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

A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification