# SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising

arXiv: https://arxiv.org/abs/2609.11663 · HTML: https://mlsanity.com/paper/2609.11663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Physiological signal modeling, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 10 Sep 2026.

## Placed in

- State-space sequence models (S4, Mamba) / [Physiological signal modeling](https://mlsanity.com/ssm/physiological-signal-modeling/)

## Builds on (most cited first, 10 of 10)

- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement](https://mlsanity.com/paper/2104.03538/) (2021)
- [MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra](https://mlsanity.com/paper/2305.13686/) (2023)
- [Speech Denoising with Deep Feature Losses](https://mlsanity.com/paper/1806.10522/) (2018)
- [ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks](https://mlsanity.com/paper/2210.13271/) (2022)
- [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)
- [MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal](https://mlsanity.com/paper/2409.18828/) (2024)
- [DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse](https://mlsanity.com/paper/2312.09417/) (2023)
- [MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network](https://mlsanity.com/paper/2411.18902/) (2024)

## Abstract (arXiv)

Electrocardiogram (ECG) artifact contamination frequently occurs in surface electromyography (sEMG) when muscles are recorded near the heart. Existing neural network (NN)-based approaches typically perform waveform-level end-to-end denoising with pointwise losses, but often fail to preserve the spectral structures of sEMG. In addition, these models do not exploit the prior knowledge that ECG mainly distorts the low-frequency band of sEMG. We propose SSEMG-Net, a spectrogram-domain model that explicitly captures sEMG-ECG interactions using a bidirectional Mamba backbone and a dual-head decoder for magnitude masking and wrapped-phase estimation. The magnitude masking module is physiologically aware, restricting denoising to the low-frequency sub-band where ECG activity predominates. Experimental results demonstrate that SSEMG-Net achieves superior signal quality and lower feature-extraction error compared to prior methods, underscoring its potential for clinical applications where preserving sEMG spectral features is essential.