  Sleep stages pattern provides important clues in diagnosing the presence of
sleep disorder. By analyzing sleep stages pattern and extracting its features
from EEG, EOG, and EMG signals, we can classify sleep stages. This study
presents a novel classification model for predicting sleep stages with a high
accuracy. The main idea is to combine the generative capability of Deep Belief
Network (DBN) with a discriminative ability and sequence pattern recognizing
capability of Long Short-term Memory (LSTM). We use DBN that is treated as an
automatic higher level features generator. The input to DBN is 28 "handcrafted"
features as used in previous sleep stages studies. We compared our method with
other techniques which combined DBN with Hidden Markov Model (HMM).In this
study, we exploit the sequence or time series characteristics of sleep dataset.
To the best of our knowledge, most of the present sleep analysis from
polysomnogram relies only on single instanced label (nonsequence) for
classification. In this study, we used two datasets: an open data set that is
treated as a benchmark; the other dataset is our sleep stages dataset
(available for download) to verify the results further. Our experiments showed
that the combination of DBN with LSTM gives better overall accuracy 98.75\%
(Fscore=0.9875) for benchmark dataset and 98.94\% (Fscore=0.9894) for MKG
dataset. This result is better than the state of the art of sleep stages
classification that was 91.31\%.
