  Patients with sleep disorders can better manage their lifestyle if they know
about their special situations. Detection of such sleep disorders is usually
possible by analyzing a number of vital signals that have been collected from
the patients. To simplify this task, a number of Automatic Sleep Stage
Recognition (ASSR) methods have been proposed. Most of these methods use
temporal-frequency features that have been extracted from the vital signals.
However, due to the non-stationary nature of sleep signals, such schemes are
not leading an acceptable accuracy. Recently, some ASSR methods have been
proposed which use deep neural networks for unsupervised feature extraction. In
this paper, we proposed to combine the two ideas and use both
temporal-frequency and unsupervised features at the same time. To augment the
time resolution, each standard epoch is segmented into 5 sub-epochs.
Additionally, to enhance the accuracy, we employ three classifiers with
different properties and then use an ensemble method as the ultimate
classifier. The simulation results show that the proposed method enhances the
accuracy of conventional ASSR methods.
