A novel approach for phenotypic characterization of sleep disorders

Obstructive sleep apnea (OSA) is associated with increased morbidity and mortality, afflicting nearly one billion individuals worldwide. Polysomnography is the current gold standard for diagnosing OSA. It is an expensive, timeconsuming exam that relies on a single numerical measure of the frequency of apneas and hypopneas per hour (AHI) to diagnose severity. Considering the rich physiological data collected in each overnight PSG, we foresee a large potential of using automated PSG analysis, leading to a more efficient and informative characterization of OSA pathophysiology. Therefore, we built a real-world dataset $(\mathrm{n}=10,000)$ from historical PSG registries, incorporating diverse physiological signals (EEG, ECG, airflow, oximetry, CO2, thoracoabdominal effort) and electronic health records. We implemented strategies to ensure data quality and representation, considering age distribution, sex balance, and inclusion of under-represented minorities, facilitating the conduct of quasi-experimental research within this dataset. Challenges in data curation included artifact identification, historical variability in scoring, and the range of diagnoses in the large cohort. A comparative analysis with other existing PSG study datasets highlighted significant discrepancies, including variations in demographic distributions, comorbidity profiles, longitudinal data continuity, and AHI. These differences emphasize the value of real-world clinical data in providing a comprehensive representation of patient conditions and outcomes, thereby offering deeper insights into the clinical epidemiology of sleep disorders. We anticipate using time series foundational models for the embedding generation and implementing new clinically meaningful metrics beyond AHI. Our proposed approach for the analysis of PSG studies is an unparalleled opportunity to create models that better decipher human physiological patterns, with potential applications extending sleep medicine to disciplines such as neurology, pulmonary, psychiatry, and cardiology.