Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions
It tackles the challenge of automating routine sleep analysis for clinicians, but it is incremental as it reviews existing developments rather than presenting new methods.
This review addresses the problem of automating sleep staging using deep learning to reduce clinician workload, noting that current systems achieve performance similar to human experts on healthy subjects but have not been widely adopted clinically.
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.