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.

通过利用脉搏信号和患者个体特征，我们提出了一种模板引导的去噪扩散概率模型 (PulseDiff)，用于改善心电图 (ECG) 数据插值和预测的准确性，并在实验中证实了其优越性。