Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning

TL;DR

This paper introduces MASS, a novel framework for resource-efficient sleep staging that reduces EEG data requirements using multi-level masking and prompt learning, achieving state-of-the-art results in low-resource scenarios.

Contribution

The paper proposes a new framework called Mask-Aware Sleep Staging (MASS) that employs multi-level masking and hierarchical prompt learning to enable effective sleep staging with limited EEG data.

Findings

Achieves state-of-the-art performance on four datasets.

Excels in scenarios with very limited data.

Demonstrates potential for low-resource sleep monitoring.

Abstract

Automatic sleep staging plays a vital role in assessing sleep quality and diagnosing sleep disorders. Most existing methods rely heavily on long and continuous EEG recordings, which poses significant challenges for data acquisition in resource-constrained systems, such as wearable or home-based monitoring systems. In this paper, we propose the task of resource-efficient sleep staging, which aims to reduce the amount of signal collected per sleep epoch while maintaining reliable classification performance. To solve this task, we adopt the masking and prompt learning strategy and propose a novel framework called Mask-Aware Sleep Staging (MASS). Specifically, we design a multi-level masking strategy to promote effective feature modeling under partial and irregular observations. To mitigate the loss of contextual information introduced by masking, we further propose a hierarchical prompt learning mechanism that aggregates unmasked data into a global prompt, serving as a semantic anchor for guiding both patch-level and epoch-level feature modeling. MASS is evaluated on four datasets, demonstrating state-of-the-art performance, especially when the amount of data is very limited. This result highlights its potential for efficient and scalable deployment in real-world low-resource sleep monitoring environments.