  Pulsative signals such as the electrocardiogram (ECG) are extensively
collected as part of routine clinical care. However, noisy and poor-quality
recordings are a major issue for signals collected using mobile health systems,
decreasing the signal quality, leading to missing values, and affecting
automated downstream tasks. Recent studies have explored the imputation of
missing values in 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
imputation and forecasting accuracy for ECG with probabilistic models, we
present a template-guided denoising diffusion probabilistic model (DDPM),
PulseDiff, which is conditioned on an informative prior for a range of health
conditions. Specifically, 1) we first extract a subject-level pulsative
template from the observed values to use as an informative prior of the missing
values, which personalises the prior; 2) we then add beat-level stochastic
shift terms to augment the prior, which considers variations in the position
and amplitude of the prior at each beat; 3) we finally design a confidence
score to consider the health condition of the subject, which ensures our prior
is provided safely. Experiments with the PTBXL dataset reveal that PulseDiff
improves the performance of two strong DDPM baseline models, CSDI and
SSSD$^{S4}$, verifying that our method guides the generation of DDPMs while
managing the uncertainty. When combined with SSSD$^{S4}$, PulseDiff outperforms
the leading deterministic model for short-interval missing data and is
comparable for long-interval data loss.

Machine Learning