  As sleep disorders are becoming more prevalent there is an urgent need to
classify sleep stages in a less disturbing way.In particular, sleep-stage
classification using simple sensors, such as single-channel
electroencephalography (EEG), electrooculography (EOG), electromyography (EMG),
or electrocardiography (ECG) has gained substantial interest. In this study, we
proposed a sleep model that predicts the next sleep stage and used it to
improve sleep classification accuracy. The sleep models were built using
sleep-sequence data and employed either statistical $n$-gram or deep neural
network-based models. We developed beam-search decoding to combine the
information from the sensor and the sleep models. Furthermore, we evaluated the
performance of the $n$-gram and long short-term memory (LSTM) recurrent neural
network (RNN)-based sleep models and demonstrated the improvement of
sleep-stage classification using an EOG sensor. The developed sleep models
significantly improved the accuracy of sleep-stage classification, particularly
in the absence of an EEG sensor.
