提出一种跨数据集的心电图噪声检测方法，有效应对穿戴设备中的运动干扰。
Investigating the Generalizability of ECG Noise Detection Across Diverse Data Sources and Noise Types
- 基于心率变异性设计机器学习模型，识别心电图噪声段。
- 在4个不同数据集上平均准确率超90%，AUPRC超过90%。
- 适合做可泛化的医疗信号处理研究，尤其关注穿戴设备信号质量。
心电图（ECG）对心脏健康监测至关重要，可用于评估心率变异性（HRV）、检测心律失常及诊断心血管疾病。然而，可穿戴设备记录的ECG信号常受运动和大肌肉活动等噪声干扰，导致R波峰和QRS复合波形畸变，影响可靠的HRV分析并增加临床误判风险。现有研究多在单一数据集上评估噪声检测性能，限制了对方法泛化能力的认知。本文提出一种基于HRV的机器学习方法，通过跨数据集实验在四个不同采集环境（受控与非受控）的数据集上评估其泛化性。结果表明，该方法在未见过的数据集上仍保持超过90%的平均准确率，且AUPRC超过90%，展现出对异构数据源的鲁棒性能。为支持可复现性和后续研究，我们还发布了经过标注的噪声心电图数据集。
原文摘要 · Abstract (English)
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.
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