  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.
