Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring. (arXiv:2207.01910v2 [cs.LG] UPDATED)

Study Objectives: Inter-scorer variability in scoring polysomnograms is a
well-known problem. Most of the existing automated sleep scoring systems are
trained using labels annotated by a single scorer, whose subjective evaluation
is transferred to the model. When annotations from two or more scorers are
available, the scoring models are usually trained on the scorer consensus. The
averaged scorer's subjectivity is transferred into the model, losing
information about the internal variability among different scorers. In this
study, we aim to insert …

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