  Foundation models, especially those using transformers as backbones, have
gained significant popularity, particularly in language and language-vision
tasks. However, large foundation models are typically trained on high-quality
data, which poses a significant challenge, given the prevalence of poor-quality
real-world data. This challenge is more pronounced for developing foundation
models for physiological data; such data are often noisy, incomplete, or
inconsistent. The present work aims to provide a toolset for developing
foundation models on physiological data. We leverage a large dataset of
photoplethysmography (PPG) signals from hospitalized intensive care patients.
For this data, we propose SimQuality, a novel self-supervised learning task
based on convolutional neural networks (CNNs) as the backbone to enforce
representations to be similar for good and poor quality signals that are from
similar physiological states. We pre-trained the SimQuality on over 36 million
30-second PPG pairs and then fine-tuned and tested on six downstream tasks
using external datasets. The results demonstrate the superiority of the
proposed approach on all the downstream tasks, which are extremely important
for heart monitoring on wearable devices. Our method indicates that CNNs can be
an effective backbone for foundation models that are robust to training data
quality.
