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Certainty about uncertainty in sleep staging: a theoretical framework.

Hans van Gorp1,2, Iris A M Huijben1,3, Pedro Fonseca1,2

  • 1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.

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|June 8, 2022
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Summary
This summary is machine-generated.

This study introduces a framework to analyze uncertainty in sleep stage classification, distinguishing between aleatoric and epistemic uncertainty. This approach aims to improve the reliability of sleep disorder diagnosis.

Keywords:
aleatoricepistemichypnograminter-rater agreementmachine learningsleep staginguncertainty

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Area of Science:

  • Neuroscience
  • Medical Informatics
  • Machine Learning

Background:

  • Sleep stage classification is crucial for diagnosing sleep disorders.
  • Reliability in sleep staging is essential due to its clinical impact.
  • Existing human scorers and automated models exhibit uncertainty, with human agreement averaging 82.6%.

Purpose of the Study:

  • To provide a theoretical framework for discussing and analyzing uncertainty in sleep staging.
  • To introduce and differentiate between aleatoric and epistemic uncertainty in this context.
  • To offer recommendations for improving future sleep staging.

Main Methods:

  • Introduction of aleatoric and epistemic uncertainty concepts.
  • Discussion of the origins of these uncertainties in sleep staging.
  • Development of a framework to analyze and potentially mitigate these uncertainties.

Main Results:

  • A clear distinction between aleatoric and epistemic uncertainty in sleep staging is presented.
  • The framework facilitates a deeper understanding of where uncertainty arises.
  • Recommendations are provided for leveraging this framework to enhance sleep staging.

Conclusions:

  • Understanding and differentiating uncertainty types can improve sleep staging reliability.
  • The proposed framework offers a novel approach to addressing inherent uncertainties in automated and human sleep scoring.
  • This work paves the way for more robust sleep disorder diagnosis.