When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used as inputs to a neural network that outputs the estimate of SNR. While we used simulated EMG signals artificially contaminated with simulated ECG signals as the training data, the testing was done with simulated EMG signals artificially contaminated with real ECG signals. The results showed that the waveform length determined with raw EMG signals was the best feature for estimating SNR. It gave the highest average correlation coefficient of 0.9663. These results suggest that the waveform length could be deployed not only in EMG recognition systems but also in EMG signal quality measurements when the EMG signals are contaminated by ECG interference.

中文翻译：

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心电图信号污染的肌电图信号中的信噪比估计

当从躯干肌肉收集肌电图 (EMG) 信号时，它们可能会受到来自心脏活动的心电图 (ECG) 信号的干扰。在本文中，我们针对被 ECG 信号污染的 EMG 信号提出了一种新的信噪比 (SNR) 估计。我们使用评估 EMG 信号时常用的六个特征，即偏度、峰度、平均值、波形长度、过零和平均频率。这些特征是从原始 EMG 信号和离散平稳小波变换的细节系数中计算出来的。然后，这些特征被用作输出 SNR 估计值的神经网络的输入。虽然我们使用被模拟心电图信号人为污染的模拟 EMG 信号作为训练数据，测试是使用被真实心电图信号人为污染的模拟 EMG 信号完成的。结果表明，用原始 EMG 信号确定的波形长度是估计 SNR 的最佳特征。它给出了 0.9663 的最高平均相关系数。这些结果表明，当 EMG 信号被 ECG 干扰污染时，波形长度不仅可以用于 EMG 识别系统，还可以用于 EMG 信号质量测量。