Abstract
Modern deep learning holds a great potential to transform clinical studies of 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 provide the shared view of the authors on the most recent state-of-the-art developments in automatic sleep staging, the challenges that still need to be addressed, and the future directions needed for automatic sleep scoring to achieve clinical value.
| Original language | English |
|---|---|
| Article number | 04TR01 |
| Journal | Physiological Measurement |
| Volume | 43 |
| Issue | 4 |
| ISSN | 0967-3334 |
| DOIs | |
| Publication status | Published - Apr 2022 |
Keywords
- automatic sleep staging
- deep learning
- deep neural networks
- EEG
- sleep monitoring
- sleep scoring
- Sleep
- Sleep Stages
- Humans
- Electroencephalography
- Polysomnography