A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses.
A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses.
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DOI：
10.1371/journal.pone.0140783
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Scilingo EP
中科院分区：
文献类型：
--
作者：
Lanata A;Guidi A;Baragli P;Valenza G;Scilingo EP
本研究报告了一种新的方法，以检测和减少运动伪影(MA)在自由运动条件下采集的马的心电记录中的贡献。我们提出了一个综合心血管和运动信息的模型来估计MA的贡献。具体地说，通过可穿戴系统从七匹马身上连续获取心电和体力活动。这种系统采用完全集成的纺织电极来监测心电，并配备了三轴加速度计用于运动监测。在文献中，当噪声带宽与主源带宽重叠时，最常用的去除运动伪影的技术是自适应滤波。在这项研究中，我们提出了一种新的算法，以下称为平稳小波运动伪影减少(SWMAR)，其中使用平稳小波变换(SWT)分解算法来识别和消除马心电信号中的运动伪影。并与归一化最小均方自适应滤波技术(NLMSAF)进行了比较分析。在七个小时的记录上取得的结果显示，MA百分比(在应用所提出的算法之前和之后)减少了40%以上。此外，与NLMSAF的比较分析表明，对于相同的心电记录，MA百分比的减少更有利于SWMAR，具有统计学意义(p值为0.0.5)。
This study reports on a novel method to detect and reduce the contribution of movement artifact (MA) in electrocardiogram (ECG) recordings gathered from horses in free movement conditions. We propose a model that integrates cardiovascular and movement information to estimate the MA contribution. Specifically, ECG and physical activity are continuously acquired from seven horses through a wearable system. Such a system employs completely integrated textile electrodes to monitor ECG and is also equipped with a triaxial accelerometer for movement monitoring. In the literature, the most used technique to remove movement artifacts, when noise bandwidth overlaps the primary source bandwidth, is the adaptive filter. In this study we propose a new algorithm, hereinafter called Stationary Wavelet Movement Artifact Reduction (SWMAR), where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG signals in horses. A comparative analysis with the Normalized Least Mean Square Adaptive Filter technique (NLMSAF) is performed as well. Results achieved on seven hours of recordings showed a reduction greater than 40% of MA percentage (between before- and after- the application of the proposed algorithm). Moreover, the comparative analysis with the NLMSAF, applied to the same ECG recordings, showed a greater reduction of MA percentage in favour of SWMAR with a statistical significant difference (p–value < 0.0.5).