AI Article Synopsis
- Sleep stage classification helps doctors figure out if someone has sleep problems, and it's really important for getting the right treatment.
- Experts and machines don’t always agree on how to classify sleep, with people only agreeing about 83% of the time.
- The study talks about two types of uncertainty—aleatoric and epistemic—and suggests ways to better understand these to improve how we analyze sleep stages in the future.
Article Abstract
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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| http://dx.doi.org/10.1093/sleep/zsac134 | DOI Listing |
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