  The regulation of the autonomic nervous system changes with the sleep stages
causing variations in the physiological variables. We exploit these changes
with the aim of classifying the sleep stages in awake or asleep using pulse
oximeter signals. We applied a recurrent neural network to heart rate and
peripheral oxygen saturation signals to classify the sleep stage every 30
seconds. The network architecture consists of two stacked layers of
bidirectional gated recurrent units (GRUs) and a softmax layer to classify the
output. In this paper, we used 5000 patients from the Sleep Heart Health Study
dataset. 2500 patients were used to train the network, and two subsets of 1250
were used to validate and test the trained models. In the test stage, the best
result obtained was 90.13% accuracy, 94.13% sensitivity, 80.26% specificity,
92.05% precision, and 84.68% negative predictive value. Further, the Cohen's
Kappa coefficient was 0.74 and the average absolute error percentage to the
actual sleep time was 8.9%. The performance of the proposed network is
comparable with the state-of-the-art algorithms when they use much more
informative signals (except those with EEG).
