Investigating the Generalizability of ECG Noise Detection Across Diverse Data Sources and Noise Types
This work addresses the issue of unreliable ECG analysis due to noise artifacts, which is critical for clinicians and patients using wearable devices, but it is incremental as it builds on existing noise detection methods by focusing on generalizability.
The paper tackled the problem of ECG noise detection by investigating the generalizability of a novel HRV-based approach across diverse datasets and noise types, achieving an average accuracy of over 90% and an AUPRC of more than 0.9 in cross-dataset experiments.
Electrocardiograms (ECGs) are essential for monitoring cardiac health, allowing clinicians to analyze heart rate variability (HRV), detect abnormal rhythms, and diagnose cardiovascular diseases. However, ECG signals, especially those from wearable devices, are often affected by noise artifacts caused by motion, muscle activity, or device-related interference. These artifacts distort R-peaks and the characteristic QRS complex, making HRV analysis unreliable and increasing the risk of misdiagnosis. Despite this, the few existing studies on ECG noise detection have primarily focused on a single dataset, limiting the understanding of how well noise detection models generalize across different datasets. In this paper, we investigate the generalizability of noise detection in ECG using a novel HRV-based approach through cross-dataset experiments on four datasets. Our results show that machine learning achieves an average accuracy of over 90\% and an AUPRC of more than 0.9. These findings suggest that regardless of the ECG data source or the type of noise, the proposed method maintains high accuracy even on unseen datasets, demonstrating the feasibility of generalizability.