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Analytics of Prediction for Insomnia and Cognitive Impairment

Team Members:
  • Ali Al Abdullatif
  • Amy He
  • Elysia Chou
  • John Lin
  • Jung Min Lee
  • Sridevi Sarma, PhD
  • Charlene Gamaldo, MD
  • Rachel Salas, MD
  • Alyssa Gamaldo, PhD


Insomnia is the most common sleep disorder in the United States and affects approximately 60 million Americans. Patients with insomnia also have an increased risk for cognitive impairment. However, insomnia and cognitive impairment are currently diagnosed through subjective clinical interviews with sleep experts and self-reported screening tools. The goal of this study is to create an objective computational model that can predict insomnia and cognitive impairment from physiological recordings obtained during sleep. We construct and test our model using data collected from either polysomnography that was conducted in a sleep center or an ambulatory Sleep Profilerâ„¢ device that can be worn at home. Our cohort consists of HIV seropositive patients (n = 31) who are known to suffer from both insomnia and cognitive impairment. Physiological signals from these patients can be used to derive hundreds of sleep stage-specific features, such as average power of a given frequency band from a specific brain area. Generalized Linear Models (GLMs) are then utilized to select the most predictive features for insomnia and cognitive impairment. If successful, our model can provide an accessible and objective means of diagnosing insomnia and identifying patients with cognitive impairment for cognitive behavioral therapy.

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