Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury.
Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury.
复制标题
DOI：
10.4236/jbise.2013.67a2001
复制
发表时间：
2013-07-18
期刊：
影响因子：
--
通讯作者：
Ovechkin A
中科院分区：
文献类型：
--
作者：
Nitzken M;Bajaj N;Aslan S;Gimel'farb G;El-Baz A;Ovechkin A
表面肌电图（EMG）是临床实践和研究中用于评估运动功能的标准方法，以帮助诊断人类和动物模型中的神经肌肉病理学。从参与呼吸活动的躯干肌肉记录的EMG可以用作患有脊髓损伤（SCI）或与运动控制缺陷相关的其他疾病的患者的呼吸运动功能的直接测量。然而，从这些肌肉记录的EMG电位经常被心脏诱发的心电图（ECG）信号污染。这些伪影的消除在呼吸肌电活动的精确测量中起着关键作用。本研究旨在寻找一种最佳的方法来消除肌电图记录中的ECG伪影。常规的全局滤波可以用于减少ECG诱导的伪影。然而，这种方法可以改变EMG信号并改变生理相关信息。我们假设，与全局滤波不同，ECG伪影的局部去除不会改变原始EMG信号。我们开发了一种方法来消除ECG伪影，而不改变的幅度和频率分量的EMG信号，通过使用外部记录的ECG信号作为掩模，以定位区域内的ECG尖峰的EMG数据。这些段包含心电图棘波分解成128个子小波的定制缩放Morlet小波变换。去除心电峰电位处的子小波，重建去噪后的肌电信号。利用数学模拟的合成信号和从SCI患者获得的肌电信号证明了所提出的方法的有效性。我们比较了这种方法，全局，陷波和自适应滤波器之间的均方根误差和相对方差变化。结果表明，基于局部小波的滤波具有不引入误差的优点，可以准确地去除肌电信号中的ECG伪影。
Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.