# Enhancing AI-Based ECG Delineation with Deep Learning Denoising Techniques

arXiv: https://arxiv.org/abs/2605.03183 · HTML: https://mlsanity.com/paper/2605.03183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 4 May 2026.

## Builds on (most cited first, 4 of 4)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (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)
- [ASCNet-ECG: Deep Autoencoder based Attention aware Skip Connection network for ECG filtering](https://mlsanity.com/paper/2303.15960/) (2023)

## Abstract (arXiv)

Evaluating canine electrocardiograms (ECGs) is challenging due to noise that can obscure clinically relevant cardiac electrical activity. Common sources of interference include respiration, muscle activity, poor lead contact, and external electrical artifacts. Classical signal denoising techniques, such as filtering and wavelet-based methods, struggle to suppress diverse noise patterns while preserving morphological features critical for accurate ECG delineation. We propose an autoencoder-based neural network model and training strategy for ECG denoising as a preprocessing step for canine ECG analysis. The model is trained to reconstruct clean cardiac signals from noisy inputs, enabling effective noise reduction without degrading diagnostically important waveforms. Our approach demonstrates strong performance across both noisy and clean ECG recordings, indicating robustness to varying signal conditions and suitability for downstream delineation tasks.