# Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels

arXiv: https://arxiv.org/abs/2208.08853 · HTML: https://mlsanity.com/paper/2208.08853/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 8 Aug 2022.

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

- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [SSD: A Unified Framework for Self-Supervised Outlier Detection](https://mlsanity.com/paper/2103.12051/) (2021)
- [Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?](https://mlsanity.com/paper/2105.09270/) (2021)
- [Task Agnostic and Post-hoc Unseen Distribution Detection](https://mlsanity.com/paper/2207.13083/) (2022)

## Built on by (most cited first, 2 of 2)

- [Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection](https://mlsanity.com/paper/2506.11815/) (2025)
- [Investigating the Generalizability of ECG Noise Detection Across Diverse Data Sources and Noise Types](https://mlsanity.com/paper/2502.14522/) (2025)

## Abstract (arXiv)

Electrocardiogram (ECG) signals are beneficial in diagnosing cardiovascular diseases, which are one of the leading causes of death. However, they are often contaminated by noise artifacts and affect the automatic and manual diagnosis process. Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time. To address this limitation, we present a two-stage deep learning-based framework to automatically detect the noisy ECG samples. Through extensive experiments and analysis on two different datasets, we observe that the deep learning-based framework can detect slightly and highly noisy ECG samples effectively. We also study the transfer of the model learned on one dataset to another dataset and observe that the framework effectively detects noisy ECG samples.