SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures
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arXiv
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| Format: | Preprint |
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2026
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| author | Park, Keondo Na, Younghoon Choi, Yourim Ryu, Hyunwoo Shin, Hyun-Woo Kim, Hyung-Sin |
| author_facet | Park, Keondo Na, Younghoon Choi, Yourim Ryu, Hyunwoo Shin, Hyun-Woo Kim, Hyung-Sin |
| contents | While the shift toward unified foundation models has revolutionized many deep learning domains, sleep medicine remains largely restricted to task-specific models that focus on localized micro-structure features. These approaches often neglect the rich, multi-modal context of Polysomnography (PSG) and fail to capture the global macro-structure 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),SleepMaMi outperforms existing foundation models across a diverse suite of downstream tasks, demonstrating superior generalizability and label-efficient adaptation for clinical sleep analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07628 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures Park, Keondo Na, Younghoon Choi, Yourim Ryu, Hyunwoo Shin, Hyun-Woo Kim, Hyung-Sin Artificial Intelligence Machine Learning While the shift toward unified foundation models has revolutionized many deep learning domains, sleep medicine remains largely restricted to task-specific models that focus on localized micro-structure features. These approaches often neglect the rich, multi-modal context of Polysomnography (PSG) and fail to capture the global macro-structure 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),SleepMaMi outperforms existing foundation models across a diverse suite of downstream tasks, demonstrating superior generalizability and label-efficient adaptation for clinical sleep analysis. |
| title | SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.07628 |