  Polysomnography (PSG) is a type of sleep study that records multimodal
physiological signals and is widely used for purposes such as sleep staging and
respiratory event detection. Conventional machine learning methods assume that
each sleep study is associated with a fixed set of observed modalities and that
all modalities are available for each sample. However, noisy and missing
modalities are a common issue in real-world clinical settings. In this study,
we propose a comprehensive pipeline aiming to compensate for the missing or
noisy modalities when performing sleep apnea detection. Unlike other existing
studies, our proposed model works with any combination of available modalities.
Our experiments show that the proposed model outperforms other state-of-the-art
approaches in sleep apnea detection using various subsets of available data and
different levels of noise, and maintains its high performance (AUROC>0.9) even
in the presence of high levels of noise or missingness. This is especially
relevant in settings where the level of noise and missingness is high (such as
pediatric or outside-of-clinic scenarios).
