Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach.
Aria KhademiYasser El-ManzalawyLindsay MasterOrfeu M BuxtonVasant G HonavarPublished 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.