Work

A foundation model of wearable pulse oximetry reveals physiological signatures of health and cardiometabolic risk

Year: 2026

Type: preprint

Abstract: While Photoplethysmography (PPG) is established as a noninvasive optical tool for monitoring heart rate and oxygen saturation, its high-resolution pulsatile blood volume waveforms contain rich physiological data that extend far beyond conventional vital signs. We introduce PulseOx-FM, a foundation model, trained using self-supervised learning on 6,995,558 segments of pulse oximetry signals collected during 42,282 overnight sleep monitoring recordings of 10,704 adult participants in the Human Phenotype Project (HPP). Using chronological age as a global health benchmark, PulseOx-FM significantly outperformed existing open-source and proprietary feature extraction methods while demonstrating robust generalization in an external out-of-distribution cohort. PulseOx-FM representations predicted 71 phenotypic targets spanning cardiometabolic, and neuropsychiatric domains beyond age, sex and body mass index. Applied in the same way to 3,721 intensive-care patients, two-minute windows of routine bedside pulse oximetry from the first day of admission improved the prediction of survival beyond age, sex and the heart rate and oxygen saturation the same device reports. Nightly embeddings further tracked next-day glycemic, dietary and activity-based state within individuals, dissociating this signal from sleep architecture alone. This next-day glycemic signal was predominantly a direct physiological effect, not explained by next-day dietary intake. These findings suggest that PulseOx-FM provides a generalizable framework for encoding physiological patterns from sleep, offering a non-invasive tool for global health risk stratification and precision medicine.

Source: medRxiv

DOI: 10.64898/2026.07.01.26356992