Abstract
Sleep apnea is a prevalent disorder with serious cardiovascular and cognitive impacts. The standard diagnosis, based on manual analysis of polysomnography (PSG), is an expensive, time-consuming, and access-restricted process, which hinders large-scale screening. Existing artificial intelligence models for automated diagnosis often assume the synchronicity of physiological signals, ignorin…