Electrocardiogram (ECG) artifact contamination often occurs in surface electromyography (sEMG) applications when the measured muscles are in proximity to the heart. Previous studies have developed and proposed various methods, such as high-pass filtering, template subtraction and so forth. However, these methods remain limited by the requirement of reference signals and distortion of original sEMG. This study proposed a novel denoising method to eliminate ECG artifacts from the single-channel sEMG signals using fully convolutional networks (FCN). The proposed method adopts a denoise autoencoder structure and powerful nonlinear mapping capability of neural networks for sEMG denoising. We compared the proposed approach with conventional approaches, including high-pass filters and template subtraction, on open datasets called the Non-Invasive Adaptive Prosthetics database and MIT-BIH normal sinus rhythm database. The experimental results demonstrate that the FCN outperforms conventional methods in sEMG reconstruction quality under a wide range of signal-to-noise ratio inputs.
翻译:当测量的肌肉接近心脏时,电动心电图(ECG)工艺污染经常出现在表面电磁学(sEMG)应用中。以前的研究已经制定并提出了各种方法,例如高通过滤器、模板减色等。然而,这些方法仍然受到参考信号要求和原始SEMG扭曲的限制。这项研究提出了一种新型的分层方法,用完全连动网络从单通道的SEMG信号中清除ECG工艺。拟议方法采用了一个双向自动编码器结构和神经网络的强大非线性绘图能力,用于SEMG的分流。我们将拟议方法与常规方法进行了比较,包括高通过过滤器和模板减色器,用于称为非侵入性调节预测数据库的开放数据集和MIT-BIH正常的关节率数据库。实验结果表明,FCN在信号到氮比率输入的广泛范围内,在SEMG的重建质量中将常规方法转化为信号-神经比率输入。