Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models

Tiezhi Wang, Nils Strodthoff

TL;DR

The paper tackles automatic sleep staging from polysomnography by systematically exploring encoder-predictor design choices, emphasizing structured state space models (S4) to capture long-range dependencies. It identifies two robust architectures, S4Sleep(ts) and S4Sleep(spec), that excel across raw time-series and spectrogram inputs, single- and multi-epoch configurations, and on large-scale SHHS1 as well as smaller public datasets. The authors demonstrate statistically significant improvements over state-of-the-art methods on Sleep EDF, MASS-SS3, and SHHS1, with explicit uncertainty estimates and robust generalization without hyperparameter tuning. This work provides a blueprint for architecture search in long time-series annotation tasks and suggests S4-based designs as powerful candidates for clinical and cross-domain time-series analysis, with code and data splits made publicly available.

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.

S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models

TL;DR

The paper tackles automatic sleep staging from polysomnography by systematically exploring encoder-predictor design choices, emphasizing structured state space models (S4) to capture long-range dependencies. It identifies two robust architectures, S4Sleep(ts) and S4Sleep(spec), that excel across raw time-series and spectrogram inputs, single- and multi-epoch configurations, and on large-scale SHHS1 as well as smaller public datasets. The authors demonstrate statistically significant improvements over state-of-the-art methods on Sleep EDF, MASS-SS3, and SHHS1, with explicit uncertainty estimates and robust generalization without hyperparameter tuning. This work provides a blueprint for architecture search in long time-series annotation tasks and suggests S4-based designs as powerful candidates for clinical and cross-domain time-series analysis, with code and data splits made publicly available.

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.
Paper Structure (23 sections, 2 equations, 3 figures, 10 tables)

This paper contains 23 sections, 2 equations, 3 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methods
  4. Models
  5. Single-epoch prediction models
  6. Multi-epoch prediction models
  7. Datasets
  8. Training procedure and performance evaluation
  9. Results
  10. Model selection: single-epoch models
  11. Model selection: multi-epoch models
  12. Performance evaluation
  13. Discussion
  14. Summary and conclusion
  15. Model architectures
  16. ...and 8 more sections

Figures (3)

  • Figure 1: Schematic representation of the encoder-predictor architecture used in sleep staging models.
  • Figure 2: Flow chart demonstrating the organization of the experiments that led to the identification of optimal model architectures for (a) raw time series and (b) spectrograms as input. Both start by identifying strong single-epoch models, which are then leveraged (in the form of epoch encoders) to explore multi-epoch prediction models while also investigating alternative encoder choices. Subsequently, for the two identified architectures the usage of sub-epoch instead of full-epoch encoders is explored. Finally, the two best-performing model architectures, S4Sleep(ts) and S4Sleep(spec), are evaluated on held-out test sets and retrained on the commonly used SEDF20 datatset , the MASS-SS3 dataset and the large-scale SHHS1 dataset, culminating in the final performance evaluation compiled in \ref{['tab:mainresult_sedf']} and \ref{['tab:mainresult_massshhs']}.
  • Figure 3: Schematic representation of the selected model architectures used in this work. (a) S4Sleep(ts): Raw time series as input modality; (b) S4Sleep(spec): Spectrogram as input modality.