Abstract
Foundation models have recently emerged as a powerful paradigm for learning transferable representations from large-scale biomedical data. However, existing approaches for physiological waveforms primarily optimize reconstruction or forecasting objectives that do not explicitly preserve clinically meaningful waveform morphology. Electrocardiograms (ECGs) and pulse oximetry (SpO2) waveforms encode rich cardiovascular and hemodynamic information through their morphological structure.
In this work, we introduce MorphologyFM, a multimodal foundation model pretrained on paired ECG and SpO2 waveforms from the MIMIC critical care database using a morphology-aware self-supervised learning objective. MorphologyFM combines morphology-guided masking, cross-modal representation learning, and contrastive latent alignment to learn representations that capture clinically relevant physiological structure without requiring manual annotations.
We evaluate MorphologyFM across multiple downstream prediction tasks, including arrhythmia classification, hypoxemia prediction, mortality prediction, and length of stay estimation, demonstrating consistent improvements over representative self-supervised learning methods, including Masked Autoencoders (MAE), contrastive learning, Barlow Twins, and Joint Embedding Predictive Architectures (JEPA). Furthermore, we show that jointly modeling ECG and SpO2 waveforms produces more transferable representations than single modality pretraining.
Our results establish waveform morphology as a powerful inductive bias for self-supervised physiological representation learning and introduce MorphologyFM as a general-purpose foundation model for continuous physiological monitoring.
Blogger's Review: The introduction of MorphologyFM offers a novel approach to processing physiological signals, particularly enhancing model expressiveness through morphological features without manual labeling. Its potential applications in various clinical tasks are noteworthy, and it could drive advancements in medical monitoring technologies.