Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection
This work addresses noise issues in ECG signals for clinical and wearable monitoring, offering improved generalizability and diagnostic accuracy, though it is incremental as it builds on existing diffusion-based and anomaly detection methods.
The paper tackled the problem of noise in electrocardiography (ECG) signals by reframing noise quantification as an anomaly detection task, achieving a macro-average Wasserstein-1 distance score of 1.308, which outperformed the next-best method by over 48%.
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.