105856. doi: 10.1016/j.cmpb.2020.105856. Epub 2020 Nov 21.
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.
心电图（ECG）被广泛用于检测和诊断诸如心房颤动等心律失常。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成分，如QRS波群，以便提供可靠的结果。然而，ECG常常受到噪声和伪迹的干扰，特别是如果它们是使用可穿戴传感器获得的，因此，准确识别QRS波群往往变得具有挑战性。大多数现有的去噪方法是通过向干净的ECG信号添加模拟噪声来验证的，并且它们没有考虑真实的含噪ECG信号。此外，它们中的许多方法依赖于模型和采样频率，并且需要大量的计算时间。
本文提出了一种使用变频复解调（VFCDM）算法的新型ECG去噪技术，该算法考虑了来自各种来源的噪声。我们使用VFCDM对受噪声污染的ECG信号进行子带分解，以去除噪声成分，从而重建质量更好的ECG。提出了一种自适应自动掩蔽方法，以便在去除不必要的噪声成分的同时保留QRS波群。最后，基于对噪声污染严重程度的自动识别，使用动态重建规则重建ECG。通过去除基线漂移并通过自适应均值滤波进行平滑处理，进一步提高了ECG信号质量。
在标准的MIT-BIH心律失常数据库上的评估结果表明，与文献中的研究相比，所提出的去噪技术具有卓越的去噪性能。此外，所提出的方法使用从噪声应激测试数据库（NSTDB）收集的真实噪声源以及来自包含大量肌肉伪迹的臂带式ECG设备的数据进行了验证。来自可穿戴臂带式ECG数据和NSTDB数据的结果表明，与一些最近现有的去噪算法相比，所提出的去噪方法在准确检测QRS波群和提高信噪比（SNR）方面提供了显著更好的性能。
详细的定性和定量分析表明，所提出的去噪方法在过滤ECG中存在的各种噪声方面具有鲁棒性。去噪后的臂带式ECG信号的QRS检测性能表明，所提出的去噪方法有可能增加可用的臂带式ECG数据量，因此，采用所提出的去噪方法的臂带式设备可用于心房颤动的长期监测。
