论文标题
使用完全卷积网络从单渠道表面EMG中删除ECG人工图形
ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks
论文作者
论文摘要
当测得的肌肉靠近心脏时，心电图（ECG）伪影污染通常发生在表面肌电图（SEMG）应用中。先前的研究已经开发并提出了各种方法，例如高通滤波，模板减法等。但是，这些方法仍然受到参考信号和原始SEMG失真的要求的限制。这项研究提出了一种新型的剥离方法，以使用完全卷积网络（FCN）消除单渠道SEMG信号中的心电图伪像。所提出的方法采用了Denoise自动编码器结构和神经网络的强大非线性映射能力进行SEMG DENOISISISIS。我们将提出的方法与传统方法（包括高通滤波器和模板减法）进行了比较，称为非侵入性自适应假体数据库和MIT-BIH正常窦性节奏数据库。实验结果表明，在广泛的信噪比输入下，FCN的表现优于SEMG重建质量的常规方法。
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.
