Enhanced Respiratory Polygraphy (PV_e) Using Single Channel EEG-based Automatic Sleep Scoring in Suspected Obstructive Sleep Apnea: Feasibility and Efficiency as Compared With Polygraphy and Polysomnography

Obstructive Sleep Apnea (OSA) remains underdiagnosed in 2022, as a result of the unawareness of its serious health-related consequences and the lack of diagnosis accessibility. Respiratory polygraphy (PV) is widely used as a screening tool and sometimes a diagnosis test, although polysomnography (PSG) remains the gold standard investigation as it provides complete information about sleep architecture and arousals. Thus, it has been shown that the Apnea Hypopnea Index (AHI) and Respiratory Disorder Index (RDI) are underestimated by PV vs PSG. Approaches to substitute PSG by simpler but equally efficient diagnosis tests have included devices aiming to record complementary signals and to analyze them with Artificial Intelligence. In this context, ASEEGA algorithm has demonstrated its performance for automatic sleep scoring in healthy individuals and patients with various sleep disorders, based on a single channel EEG analysis.

This study aims at comparing the real-life performance and feasibility of added single channel EEG automatic sleep scoring using ASEEGA to PV versus standard PV and PSG in adults referred to a regional sleep reference center for suspected OSA.

We hypothesize that this approach (1) is as accurate as PSG and more accurate that PV for AHI analysis, and (2) is less time-consuming than PSG.