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首页> 外文期刊>Future generation computer systems >A multi-stage denoising framework for ambulatory ECG signal based on domain knowledge and motion artifact detection
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A multi-stage denoising framework for ambulatory ECG signal based on domain knowledge and motion artifact detection

机译：基于域知识和运动伪影检测的动态ECG信号的多级去噪框架

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摘要

Electrocardiogram (ECG) acquired by wearable devices is increasingly used for healthcare applications. However, the ECG signals are severely corrupted by various noises (e.g. baseline wander and motion artifacts) in daily activities, resulting in unreliable or wrong detection of heart problems and hindering the automatic ECG analysis. Because of the overlap of different kinds of noises in the time and frequency domains, noise removal is a difficult task for ambulatory ECG signals. Especially, motion artifacts with variable frequencies and amplitudes pose a great challenge to ECG denoising. To address this problem, we propose a multi-stage ECG denoising framework concentrating on the detection of motion artifact based on domain knowledge. In the framework, motion artifact candidates are first located by noise-adaptive thresholding. Then we use multiple metrics combined with decision rules to find actual motion artifacts and suppress them by local scaling and morphological filtering. The complete ensemble empirical mode decomposition (CEEMD) and wavelet transform are employed to remove baseline wander and high-frequency noise, respectively. The proposed method is evaluated on the MIT-BIH arrhythmia database, the TELE database, and the Sport database. The results on the MIT-BIH database show that the proposed method achieved statistically significant improvement of signal-to-noise ratio (SNR) ranging from 7% to 25% compared with other approaches. The results also demonstrate that the proposed method effectively suppressed QRS-like motion artifacts and hence decreased false positives generated by the QRS detector, which is important for clinical diagnosis.
机译：可穿戴设备获取的心电图（ECG）越来越多地用于医疗保健应用。然而，日常活动中的各种噪声（例如基线漫游和运动伪影）严重破坏了ECG信号，导致心脏问题的不可靠或错误的检测，并阻碍自动ECG分析。由于时间和频率域不同种类的不同类型的噪声重叠，噪声去除是动态ECG信号的难以任务。特别是，具有可变频率和振幅的运动伪影对心电图造成了巨大挑战。为了解决这个问题，我们提出了一种基于域知识的运动伪影检测的多阶段ECG去噪框架。在框架中，运动伪影候选首先通过噪声自适应阈值定位。然后，我们使用多个指标与决策规则相结合，以找到实际的运动伪影并通过本地缩放和形态过滤抑制它们。完整的集合经验模式分解（CeeMD）和小波变换分别用于移除基线漫游和高频噪声。该方法在MIT-BIH心律失常数据库，远程数据库和运动数据库上进行评估。 MIT-BIH数据库的结果表明，与其他方法相比，该方法的统计上显着提高了7％至25％的信噪比（SNR）。结果还表明，所提出的方法有效地抑制了QRS样运动伪影，因此降低了QRS检测器产生的误报，这对于临床诊断很重要。

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  • 来源
    《Future generation computer systems》 |2021年第3期|103-116|共14页
  • 作者

    Xiaoyun Xie; Hui Liu; Minglei Shu; Qing Zhu; Anpeng Huang; Xiangpu Kong; Yinglong Wang;

  • 作者单位

    Shandong Artificial Intelligence Institute Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China;

    Shandong Artificial Intelligence Institute Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China;

    Shandong Artificial Intelligence Institute Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China;

    Qilu Hospital of Shangdong University Jinan 250012 China;

    National Institute of Health Data Science Peking University Beijing 100871 China;

    WeDoctor Group Co. Ltd. Hangzhou 311200 China;

    Shandong Artificial Intelligence Institute Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Electrocardiogram (ECG); Domain knowledge; Decision rule; Morphological filtering; Motion artifact; QRS detection;

    机译：心电图（ECG）;领域知识;决策规则;形态过滤;运动伪影;QRS检测;

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