  Clinical sleep analysis require manual analysis of sleep patterns for correct
diagnosis of sleep disorders. However, several studies have shown significant
variability in manual scoring of clinically relevant discrete sleep events,
such as arousals, leg movements, and sleep disordered breathing (apneas and
hypopneas). We investigated whether an automatic method could be used for event
detection and if a model trained on all events (joint model) performed better
than corresponding event-specific models (single-event models). We trained a
deep neural network event detection model on 1653 individual recordings and
tested the optimized model on 1000 separate hold-out recordings. F1 scores for
the optimized joint detection model were 0.70, 0.63, and 0.62 for arousals, leg
movements, and sleep disordered breathing, respectively, compared to 0.65,
0.61, and 0.60 for the optimized single-event models. Index values computed
from detected events correlated positively with manual annotations ($r^2$ =
0.73, $r^2$ = 0.77, $r^2$ = 0.78, respectively). We furthermore quantified
model accuracy based on temporal difference metrics, which improved overall by
using the joint model compared to single-event models. Our automatic model
jointly detects arousals, leg movements and sleep disordered breathing events
with high correlation with human annotations. Finally, we benchmark against
previous state-of-the-art multi-event detection models and found an overall
increase in F1 score with our proposed model despite a 97.5% reduction in model
size. Source code for training and inference is available at
https://github.com/neergaard/msed.git.
