# Investigating the Generalizability of ECG Noise Detection Across Diverse Data Sources and Noise Types

arXiv: https://arxiv.org/abs/2502.14522 · HTML: https://mlsanity.com/paper/2502.14522/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [On the Generalizability of ECG-based Stress Detection Models](https://mlsanity.com/paper/2210.06225/) (2022)
- [Stressor Type Matters! -- Exploring Factors Influencing Cross-Dataset Generalizability of Physiological Stress Detection](https://mlsanity.com/paper/2405.09563/) (2024)
- [Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels](https://mlsanity.com/paper/2208.08853/) (2022)

## Abstract (arXiv)

Electrocardiograms (ECGs) are vital for monitoring cardiac health, enabling the assessment of heart rate variability (HRV), detection of arrhythmias, and diagnosis of cardiovascular conditions. However, ECG signals recorded from wearable devices are frequently corrupted by noise artifacts, particularly those arising from motion and large muscle activity, which distort R-peaks and the QRS complex. These distortions hinder reliable HRV analysis and increase the risk of clinical misinterpretation. Existing studies on ECG noise detection typically evaluate performance on a single dataset, limiting insight into the generalizability of such methods across diverse sensors and recording conditions. In this work, we propose an HRV-based machine learning approach to detect noisy ECG segments and evaluate its generalizability using cross-dataset experiments on four datasets collected in both controlled and uncontrolled settings. Our method achieves over 90% average accuracy and an AUPRC exceeding 90%, even on previously unseen datasets-demonstrating robust performance across heterogeneous data sources. To support reproducibility and further research, we also release a curated and labeled ECG dataset annotated for noise artifacts.