SleepLM: Natural-Language Intelligence for Human Sleep
arXiv:2602.23605v1 Announce Type: new Abstract: We present SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural language. Despite the critical role of sleep, learning-based sleep analysis systems operate in closed label spaces (e.g., predefined stages or events) and fail to describe, query, or generalize to novel sleep phenomena. SleepLM bridges natural language and multimodal polysomnography, enabling language-grounded representations of sleep physiology. To support this alignment, we introduce a multilevel sleep caption generation pipeline that enables the curation of the first large-scale sleep-text dataset, comprising over 100K hours of data from more than 10,000 individuals. Furthermore, we present a unified pretraining objective that combines contrastive alignment, caption generation, and signal reconstruction to better capture physiological fidelity and cross-modal interactions. Extensive experimen
arXiv:2602.23605v1 Announce Type: new Abstract: We present SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural language. Despite the critical role of sleep, learning-based sleep analysis systems operate in closed label spaces (e.g., predefined stages or events) and fail to describe, query, or generalize to novel sleep phenomena. SleepLM bridges natural language and multimodal polysomnography, enabling language-grounded representations of sleep physiology. To support this alignment, we introduce a multilevel sleep caption generation pipeline that enables the curation of the first large-scale sleep-text dataset, comprising over 100K hours of data from more than 10,000 individuals. Furthermore, we present a unified pretraining objective that combines contrastive alignment, caption generation, and signal reconstruction to better capture physiological fidelity and cross-modal interactions. Extensive experiments on real-world sleep understanding tasks verify that SleepLM outperforms state-of-the-art in zero-shot and few-shot learning, cross-modal retrieval, and sleep captioning. Importantly, SleepLM also exhibits intriguing capabilities including language-guided event localization, targeted insight generation, and zero-shot generalization to unseen tasks. All code and data will be open-sourced.
Executive Summary
This article presents SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural language. Leveraging a multilevel sleep caption generation pipeline and a unified pretraining objective, SleepLM bridges natural language and multimodal polysomnography, capturing physiological fidelity and cross-modal interactions. Extensive experiments demonstrate SleepLM's superiority in zero-shot and few-shot learning, cross-modal retrieval, and sleep captioning. The model's capabilities, including language-guided event localization and targeted insight generation, hold significant promise for advancing sleep research and clinical applications. This innovative approach has the potential to transform the field of sleep medicine and its related disciplines.
Key Points
- ▸ Development of SleepLM, a family of sleep-language foundation models
- ▸ Multilevel sleep caption generation pipeline for large-scale sleep-text dataset curation
- ▸ Unified pretraining objective for contrastive alignment, caption generation, and signal reconstruction
- ▸ Experiments demonstrating SleepLM's superiority in various sleep understanding tasks
Merits
Strength in Multimodal Integration
SleepLM effectively bridges natural language and multimodal polysomnography, enabling a more comprehensive understanding of sleep physiology.
Improved Sleep Research and Clinical Applications
The model's capabilities, including language-guided event localization and targeted insight generation, hold significant promise for advancing sleep research and clinical applications.
Open-Source Availability
The open-sourcing of code and data ensures the reproducibility and further development of SleepLM, facilitating its widespread adoption and impact.
Demerits
Limited Generalizability to Non-Sleep Domains
While SleepLM demonstrates impressive capabilities in sleep-related tasks, its generalizability to non-sleep domains remains unclear and warrants further investigation.
Potential for Overreliance on Predefined Labels
The use of predefined labels in sleep analysis systems may lead to overreliance on these labels, potentially limiting the model's ability to detect novel sleep phenomena.
Expert Commentary
The article presents a groundbreaking approach to integrating natural language and multimodal polysomnography for sleep analysis. The SleepLM model's ability to capture physiological fidelity and cross-modal interactions holds significant promise for advancing sleep research and clinical applications. However, further investigation is needed to address concerns related to generalizability and overreliance on predefined labels. Additionally, the article highlights the need for policy interventions to ensure the responsible development and deployment of SleepLM in the sleep medicine domain. Overall, this article represents a significant contribution to the field of sleep medicine and AI, with far-reaching implications for the diagnosis, treatment, and prevention of sleep disorders.
Recommendations
- ✓ Recommendation 1: Future research should focus on exploring the generalizability of SleepLM to non-sleep domains and developing strategies to mitigate the potential for overreliance on predefined labels.
- ✓ Recommendation 2: The development and deployment of SleepLM should be accompanied by policy interventions aimed at ensuring data privacy, security, and regulatory compliance in the sleep medicine domain.