# SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising

arXiv: https://arxiv.org/abs/2402.03808 · HTML: https://mlsanity.com/paper/2402.03808/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 15 of 15)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [Sliced Score Matching: A Scalable Approach to Density and Score Estimation](https://mlsanity.com/paper/1905.07088/) (2019)
- [HINet: Half Instance Normalization Network for Image Restoration](https://mlsanity.com/paper/2105.06086/) (2021)
- [Noise2Music: Text-conditioned Music Generation with Diffusion Models](https://mlsanity.com/paper/2302.03917/) (2023)
- [Conditional Diffusion Probabilistic Model for Speech Enhancement](https://mlsanity.com/paper/2202.05256/) (2022)
- [A Study on Speech Enhancement Based on Diffusion Probabilistic Model](https://mlsanity.com/paper/2107.11876/) (2021)
- [EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising](https://mlsanity.com/paper/2009.11662/) (2020)
- [DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal](https://mlsanity.com/paper/2208.00542/) (2022)
- [DeepFilter: an ECG baseline wander removal filter using deep learning techniques](https://mlsanity.com/paper/2101.03423/) (2021)
- [ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks](https://mlsanity.com/paper/2210.13271/) (2022)

## Built on by (most cited first, 5 of 5)

- [RespDiff: An End-to-End Multi-scale RNN Diffusion Model for Respiratory Waveform Estimation from PPG Signals](https://mlsanity.com/paper/2410.04366/) (2024)
- [MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network](https://mlsanity.com/paper/2411.18902/) (2024)
- [Cyclostationary Phase Conditioning for Medical Time Series Diffusion](https://mlsanity.com/paper/2609.34965/) (2026)
- [MID: A Self-supervised Multimodal Iterative Denoising Framework](https://mlsanity.com/paper/2511.00997/) (2025)
- [SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising](https://mlsanity.com/paper/2609.11663/) (2026)

## Abstract (arXiv)

Surface electromyography (sEMG) recordings can be influenced by electrocardiogram (ECG) signals when the muscle being monitored is close to the heart. Several existing methods use signal-processing-based approaches, such as high-pass filter and template subtraction, while some derive mapping functions to restore clean sEMG signals from noisy sEMG (sEMG with ECG interference). Recently, the score-based diffusion model, a renowned generative model, has been introduced to generate high-quality and accurate samples with noisy input data. In this study, we proposed a novel approach, termed SDEMG, as a score-based diffusion model for sEMG signal denoising. To evaluate the proposed SDEMG approach, we conduct experiments to reduce noise in sEMG signals, employing data from an openly accessible source, the Non-Invasive Adaptive Prosthetics database, along with ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The experiment result indicates that SDEMG outperformed comparative methods and produced high-quality sEMG samples. The source code of SDEMG the framework is available at: https://github.com/tonyliu0910/SDEMG