期刊
PHYSIOLOGICAL MEASUREMENT
卷 43, 期 4, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1361-6579/ac6049
关键词
automatic sleep staging; sleep scoring; deep learning; sleep monitoring; deep neural networks; EEG
资金
- Turing Fellowship under the EPSRC [EP/N510129/1]
Modern deep learning has the potential to transform clinical studies of human sleep and reduce the workload of clinicians. Automatic sleep staging systems have achieved similar performance to human experts, but face challenges in clinical adoption.
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.
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