用扩散模型检测心电图噪声，无需标注即可精准识别异常信号。
Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection
- 将心电图噪声量化转化为异常检测问题，利用扩散模型学习正常信号分布。
- 在外部数据集上达到1.308的W1分数，比现有方法提升48%以上。
- 适合临床实时监测和可穿戴设备，推动数字健康中的心电应用。
心电图信号常受噪声干扰，影响传统与可穿戴场景下的临床可靠性。现有去噪或伪影分类方法受限于标注不一致和泛化能力差。本文将心电图噪声量化重构为异常检测任务，提出基于扩散模型的框架，通过建模正常心电图分布来识别偏离信号，无需人工标注伪影。为更稳健评估性能并减少标签偏差，引入基于Wasserstein-1距离（W₁）的分布度量。模型在宏平均W₁上达1.308，优于次优方法超48%。外部验证显示强泛化性，可有效剔除噪声段以提升诊断准确率，支持及时临床干预。该方法增强实时心电监测能力，拓展心电图在数字健康技术中的适用性。
原文摘要 · Abstract (English)
Electrocardiography (ECG) signals are frequently degraded by noise, limiting their clinical reliability in both conventional and wearable settings. Existing methods for addressing ECG noise, relying on artifact classification or denoising, are constrained by annotation inconsistencies and poor generalizability. Here, we address these limitations by reframing ECG noise quantification as an anomaly detection task. We propose a diffusion-based framework trained to model the normative distribution of clean ECG signals, identifying deviations as noise without requiring explicit artifact labels. To robustly evaluate performance and mitigate label inconsistencies, we introduce a distribution-based metric using the Wasserstein-1 distance ($W_1$). Our model achieved a macro-average $W_1$ score of 1.308, outperforming the next-best method by over 48\%. External validation confirmed strong generalizability, facilitating the exclusion of noisy segments to improve diagnostic accuracy and support timely clinical intervention. This approach enhances real-time ECG monitoring and broadens ECG applicability in digital health technologies.
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