Comparison of Dreem to Clinical PSG for Sleep Monitoring in Apnea Patients
Comparison of Dreem to Clinical PSG for Sleep Monitoring in Apnea Patients
This study aims to evaluate the accuracy of apnea detection and automated sleep analysis by
 the Dreem dry-EEG headband and deep learning algorithm in comparison to the consensus of 5
 sleep technologists' manual scoring of a gold-standard clinical polysomnogram (PSG) record in
 adults during a physician-referred overnight sleep study due to suspicion of sleep-disordered
 breathing.
The study will enroll up to 70 adults who are referred to the Stanford Sleep Medicine Center
 by their physician for an overnight polysomnographic sleep study due to suspicion of
 sleep-disordered breathing, with the aim of collecting 60 usable data sets (i.e., eligible
 subjects with high-quality PSG and Dreem recordings). Upon arrival to the clinic, patients
 provide informed consent, are interviewed to determine eligibility, and complete a detailed
 demographic, medical, health, sleep, and lifestyle questionnaire (Alliance Sleep
 Questionnaire; ASQ). After the ASQ, participants are fitted with the PSG and the Dreem
 headband by the sleep technologist. During the PSG sleep study, the Dreem headband records
 EEG, pulse, oxygen saturation (SO2), movement, and respiratory rate. Many participants may
 undergo a split-night study with a continuous positive airway pressure (CPAP) device during
 their participation, as deemed necessary by the clinical staff pursuant to the sleep study.

 The PSG data from the first 30 eligible participants will be manually scored by 5 sleep
 technologists. These manually-scored PSG data files (referred to as the training dataset)
 will be synchronized with Dreem data files from the same night and the synchronized files
 will be used to train Dreem's deep learning algorithms. Following training, the algorithms
 will be deployed to automatically score the final 30 participants' Dreem datasets (testing
 dataset). Finally, PSG records for the second 30 participants will be provided to the sponsor
 and manually scored by 5 sleep technologists. The manual scoring results will be compared to
 the Dreem automatic analysis to determine the accuracy of Dreem's apnea-hypopnea index (AHI)
 severity detection and sleep staging algorithms.
Inclusion Criteria:

 -  18-70 years of age

 -  Capable of providing informed consent

 -  Suspicion of sleep breathing disorder (both diagnostic and split-night studies)

 Exclusion Criteria:

 -  Concomitant diagnosis of a sleep disorder other than sleep apnea syndrome or insomnia

 -  Morbid obesity (BMI > 39)

 -  Use of benzodiazepines, nonbenzodiazepine (Z-drugs), or Gammahydroxybutyrate (GHB) the
 day/night of the study

 -  Concomitant diagnosis of cardiopulmonary or neurological comorbidities (such as heart
 failure, COPD, neurodegenerative conditions)