  Automatic sleep staging is a challenging problem and state-of-the-art
algorithms have not yet reached satisfactory performance to be used instead of
manual scoring by a sleep technician. Much research has been done to find good
feature representations that extract the useful information to correctly
classify each epoch into the correct sleep stage. While many useful features
have been discovered, the amount of features have grown to an extent that a
feature reduction step is necessary in order to avoid the curse of
dimensionality. One reason for the need of such a large feature set is that
many features are good for discriminating only one of the sleep stages and are
less informative during other stages. This paper explores how a second feature
representation over a large set of pre-defined features can be learned using an
auto-encoder with a selective attention for the current sleep stage in the
training batch. This selective attention allows the model to learn feature
representations that focuses on the more relevant inputs without having to
perform any dimensionality reduction of the input data. The performance of the
proposed algorithm is evaluated on a large data set of polysomnography (PSG)
night recordings of patients with sleep-disordered breathing. The performance
of the auto-encoder with selective attention is compared with a regular
auto-encoder and previous works using a deep belief network (DBN).
