Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions

TL;DR

This paper reviews recent advances in automatic sleep staging using deep learning, highlighting progress, challenges, and future directions to enhance clinical adoption and improve sleep disorder diagnosis.

Contribution

It provides a comprehensive overview of recent deep learning methods for sleep staging, discussing challenges and proposing future research directions for clinical integration.

Findings

Deep learning models achieve performance comparable to human experts on healthy subjects.

Automatic sleep staging systems face challenges in clinical environments due to variability and complexity.

The review identifies key future research directions to facilitate clinical adoption.

Abstract

Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.