A journey toward artificial intelligence-assisted automated sleep scoring.
Patterns (New York, N.Y.)(2022)
摘要
Sleep scoring is a tedious, time-consuming process that presents a huge challenge in clinics. Leveraging the state-of-the-art U-net architecture, Zhang et al. developed a deep learning algorithm to simultaneously annotate basic and pathologic sleep stages. This model can analyze a full-length sleep record in a few seconds with high accuracy.
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