Joint Fine-Grained Representation Learning and Masked Relational Modeling for EEG-Based Automatic Sleep Staging in Fabric Space.

Abstract

Sleep staging is a crucial method for the evaluation of sleep quality and the diagnosis of sleep disorders. In recent years, rapid progress has been made in sleep research through the application of fabric computing and neural networks. Flexible fabric sensors introduced by fabric computing minimize the discomfort of data collection devices on individuals, while neural network-based algorithms can automatically perform sleep staging based on the collected signals. However, there are two key challenges hinder the integration of automatic sleep staging networks with fabric computing: (1) signals in fabric-based environments exhibit strong heterogeneity due to the wide range of individuals, and (2) interactions between individuals and the fabric space introduce behavioral dynamics to the system. In this paper, we propose a masked autoencoder-based sleep staging neural networks (MAESleepNet), designed to integrate automatic sleep staging algorithm with fabric space. Specifically, MAESleepNet addresses the challenge of signal heterogeneity by learning fine-grained representations from local signals. Furthermore, MAESleepNet tackle the challenge of behavioral dynamics through stochastic masking and reconstruction pre-training. Experiments were conducted on three public datasets: (1) Sleep-EDF-20, (2) Sleep-EDF-78 and (3) SHHS. MAESleepNet achieves overall accuracies of 88.9%, 85.5%, and 87.3%, respectively, outperforming other state-of-the-art models. Furthermore, feature visualization and reconstruction visualization experiments were also conducted. The results demonstrates that MAESleepNet is an effective solution to the aforementioned challenges, paving the way for seamless integration into the fabric space.