  Study objective: Clinical sleep analysis require manual analysis of sleep
patterns for correct diagnosis of sleep disorders. Several studies show
significant variability in scoring discrete sleep events. We wished to
investigate, whether an automatic method could be used for detection of
arousals (Ar), leg movements (LM) and sleep disordered breathing (SDB) events,
and if the joint detection of these events performed better than having three
separate models.
  Methods: We designed a single deep neural network architecture to jointly
detect sleep events in a polysomnogram. We trained the model on 1653 recordings
of individuals, and tested the optimized model on 1000 separate recordings. The
performance of the model was quantified by F1, precision, and recall scores,
and by correlating index values to clinical values using Pearson's correlation
coefficient.
  Results: F1 scores for the optimized model was 0.70, 0.63, and 0.62 for Ar,
LM, and SDB, respectively. The performance was higher, when detecting events
jointly compared to corresponding single-event models. Index values computed
from detected events correlated well with manual annotations ($r^2$ = 0.73,
$r^2$ = 0.77, $r^2$ = 0.78, respectively).
  Conclusion: Detecting arousals, leg movements and sleep disordered breathing
events jointly is possible, and the computed index values correlates well with
human annotations.
