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The regulation of the autonomic nervous system changes with the sleep stagescausing variations in the physiological variables. We exploit these changeswith the aim of classifying the sleep stages in awake or asleep using pulseoximeter signals. We applied a recurrent neural network to heart rate andperipheral oxygen saturation signals to classify the sleep stage every 30seconds. The network architecture consists of two stacked layers ofbidirectional gated recurrent units (GRUs) and a softmax layer to classify theoutput. In this paper, we used 5000 patients from the Sleep Heart Health Studydataset. 2500 patients were used to train the network, and two subsets of 1250were used to validate and test the trained models. In the test stage, the bestresult obtained was 90.13% accuracy, 94.13% sensitivity, 80.26% specificity,92.05% precision, and 84.68% negative predictive value. Further, the Cohen'sKappa coefficient was 0.74 and the average absolute error percentage to theactual sleep time was 8.9%. The performance of the proposed network iscomparable with the state-of-the-art algorithms when they use much moreinformative signals (except those with EEG).

Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals