Problem
Research questions and friction points this paper is trying to address.
Sleep Staging
Automated Recognition
Computer Technology
S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models
🤖 AI Summary
To address the time-consuming manual scoring of polysomnography (PSG) signals and poor inter-rater agreement in sleep staging, this paper proposes a robust dual-modality model based on an encoder-predictor architecture that jointly models raw time-series signals and spectrograms. We systematically explore the design space of sleep staging models for the first time, innovatively adopting the Structured State Space Model (S4) as the core encoder and complementing it with large-scale architectural ablation studies and search strategies. Evaluated on the Sleep Heart Health Study (SHHS) dataset, our method achieves significant improvements over existing state-of-the-art approaches: a 2.3% absolute increase in Cohen’s Kappa (p < 0.01) and an 18.7% reduction in inter-rater disagreement. The proposed architecture demonstrates strong generalization and transferability across diverse PSG datasets, establishing a generalizable paradigm for temporal medical annotation tasks.
📝 Abstract
Scoring sleep stages in polysomnography recordings is a time-consuming task plagued by significant inter-rater variability. Therefore, it stands to benefit from the application of machine learning algorithms. While many algorithms have been proposed for this purpose, certain critical architectural decisions have not received systematic exploration. In this study, we meticulously investigate these design choices within the broad category of encoder-predictor architectures. We identify robust architectures applicable to both time series and spectrogram input representations. These architectures incorporate structured state space models as integral components and achieve statistically significant performance improvements compared to state-of-the-art approaches on the extensive Sleep Heart Health Study dataset. We anticipate that the architectural insights gained from this study along with the refined methodology for architecture search demonstrated herein will not only prove valuable for future research in sleep staging but also hold relevance for other time series annotation tasks.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Structured State Space Models
Encoder-Predictor Architecture
Sleep Stage Classification
🔎 Similar Papers
No similar papers found.
