Electromyograms (EMG signals) may be contaminated by electrocardiographic (ECG) signals that cannot be easily separated with traditional filters, because both signals have some overlapping spectral components. Therefore, the first challenge encountered in signal processing is to extract the ECG noise from the EMG signal. In this study, the EMG, mixed with different degrees of noise (ECG), is simulated to investigate the variations of the EMG features. Simulated data were derived from the MIT-BIH Noise Stress Test (NSTD) Database. Two EMG and four ECG data were composed with four EMG/ECG SNR to 32 simulated signals. Following Pan-Tompkins R-peak detection, four ECG removal methods were used to remove ECG with different compensation algorithms to obtain the denoised EMG signal. A total of 13 time-domain and four frequency-domain EMG features were calculated from the denoised EMG. In addition, the similarity of denoised EMG features compared to clean EMG was also evaluated. Our results showed that with the ratio EMG/ECG SNR = 10 and 20, the ECG can be almost ignored, and the similarity of EMG features is close to 1. When EMG/ECG SNR = 1 and 2, there is a large variation of EMG features. The results of our simulation study would be beneficial for understanding the variations of EMG features upon the different EMG/ECG SNR.

中文翻译：

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心电图噪声随肌电图参数变化

肌电图（EMG 信号）可能会受到心电图（ECG）信号的污染，这些信号无法用传统滤波器轻松分离，因为这两种信号都有一些重叠的频谱分量。因此，信号处理中遇到的第一个挑战是从 EMG 信号中提取 ECG 噪声。在这项研究中，EMG 与不同程度的噪声 (ECG) 混合，被模拟以研究 EMG 特征的变化。模拟数据来自 MIT-BIH 噪声压力测试 (NSTD) 数据库。两个 EMG 和四个 ECG 数据由四个 EMG/ECG SNR 和 32 个模拟信号组成。在泛汤普金斯 R 峰检测之后，使用四种心电图去除方法，通过不同的补偿算法去除心电图，以获得去噪的 EMG 信号。从去噪 EMG 中计算出总共 13 个时域和 4 个频域 EMG 特征。此外，还评估了去噪 EMG 特征与干净 EMG 相比的相似性。我们的结果表明，当 EMG/ECG SNR = 10 和 20 时，ECG 几乎可以忽略不计，EMG 特征的相似度接近于 1。当 EMG/ECG SNR = 1 和 2 时，存在较大的变化肌电图特征。我们的模拟研究结果将有助于理解 EMG 特征在不同 EMG/ECG SNR 上的变化。EMG 特征有很大的变化。我们的模拟研究结果将有助于理解 EMG 特征在不同 EMG/ECG SNR 上的变化。EMG 特征有很大的变化。我们的模拟研究结果将有助于理解 EMG 特征在不同 EMG/ECG SNR 上的变化。