  Study Objective: Sleep is reflected not only in the electroencephalogram but
also in heart rhythms and breathing patterns. Therefore, we hypothesize that it
is possible to accurately stage sleep based on the electrocardiogram (ECG) and
respiratory signals. Methods: Using a dataset including 8,682 polysomnographs,
we develop deep neural networks to stage sleep from ECG and respiratory
signals. Five deep neural networks consisting of convolutional networks and
long short-term memory networks are trained to stage sleep using heart and
breathing, including the timing of R peaks from ECG, abdominal and chest
respiratory effort, and the combinations of these signals. Results: ECG in
combination with the abdominal respiratory effort achieve the best performance
for staging all five sleep stages with a Cohen's kappa of 0.600 (95% confidence
interval 0.599 -- 0.602); and 0.762 (0.760 -- 0.763) for discriminating awake
vs. rapid eye movement vs. non-rapid eye movement sleep. The performance is
better for young participants and for those with a low apnea-hypopnea index,
while it is robust for commonly used outpatient medications. Conclusions: Our
results validate that ECG and respiratory effort provide substantial
information about sleep stages in a large population. It opens new
possibilities in sleep research and applications where electroencephalography
is not readily available or may be infeasible, such as in critically ill
patients.
