An EEMD-derived slow-wave activity metric improves association with obstructive sleep apnea severity beyond...

Conventional sleep staging relies on visual criteria, overlooking the contiguous transitions of sleep configuration and individual variability of brainwaves. Consequently, whether conventional N3 sleep metrics sufficiently capture sleep architecture alterations associated with heterogeneous sleep disorders such as obstructive sleep apnea (OSA) remains uncertain. Our objective was to determine whether slow-wave sleep (SWS) derived from an elevated slow-wave activity (SWA)-based approach exhibits stronger associations with the apnea-hypopnea index (AHI) than conventional N3 sleep.

We analyzed polysomnography (PSG) data from the community-based Sleep Heart Health Study (SHHS), comprising 823 eligible participants (age: 45-90 years; 412 males and 411 females). Sleep electroencephalogram (EEG) was decomposed using ensemble empirical mode decomposition (EEMD). Slow-wave-related intrinsic mode functions (IMFs) were identified to derive normalized SWA, and empirically informed and data-driven thresholds were applied to detect SWS episodes. Associations between SWS metrics and AHI were compared to those from traditional N3 sleep. Participants were further stratified into high- and low-risk groups based on heart rate response (ΔHR) and hypoxia burden (HB). Correlation trends were evaluated across different stratification thresholds.

Compared with traditional sleep scoring, SWS quantification yielded substantially stronger correlations with AHI, with correlation coefficients improving significantly from -0.27 to -0.28 for traditional N3-based metrics to -0.36 to -0.39 for SWS-based metrics. Stratification analyses revealed that this enhancement in correlation strength was markedly greater in the high-risk group (elevated ΔHR or HB), reaching correlation coefficients of up to -0.70 under certain stratification thresholds.

This study identifies SWS based on intrinsic EEG dynamical fluctuations rather than empirical visual thresholds. EEMD-derived SWA shows stronger associations with OSA severity than conventional N3 staging.