# MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal

arXiv: https://arxiv.org/abs/2409.18828 · HTML: https://mlsanity.com/paper/2409.18828/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022)
- [Jamba: A Hybrid Transformer-Mamba Language Model](https://mlsanity.com/paper/2403.19887/) (2024)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)
- [An Exponential Learning Rate Schedule for Deep Learning](https://mlsanity.com/paper/1910.07454/) (2019)
- [An Investigation of Incorporating Mamba for Speech Enhancement](https://mlsanity.com/paper/2405.06573/) (2024)
- [MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra](https://mlsanity.com/paper/2305.13686/) (2023)
- [SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model](https://mlsanity.com/paper/2405.11831/) (2024)
- [DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal](https://mlsanity.com/paper/2208.00542/) (2022)
- [Deep Recurrent Neural Networks for ECG Signal Denoising](https://mlsanity.com/paper/1807.11551/) (2018)
- [DeepFilter: an ECG baseline wander removal filter using deep learning techniques](https://mlsanity.com/paper/2101.03423/) (2021)
- [ECGMamba: Towards Efficient ECG Classification with BiSSM](https://mlsanity.com/paper/2406.10098/) (2024)

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

- [Cyclostationary Phase Conditioning for Medical Time Series Diffusion](https://mlsanity.com/paper/2609.34965/) (2026)
- [S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG](https://mlsanity.com/paper/2509.03066/) (2025)
- [Robust Photoplethysmography Signal Denoising via Mamba Networks](https://mlsanity.com/paper/2510.11058/) (2025)
- [SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising](https://mlsanity.com/paper/2609.11663/) (2026)
- [DR-net-Mamba: Selective State-Space Modeling for Long-Range ECG Time-Series Denoising](https://mlsanity.com/paper/2609.35634/) (2026)

## Abstract (arXiv)

Electrocardiogram (ECG) is an important non-invasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical interference or signal wandering, which reduces diagnostic accuracy. Various ECG denoising methods have been proposed, but most existing methods yield suboptimal performance under very noisy conditions or require several steps during inference, leading to latency during online processing. In this paper, we propose a novel ECG denoising model, namely Mamba-based ECG Enhancer (MECG-E), which leverages the Mamba architecture known for its fast inference and outstanding nonlinear mapping capabilities. Experimental results indicate that MECG-E surpasses several well-known existing models across multiple metrics under different noise conditions. Additionally, MECG-E requires less inference time than state-of-the-art diffusion-based ECG denoisers, demonstrating the model's functionality and efficiency.