# DeepFilter: an ECG baseline wander removal filter using deep learning techniques

arXiv: https://arxiv.org/abs/2101.03423 · HTML: https://mlsanity.com/paper/2101.03423/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2021.

## Built on by (most cited first, 11 of 11)

- [DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal](https://mlsanity.com/paper/2208.00542/) (2022)
- [A Comprehensive Survey on Generative Diffusion Models for Structured Data](https://mlsanity.com/paper/2306.04139/) (2023)
- [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)
- [MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network](https://mlsanity.com/paper/2411.18902/) (2024)
- [Deep Filtering with DNN, CNN and RNN](https://mlsanity.com/paper/2112.12616/) (2021)
- [Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network](https://mlsanity.com/paper/2202.05177/) (2022)
- [CLARAE: Clarity Preserving Reconstruction AutoEncoder for Denoising and Rhythm Classification of Intracardiac Electrograms](https://mlsanity.com/paper/2510.17821/) (2025)
- [Advancing time series completion via RFAMoE and MDFF](https://mlsanity.com/paper/2512.07873/) (2025)
- [Enhancing AI-Based ECG Delineation with Deep Learning Denoising Techniques](https://mlsanity.com/paper/2605.03183/) (2026)
- [DR-net-Mamba: Selective State-Space Modeling for Long-Range ECG Time-Series Denoising](https://mlsanity.com/paper/2609.35634/) (2026)

## Abstract (arXiv)

According to the World Health Organization, around 36% of the annual deaths are associated with cardiovascular diseases and 90% of heart attacks are preventable. Electrocardiogram signal analysis in ambulatory electrocardiography, during an exercise stress test, and in resting conditions allows cardiovascular disease diagnosis. However, during the acquisition, there is a variety of noises that may damage the signal quality thereby compromising their diagnostic potential. The baseline wander is one of the most undesirable noises. In this work, we propose a novel algorithm for BLW noise filtering using deep learning techniques. The model performance was validated using the QT Database and the MIT-BIH Noise Stress Test Database from Physionet. In addition, several comparative experiments were performed against state-of-the-art methods using traditional filtering procedures as well as deep learning techniques. The proposed approach yields the best results on four similarity metrics: the sum of squared distance, maximum absolute square, percentage of root distance, and cosine similarity with 4.29 (6.35) au, 0.34 (0.25) au, 45.35 (29.69) au and, 91.46 (8.61) au, respectively. The source code of this work, containing our method and related implementations, is freely available on Github.