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

TL;DR

This study evaluates an HRV-based machine learning method for ECG noise detection across multiple datasets, demonstrating high accuracy and robustness in diverse real-world and controlled conditions, and provides a labeled dataset for future research.

Contribution

It introduces a cross-dataset evaluation of ECG noise detection using HRV features and releases a labeled dataset to enhance reproducibility and further research.

Findings

Achieves over 90% accuracy in noise detection across datasets

Demonstrates robustness of the method in diverse recording conditions

Provides a curated labeled ECG dataset for noise artifacts

Abstract

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.