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)

We plan to share de-identified physiological data in an open-source format to the public. However, this will only occur if the data files are of sufficient quality and likely to be of use to the scientific community. This decision may also depend on certain legal or business constraints.