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Assessing the importance of long-range correlations for deep-learning-based sleep staging

Tiezhi Wang, Nils Strodthoff

TL;DR

The paper investigates whether very long-range temporal context improves deep-learning sleep staging by evaluating the S4Sleep(TS) model on raw EEG as input and systematically increasing the input sequence length from $10$ to $200$ epochs using a staged finetuning schedule. It leverages an encoder-predictor architecture based on structured state space sequences and assesses performance with macro $F_1$-scores, employing bootstrapped 95% confidence intervals to quantify uncertainty. The findings show no statistically significant improvement with longer inputs; training from scratch at large input lengths can even degrade performance, suggesting limited diagnostic value of very long-range interactions for sleep staging within this architecture. The results emphasize the influence of model architecture and training strategy and indicate that, at least for S4Sleep(TS), very long-range context does not confer the expected diagnostic benefits, guiding future work toward architectural advances or higher-capacity models rather than simply expanding context length.

Abstract

This study aims to elucidate the significance of long-range correlations for deep-learning-based sleep staging. It is centered around S4Sleep(TS), a recently proposed model for automated sleep staging. This model utilizes electroencephalography (EEG) as raw time series input and relies on structured state space sequence (S4) models as essential model component. Although the model already surpasses state-of-the-art methods for a moderate number of 15 input epochs, recent literature results suggest potential benefits from incorporating very long correlations spanning hundreds of input epochs. In this submission, we explore the possibility of achieving further enhancements by systematically scaling up the model's input size, anticipating potential improvements in prediction accuracy. In contrast to findings in literature, our results demonstrate that augmenting the input size does not yield a significant enhancement in the performance of S4Sleep(TS). These findings, coupled with the distinctive ability of S4 models to capture long-range dependencies in time series data, cast doubt on the diagnostic relevance of very long-range interactions for sleep staging.

Assessing the importance of long-range correlations for deep-learning-based sleep staging

TL;DR

The paper investigates whether very long-range temporal context improves deep-learning sleep staging by evaluating the S4Sleep(TS) model on raw EEG as input and systematically increasing the input sequence length from to epochs using a staged finetuning schedule. It leverages an encoder-predictor architecture based on structured state space sequences and assesses performance with macro -scores, employing bootstrapped 95% confidence intervals to quantify uncertainty. The findings show no statistically significant improvement with longer inputs; training from scratch at large input lengths can even degrade performance, suggesting limited diagnostic value of very long-range interactions for sleep staging within this architecture. The results emphasize the influence of model architecture and training strategy and indicate that, at least for S4Sleep(TS), very long-range context does not confer the expected diagnostic benefits, guiding future work toward architectural advances or higher-capacity models rather than simply expanding context length.

Abstract

This study aims to elucidate the significance of long-range correlations for deep-learning-based sleep staging. It is centered around S4Sleep(TS), a recently proposed model for automated sleep staging. This model utilizes electroencephalography (EEG) as raw time series input and relies on structured state space sequence (S4) models as essential model component. Although the model already surpasses state-of-the-art methods for a moderate number of 15 input epochs, recent literature results suggest potential benefits from incorporating very long correlations spanning hundreds of input epochs. In this submission, we explore the possibility of achieving further enhancements by systematically scaling up the model's input size, anticipating potential improvements in prediction accuracy. In contrast to findings in literature, our results demonstrate that augmenting the input size does not yield a significant enhancement in the performance of S4Sleep(TS). These findings, coupled with the distinctive ability of S4 models to capture long-range dependencies in time series data, cast doubt on the diagnostic relevance of very long-range interactions for sleep staging.
Paper Structure (8 sections, 1 figure, 1 table)

This paper contains 8 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Model
  4. Datasets
  5. Training procedure and performance evaluation
  6. Results
  7. Discussion
  8. Conclusion

Figures (1)

  • Figure 1: Schematic representation of the S4Sleep(TS) model, which is composed of a S4-model-based sub-epoch encoder and a S4-model-based predictor along with a local pooling and linear classifier as prediction head.