Label-Efficient Sleep Staging Using Transformers Pre-trained with Position Prediction

TL;DR

This paper introduces a new self-supervised learning approach for sleep staging using transformers, which improves performance and reduces labeled data requirements by pretraining the entire model rather than just parts of it.

Contribution

It proposes a coupled architecture and pretraining scheme that pretrains the entire model, leading to better performance and less dependence on large labeled datasets.

Findings

Performance gains of 3-5% in sleep staging accuracy.

Reduces labeled data needs by approximately 800 subjects.

Performance does not saturate with more labeled data.

Abstract

Sleep staging is a clinically important task for diagnosing various sleep disorders, but remains challenging to deploy at scale because it because it is both labor-intensive and time-consuming. Supervised deep learning-based approaches can automate sleep staging but at the expense of large labeled datasets, which can be unfeasible to procure for various settings, e.g., uncommon sleep disorders. While self-supervised learning (SSL) can mitigate this need, recent studies on SSL for sleep staging have shown performance gains saturate after training with labeled data from only tens of subjects, hence are unable to match peak performance attained with larger datasets. We hypothesize that the rapid saturation stems from applying a sub-optimal pretraining scheme that pretrains only a portion of the architecture, i.e., the feature encoder, but not the temporal encoder; therefore, we propose adopting an architecture that seamlessly couples the feature and temporal encoding and a suitable pretraining scheme that pretrains the entire model. On a sample sleep staging dataset, we find that the proposed scheme offers performance gains that do not saturate with amount of labeled training data (e.g., 3-5\% improvement in balanced sleep staging accuracy across low- to high-labeled data settings), reducing the amount of labeled training data needed for high performance (e.g., by 800 subjects). Based on our findings, we recommend adopting this SSL paradigm for subsequent work on SSL for sleep staging.