sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals
arXiv:2602.13857v1 Announce Type: new Abstract: Tasks ranging from sleep staging to clinical diagnosis traditionally rely on standard polysomnography (PSG) devices, bedside monitors and wearable devices, which capture diverse nocturnal biosignals (e.g., EEG, EOG, ECG, SpO$_2$). However, heterogeneity across devices and frequent sensor dropout pose significant challenges for unified modelling of these multimodal signals. We present \texttt{sleep2vec}, a foundation model for diverse and incomplete nocturnal biosignals that learns a shared representation via cross-modal alignment. \texttt{sleep2vec} is contrastively pre-trained on 42,249 overnight recordings spanning nine modalities using a \textit{Demography, Age, Site \& History-aware InfoNCE} objective that incorporates physiological and acquisition metadata (\textit{e.g.}, age, gender, recording site) to dynamically weight negatives and mitigate cohort-specific shortcuts. On downstream sleep staging and clinical outcome assessment, \
arXiv:2602.13857v1 Announce Type: new Abstract: Tasks ranging from sleep staging to clinical diagnosis traditionally rely on standard polysomnography (PSG) devices, bedside monitors and wearable devices, which capture diverse nocturnal biosignals (e.g., EEG, EOG, ECG, SpO$_2$). However, heterogeneity across devices and frequent sensor dropout pose significant challenges for unified modelling of these multimodal signals. We present \texttt{sleep2vec}, a foundation model for diverse and incomplete nocturnal biosignals that learns a shared representation via cross-modal alignment. \texttt{sleep2vec} is contrastively pre-trained on 42,249 overnight recordings spanning nine modalities using a \textit{Demography, Age, Site \& History-aware InfoNCE} objective that incorporates physiological and acquisition metadata (\textit{e.g.}, age, gender, recording site) to dynamically weight negatives and mitigate cohort-specific shortcuts. On downstream sleep staging and clinical outcome assessment, \texttt{sleep2vec} consistently outperforms strong baselines and remains robust to any subset of available modalities and sensor dropout. We further characterize, to our knowledge for the first time, scaling laws for nocturnal biosignals with respect to modality diversity and model capacity. Together, these results show that unified cross-modal alignment, coupled with principled scaling, enables label-efficient, general-purpose modelling of real-world nocturnal biosignals.
Executive Summary
The article introduces sleep2vec, a foundation model designed to unify and align heterogeneous nocturnal biosignals from various devices, addressing challenges posed by device diversity and sensor dropout. Utilizing a contrastive pre-training approach on a large dataset of overnight recordings across nine modalities, sleep2vec employs a Demography, Age, Site & History-aware InfoNCE objective to learn shared representations. The model demonstrates robust performance in sleep staging and clinical outcome assessment, even with incomplete data, and explores scaling laws for nocturnal biosignals. This work highlights the potential of cross-modal alignment and principled scaling for label-efficient modeling of real-world nocturnal biosignals.
Key Points
- ▸ Introduction of sleep2vec for unified modeling of heterogeneous nocturnal biosignals.
- ▸ Use of contrastive pre-training with a Demography, Age, Site & History-aware InfoNCE objective.
- ▸ Robust performance in sleep staging and clinical outcome assessment despite sensor dropout.
- ▸ Characterization of scaling laws for nocturnal biosignals with respect to modality diversity and model capacity.
Merits
Innovative Approach
The sleep2vec model introduces a novel approach to handling heterogeneous and incomplete nocturnal biosignals, leveraging cross-modal alignment and contrastive pre-training.
Robust Performance
The model demonstrates consistent performance across various downstream tasks, even with incomplete data, showcasing its robustness and generalizability.
Scaling Laws Characterization
The article provides valuable insights into the scaling laws for nocturnal biosignals, contributing to the understanding of model capacity and modality diversity.
Demerits
Data Dependency
The effectiveness of sleep2vec is highly dependent on the availability of large and diverse datasets, which may not always be feasible or accessible.
Complexity
The model's complexity and the need for specialized hardware and software may limit its widespread adoption and practical implementation.
Generalizability
While the model shows robust performance, its generalizability to different populations and clinical settings needs further validation.
Expert Commentary
The introduction of sleep2vec represents a significant advancement in the field of nocturnal biosignal analysis. By addressing the challenges of heterogeneity and sensor dropout, the model offers a promising solution for unified and robust modeling of diverse biosignals. The use of contrastive pre-training and the incorporation of physiological and acquisition metadata further enhance the model's performance and generalizability. The characterization of scaling laws provides valuable insights into the relationship between modality diversity and model capacity, contributing to the broader understanding of machine learning principles. However, the model's complexity and data dependency pose challenges for widespread adoption. Future research should focus on validating the model's generalizability across different populations and clinical settings, as well as addressing data privacy and security concerns. Overall, sleep2vec sets a new benchmark for the analysis of nocturnal biosignals and paves the way for more advanced and reliable healthcare applications.
Recommendations
- ✓ Further validation of sleep2vec's performance across diverse populations and clinical settings to ensure generalizability.
- ✓ Development of guidelines and regulations to address data privacy and security concerns associated with the use of sensitive biosignal data.
