Improving Diffusion Models for ECG Imputation with an Augmented Template Prior

Pulsative signals such as the electrocardiogram (ECG) are extensively collected as part of routine clinical care. However, noisy and poor-quality recordings, leading to missing values, are a major issue for signals collected using mobile health systems, decreasing the signal quality and affecting the automated downstream tasks. Recent studies have explored imputation of missing values for ECG with probabilistic time-series models. Nevertheless, in comparison with the deterministic models, their performance is still limited, as the variations across subjects and heart-beat relationships are not explicitly considered in the training objective. In this work, to improve the ECG imputation and forecasting accuracy with probabilistic models, we present an template-guided denoising diffusion probabilistic model, PulseDiff, which is conditioned an informative prior for a range of health conditions. Specifically, 1) we first extract a subject-level pulsative template from the observation as an informative prior of missing values, which captures the personal characteristics; 2) we then add beat-level stochastic shift terms on the template for prior augmentation, which considers the beat-level variance of positioning and amplitude; 3) we finally design a confidence score to consider the health condition of subject, which ensures our prior is provided in a safe way. Experiments with the PTBXL dataset reveal PulseDiff improves the performance of two strong DDPMs baseline models, CSDI and SSSD$^{S4}$, verifying our method guides the generation of DDPMs while managing the uncertainty. When combining with SSSD$^{S4}$, our PulseDiff method outperforms the leading deterministic model for short-interval missing data and is comparable for long-interval data loss.