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A behavioral approach to annotating sleep in infants: Building on the classic framework.

Renée A OtteXi LongJoyce H D M Westerink
Published in: Physiological reports (2022)
In infants, monitoring and assessment of sleep can offer valuable insights into sleep problems and neuro-cognitive development. The gold standard for sleep measurements is polysomnography (PSG), but this is rather obtrusive, and unpractical in non-laboratory situations. Behavioral observations constitute a non-obtrusive, infant-friendly alternative. In the current methodological paper, we describe and validate a behavior-based framework for annotating infant sleep states. For development of the framework, we used existing sleep data from an in-home study with an unobtrusive test setup. Participants were 20 infants with a mean age of 180 days. Framework development was based on Prechtl's method. We added rules and guidelines based on discussions and consent among annotators. Key to using our framework is combining data from several modalities, for example, closely observing the frequency, type, and quality of movements, breaths, and sounds an infant makes, while taking the context into account. For a first validation of the framework, we set up a small study with 14 infants (mean age 171 days), in which they took their day-time nap in a laboratory setting. They were continuously monitored by means of PSG, as well as by the test setup from the in-home study. Recordings were annotated based both on PSG and our framework, and then compared. Data showed that for scoring wake vs. active sleep vs. quiet sleep the framework yields results comparable to PSG with a Cohen's Kappa agreement of ≥0.74. Future work with a larger cohort is necessary for further validating this framework, and with clinical populations for determining whether it can be generalized to these populations as well.
Keyphrases
  • sleep quality
  • physical activity
  • healthcare
  • electronic health record
  • mental health
  • obstructive sleep apnea
  • big data
  • machine learning
  • depressive symptoms
  • silver nanoparticles