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Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach.

Aria KhademiYasser El-ManzalawyLindsay MasterOrfeu M BuxtonVasant G Honavar
Published in: Nature and science of sleep (2019)
Personalized machine learning models of sleep-wake states outperform their generalized counterparts in terms of estimating sleep parameters and are indistinguishable from PSG labeled sleep-wake states. Personalized machine learning models can be used in actigraphy studies of sleep health and potentially screening for some sleep disorders.
Keyphrases
  • machine learning
  • sleep quality
  • physical activity
  • healthcare
  • artificial intelligence
  • public health
  • mental health
  • big data