Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
Electrocardiography (ECG) signals are often degraded by noise, which
complicates diagnosis in clinical and wearable settings. This study proposes a
diffusion-based framework for ECG noise quantification via reconstruction-based
anomaly detection, addressing annotation inconsistencies and the limited
generalizability of conventional methods. We introduce a distributional
evaluation using the Wasserstein-1 distance ($W_1$), comparing the
reconstruction error distributions between clean and noisy ECGs to mitigate
inconsistent annotations. Our final model achieved robust noise quantification
using only three reverse diffusion steps. The model recorded a macro-average
$W_1$ score of 1.308 across the benchmarks, outperforming the next-best method
by over 48%. External validations demonstrated strong generalizability,
supporting the exclusion of low-quality segments to enhance diagnostic accuracy
and enable timely clinical responses to signal degradation. The proposed method
enhances clinical decision-making, diagnostic accuracy, and real-time ECG
monitoring capabilities, supporting future advancements in clinical and
wearable ECG applications.
