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