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

2025-02-20

AI Summary

Existing ECG noise detection models suffer from limited generalizability, as most studies evaluate performance on single, homogeneous datasets, failing to assess robustness across diverse noise types and acquisition conditions. Method: This paper proposes a generalizable noise detection framework grounded in heart rate variability (HRV) features. It integrates time–frequency domain HRV feature engineering with machine learning classifiers and adopts an AUPRC-centric evaluation paradigm coupled with multi-dataset cross-validation. Contribution/Results: We conduct the first systematic cross-dataset evaluation across four heterogeneous public ECG datasets, assessing generalization against motion artifacts, electromyographic interference, and other noise classes. The framework achieves an average accuracy of 90.2% and an AUPRC of 0.912 on unseen datasets—significantly outperforming single-dataset baselines. Results demonstrate the strong robustness and broad applicability of HRV-driven approaches in cross-source, multi-noise-type scenarios.

Technology Category

Machine Learning: Evaluation and AnalysisComputer Vision: Adversarial Attacks & RobustnessIntelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

Web Mining and Content Analysis: Robustness and generalizability of Web mining methodsSearch and Retrieval-Augmented AI: Web evaluation methodologies and metricsEconomics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• Generalizability of ECG noise detection
• Cross-dataset experiments on diverse sources
• HRV-based approach for noise artifacts

Innovation

• HRV-based noise detection
• Cross-dataset generalizability
• Machine learning accuracy