Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection

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.

@article{han2025_2506.11815,
  title={ Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection },
  author={ Tae-Seong Han and Jae-Wook Heo and Hakseung Kim and Cheol-Hui Lee and Hyub Huh and Eue-Keun Choi and Dong-Joo Kim },
  journal={arXiv preprint arXiv:2506.11815},
  year={ 2025 }
}