  The gold standard to assess respiration during sleep is polysomnography; a
technique that is burdensome, expensive (both in analysis time and measurement
costs), and difficult to repeat. Automation of respiratory analysis can improve
test efficiency and enable accessible implementation opportunities worldwide.
Using 9,656 polysomnography recordings from the Massachusetts General Hospital
(MGH), we trained a neural network (WaveNet) based on a single respiratory
effort belt to detect obstructive apnea, central apnea, hypopnea and
respiratory-effort related arousals. Performance evaluation included
event-based and recording-based metrics - using an apnea-hypopnea index
analysis. The model was further evaluated on a public dataset, the
Sleep-Heart-Health-Study-1, containing 8,455 polysomnographic recordings. For
binary apnea event detection in the MGH dataset, the neural network obtained an
accuracy of 95%, an apnea-hypopnea index $r^2$ of 0.89 and area under the curve
for the receiver operating characteristics curve and precision-recall curve of
0.93 and 0.74, respectively. For the multiclass task, we obtained varying
performances: 81% of all labeled central apneas were correctly classified,
whereas this metric was 46% for obstructive apneas, 29% for respiratory effort
related arousals and 16% for hypopneas. The majority of false predictions were
misclassifications as another type of respiratory event. Our fully automated
method can detect respiratory events and assess the apnea-hypopnea index with
sufficient accuracy for clinical utilization. Differentiation of event types is
more difficult and may reflect in part the complexity of human respiratory
output and some degree of arbitrariness in the clinical thresholds and criteria
used during manual annotation.
