IEEE Trans Neural Syst Rehabil Eng. 2023;31:3047-3054. doi: 10.1109/TNSRE.2023.3294011. Epub 2023 Jul 28.
The aims of this study are to characterize the contamination of EMG signals by artifacts generated by the delivery of spinal cord transcutaneous stimulation (scTS) and to evaluate the performance of an Artifact Adaptive Ideal Filtering (AA-IF) technique to remove scTS artifacts from EMG signals.
In five participants with spinal cord injury (SCI), scTS was delivered at different combinations of intensity (from 20 to 55 mA) and frequencies (from 30 to 60 Hz) while Biceps Brachii (BB) and Triceps Brachii (TB) muscles were at rest or voluntarily activated. Using a Fast Fourier Transform (FFT), we characterized peak amplitude of scTS artifacts and boundaries of contaminated frequency bands in the EMG signals recorded from BB and TB muscles. Then, we applied the AA-IF technique and the empirical mode decomposition Butterworth filtering method (EMD-BF) to identify and remove scTS artifacts. Finally, we compared the content of the FFT that was preserved and the root mean square of the EMG signals (EMGrms) following application of the AA-IF and EMD-BF techniques.
Frequency bands of ~2Hz width were contaminated by scTS artifact at frequencies nearby the main frequency set for the stimulator and its harmonics. The width of the frequency bands contaminated by scTS artifacts increased with current intensity delivered using scTS ( [Formula: see text]), was lower when EMG signals were recorded during voluntary contractions compared to rest ( [Formula: see text]), and was larger in BB muscle compared to TB muscle ( [Formula: see text]). A larger portion of the FFT was preserved using the AA-IF technique compared to the EMD-BF technique (96±5% vs. 75±6%, [Formula: see text]).
The AA-IF technique allows for a precise identification of the frequency bands contaminated by scTS artifacts and ultimately preserves a larger amount of uncontaminated content from the EMG signals.
本研究旨在描述经皮脊髓电刺激（scTS）诱发的肌电图（EMG）信号伪迹的特征，并评估自适应理想滤波（AA-IF）技术从 EMG 信号中去除 scTS 伪迹的性能。
在 5 名脊髓损伤（SCI）患者中，在不同的强度（20-55mA）和频率（30-60Hz）组合下进行 scTS 刺激，同时肱二头肌（BB）和肱三头肌（TB）肌肉处于休息或主动激活状态。使用快速傅里叶变换（FFT），我们描述了 BB 和 TB 肌肉记录的 EMG 信号中 scTS 伪迹的峰值幅度和受污染频段的边界。然后，我们应用 AA-IF 技术和经验模态分解巴特沃斯滤波方法（EMD-BF）来识别和去除 scTS 伪迹。最后，我们比较了应用 AA-IF 和 EMD-BF 技术后保留的 FFT 内容和 EMG 信号的均方根（EMGrms）。
scTS 伪迹在刺激器的主频及其谐波附近的频率上污染了约 2Hz 宽的频段。随着 scTS 传递的电流强度增加，受污染的频段变宽（[公式：见正文]），与休息相比，在主动收缩时记录的 EMG 信号变窄（[公式：见正文]），并且在 BB 肌肉中比 TB 肌肉宽（[公式：见正文]）。与 EMD-BF 技术相比，AA-IF 技术保留了更多的 FFT（96±5%比 75±6%，[公式：见正文]）。
AA-IF 技术可以精确识别受 scTS 伪迹污染的频段，并最终从 EMG 信号中保留更多未受污染的内容。
