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

arXiv:2404.15308v1 Announce Type: cross
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 …

abstract arxiv automate cs.lg datasets deep learning deploy eess.sp labor prediction scale sleep staging transformers type