期刊
SLEEP
卷 45, 期 8, 页码 -出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/sleep/zsac134
关键词
sleep staging; hypnogram; inter-rater agreement; machine learning; uncertainty; aleatoric; epistemic
资金
- Onera Health
- European Regional Development Fund
This study provides a theoretical framework to discuss and analyze the uncertainty in sleep staging. By introducing two variants of uncertainty and understanding their types and sources in sleep staging, recommendations are made to improve sleep staging in the future.
Sleep stage classification is an important tool for the diagnosis of sleep disorders. Because sleep staging has such a high impact on clinical outcome, it is important that it is done reliably. However, it is known that uncertainty exists in both expert scorers and automated models. On average, the agreement between human scorers is only 82.6%. In this study, we provide a theoretical framework to facilitate discussion and further analyses of uncertainty in sleep staging. To this end, we introduce two variants of uncertainty, known from statistics and the machine learning community: aleatoric and epistemic uncertainty. We discuss what these types of uncertainties are, why the distinction is useful, where they arise from in sleep staging, and provide recommendations on how this framework can improve sleep staging in the future.
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