Exploring the Role of Nocturnal Hypoxemia and Sleep Fragmentation in Memory Decline: Insights From Explainable Machine Learning Models

INTRODUCTION: Sleep-disordered breathing (SDB) is linked to memory decline, but the exact relationship between sleep fragmentation, nocturnal hypoxemia, and cognitive impairment remains unclear. OBJECTIVES: This study aimed to investigate the associations between micro-arousal burden, nocturnal oxygen desaturation, and memory decline in patients with moderate-to-severe OSA. METHODS: tests were initially utilized to characterize PSG disparities between the memory-normal and memory-decline groups. And interpretable machine learning algorithms, utilizing rigorously partitioned training and validation sets, were deployed to predict cognitive trajectories and elucidate the relative prognostic importance of specific sleep-related parameters. RESULTS: was elevated (76.68% vs. 74.39%, p = 0.009), maximal obstructive events were shorter (51.42 s vs. 57.49 s, p < 0.001), and obstructive desaturation events were fewer (180.33 vs. 219.70, p = 0.006), indicating a shift toward shallower, persistent desaturation morphologies. Furthermore, interpretable machine learning models, rigorously evaluated on the independent validation set, identified spontaneous NREM micro-arousals, total REM micro-arousals, and obstructive desaturation metrics as the highest-ranking predictive determinants of memory decline. CONCLUSIONS: Memory decline in SDB is more robustly associated with the morphological profile of oxygen exposure rather than absolute event frequencies. A hypopnea-dominant profile with mild, persistent low oxygen levels offers an associative framework for understanding cognitive decline. Future research and clinical interventions should prioritize hypoxic burden as a key factor in phenotype identification and memory decline treatment.