# ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks

arXiv: https://arxiv.org/abs/2210.13271 · HTML: https://mlsanity.com/paper/2210.13271/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [T-GSA: Transformer with Gaussian-weighted self-attention for speech enhancement](https://mlsanity.com/paper/1910.06762/) (2019)
- [EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising](https://mlsanity.com/paper/2009.11662/) (2020)
- [Raw Waveform-based Speech Enhancement by Fully Convolutional Networks](https://mlsanity.com/paper/1703.02205/) (2017)
- [Deep Recurrent Neural Networks for ECG Signal Denoising](https://mlsanity.com/paper/1807.11551/) (2018)
- [Feature Learning from Incomplete EEG with Denoising Autoencoder](https://mlsanity.com/paper/1410.0818/) (2014)
- [EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer](https://mlsanity.com/paper/2109.04235/) (2021)

## Built on by (most cited first, 7 of 7)

- [TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement](https://mlsanity.com/paper/2410.03843/) (2024)
- [SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising](https://mlsanity.com/paper/2402.03808/) (2024)
- [MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network](https://mlsanity.com/paper/2411.18902/) (2024)
- [A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals](https://mlsanity.com/paper/2402.05482/) (2024)
- [Cyclostationary Phase Conditioning for Medical Time Series Diffusion](https://mlsanity.com/paper/2609.34965/) (2026)
- [SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising](https://mlsanity.com/paper/2609.11663/) (2026)
- [Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment](https://mlsanity.com/paper/2609.12724/) (2026)

## Abstract (arXiv)

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.