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.

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常因噪声和质量不佳导致数据缺失，这降低了信号质量并影响自动化下游任务。最近研究探索了基于概率时间序列模型对心电图缺失值进行插值的方法。然而，与确定性模型相比，其性能仍有限，因为训练目标中未明确考虑个体间变异和心跳关系。为提升基于概率模型的心电图插值与预测精度，本文提出模板引导的降噪扩散概率模型PulseDiff，该模型以涵盖多种健康状况的信息先验为条件。具体而言：1）首先从观测数据中提取个体级脉冲模板作为缺失值的信息先验，捕捉个人特征；2）随后在模板上添加心跳级随机位移项进行先验增强，考虑心跳级位置与幅度的变异；3）最后设计置信度分数以考虑个体健康状况，确保先验以安全方式提供。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，验证了该方法在管理不确定性的同时引导DDPM生成。当与SSSD$^{S4}$结合时，PulseDiff方法在短间隔缺失数据上优于领先的确定性模型，在长间隔缺失数据上性能相当。