SiamQuality

Self-supervised foundation model for photoplethysmography (PPG) that explicitly targets the low-signal-quality problem endemic to real-world wearable and bedside PPG, rather than assuming clean input. A Siamese ResNet (SimSiam-style) encoder is pretrained to produce similar representations for temporally-adjacent high- and low-quality PPG segments presumed to share the same underlying physiological state, with curriculum learning that gradually increases the artifact-level gap between paired segments. Pretrained on over 36 million 30-second PPG pairs drawn from more than 24,100 UCSF ICU patients, then fine-tuned and evaluated on six downstream cardiovascular-monitoring datasets/tasks (heart rate, respiratory rate, blood pressure, and atrial-fibrillation detection), where it exceeds prior state-of-the-art by roughly 75% on respiratory-rate estimation and 5% on AF detection, with performance scaling with backbone depth (ResNet152 best). Code and the pretraining dataset are not public; the pretraining data is available upon request under a UCSF-Emory data use agreement.