SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures

While the shift toward unified foundation models has revolutionized many deep learning domains, sleep medicine remains largely restricted to taskspecific models that focus on localized microstructure features. These approaches often neglect the rich, multi-modal context of Polysomnography (PSG) and fail to capture the global macrostructure of a full night's sleep. To address this, we introduce SleepMaMi, a Sleep Foundation Model engineered to master both hour-long sleep architectures and fine-grained signal morphologies. Our framework utilizes a hierarchical dual-encoder design: a Macro-Encoder to model full-night temporal dependencies and a Micro-Encoder to capture short-term characteristics from biosignals. Macro-Encoder is trained via Demographic-Guided Contrastive Learning, which aligns overnight sleep patterns with objective subject metadata, such as age, sex, and BMI to refine global representations. Micro-Encoder is optimized via a hybrid Masked Autoencoder (MAE) and multi-modal contrastive objective. Pre-trained on a massive corpus of >20,000 PSG recordings (158K hours), Sleep-MaMi outperforms or matches state-of-the-art foundation models across a diverse suite of downstream tasks, demonstrating superior generalizability and label-efficient adaptation for clinical sleep analysis.