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.

翻译：脉冲信号如心电图(ECG)在常规临床护理中被广泛采集。然而，使用移动健康系统采集的信号存在噪声和质量差的主要问题，这会降低信号质量、导致缺失值并影响自动化下游任务。近期研究探索了使用概率时间序列模型对心电图缺失值进行插补。然而，与确定性模型相比，其性能仍然有限，因为训练目标中未明确考虑受试者间差异和心跳关系。为提升概率模型在心电图插补与预测中的准确性，本文提出了一种模板引导的去噪扩散概率模型(DDPM)——PulseDiff，该模型以针对多种健康状态的信息性先验为条件。具体而言：1) 首先从观测值中提取受试者级别的脉冲模板，作为缺失值的信息性先验，实现先验的个性化；2) 其次引入搏动级别的随机位移项以增强先验，考虑每次搏动中先验位置和幅度的变化；3) 最后设计置信度分数以考虑受试者健康状态，确保先验的安全性。在PTBXL数据集上的实验表明，PulseDiff提升了两种强DDPM基线模型CSDI和SSSD$^{S4}$的性能，验证了本方法在管理不确定性的同时引导DDPM生成过程。当与SSSD$^{S4}$结合时，PulseDiff在短间隔缺失数据上优于领先的确定性模型，在长间隔数据缺失情况下表现相当。