Novel Digital Markers of Sleep Dynamics: A Causal Inference Approach Revealing Age and Gender Phenotypes in Obstructive Sleep Apnea

Most individuals with sleep-disorders remain undiagnosed due to unawareness of symptoms or the high cost of polysomno-graphic (PSG) studies, impacting quality of life. Despite evidence that sleep-disorders alter sleep-stage-dynamics, clinical practice resists including these parameters in PSG-reports. We introduce a novel digital sleep-fingerprint , leveraging the matrix of sleep-stage-transition-proportions, enabling the derivation of several novel digital-markers and investigation of dynamics mechanisms. Using causal inference we address confounding in an observational clinical database and estimate personalized markers across ages, genders, and Obstructive-Sleep-Apnea (OSA) severities. Notably, our approach adjusts for five categories of sleep-wake-related-comorbidities, an aspect ignored in existing research, impacting 48.6% of OSA-subjects in our data. Key markers proposed, including NREM-REM-oscillations and sleep-stage-specific-fragmentations, were significantly increased across all OSA-severities and demographics. We also identified several OSA-gender-phenotypes, suggesting higher vulnerability of females to awakening and REM-sleep disruptions. Considering advances in automated-sleep-scoring and wearables, our approach can enable novel, low-cost screening tools.