  Polysomnography (PSG) is the gold standard for diagnosing sleep obstructive
apnea (OSA). It allows monitoring of breathing events throughout the night. The
detection of these events is usually done by trained sleep experts. However,
this task is tedious, highly time-consuming and subject to important
inter-scorer variability. In this study, we adapted our state-of-the-art deep
learning method for sleep event detection, DOSED, to the detection of sleep
breathing events in PSG for the diagnosis of OSA. We used a dataset of 52 PSG
recordings with apnea-hypopnea event scoring from 5 trained sleep experts. We
assessed the performance of the automatic approach and compared it to the
inter-scorer performance for both the diagnosis of OSA severity and, at the
microscale, for the detection of single breathing events. We observed that
human sleep experts reached an average accuracy of 75\% while the automatic
approach reached 81\% for sleep apnea severity diagnosis. The F1 score for
individual event detection was 0.55 for experts and 0.57 for the automatic
approach, on average. These results demonstrate that the automatic approach can
perform at a sleep expert level for the diagnosis of OSA.
