With the progress of sensor technology in wearables, the collection and
analysis of PPG signals are gaining more interest. Using Machine Learning, the
cardiac rhythm corresponding to PPG signals can be used to predict different
tasks such as activity recognition, sleep stage detection, or more general
health status. However, supervised learning is often limited by the amount of
available labeled data, which is typically expensive to obtain. To address this
problem, we propose a Self-Supervised Learning (SSL) method with a pretext task
of signal reconstruction to learn an informative generalized PPG
representation. The performance of the proposed SSL framework is compared with
two fully supervised baselines. The results show that in a very limited label
data setting (10 samples per class or less), using SSL is beneficial, and a
simple classifier trained on SSL-learned representations outperforms fully
supervised deep neural networks. However, the results reveal that the
SSL-learned representations are too focused on encoding the subjects.
Unfortunately, there is high inter-subject variability in the SSL-learned
representations, which makes working with this data more challenging when
labeled data is scarce. The high inter-subject variability suggests that there
is still room for improvements in learning representations. In general, the
results suggest that SSL may pave the way for the broader use of machine
learning models on PPG data in label-scarce regimes.

本文提出了一种自监督学习方法用于学习一个通用的、具有信息的 PPG 表示，并将其用于活动和睡眠阶段的识别以及更普遍的健康状态预测，结果表明在标记数据稀缺时，使用 SSL 对于简单分类器的训练表现更好，同时也发现 SSL 的结果集中于编码受试者，因此对于标记数据不足的情况有一定局限性。