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Sleep disorders are very widespread in the world population and suffer from ageneralized underdiagnosis, given the complexity of their diagnostic methods.Therefore, there is an increasing interest in developing simpler screeningmethods. A pulse oximeter is an ideal device for sleep disorder screeningssince it is a portable, low-cost and accessible technology. This device canprovide an estimation of the heart rate (HR), which can be useful to obtaininformation regarding the sleep stage. In this work, we developed a networkarchitecture with the aim of classifying the sleep stage in awake or asleepusing only HR signals from a pulse oximeter. The proposed architecture has twofundamental parts. The first part has the objective of obtaining arepresentation of the HR by using temporal convolutional networks. Then, theobtained representation is used to feed the second part, which is based ontransformers, a model built solely with attention mechanisms. Transformers areable to model the sequence, learning the transition rules between sleep stages.The performance of the proposed method was evaluated on Sleep Heart HealthStudy dataset, composed of 5000 healthy and pathological subjects. The datasetwas split into three subsets: 2500 for training, $1250$ for validating, and1250 for testing. The overall accuracy, specificity, sensibility, and Cohen'sKappa coefficient were 90.0%, 94.9%, 78.1%, and 0.73.

Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signals