Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals
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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 92.05 Kappa coefficient was 0.74 and the average absolute error percentage to the actual sleep time was 8.9 comparable with the state-of-the-art algorithms when they use much more informative signals (except those with EEG).
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