  Over the years, several approaches have tried to tackle the problem of
performing an automatic scoring of the sleeping stages. Although any
polysomnography usually collects over a dozen of different signals, this
particular problem has been mainly tackled by using only the
Electroencephalograms presented in those records. On the other hand, the other
recorded signals have been mainly ignored by most works. This paper explores
and compares the convenience of using additional signals apart from
electroencephalograms. More specifically, this work uses the SHHS-1 dataset
with 5,804 patients containing an electromyogram recorded simultaneously as two
electroencephalograms. To compare the results, first, the same architecture has
been evaluated with different input signals and all their possible
combinations. These tests show how, using more than one signal especially if
they are from different sources, improves the results of the classification.
Additionally, the best models obtained for each combination of one or more
signals have been used in ensemble models and, its performance has been
compared showing the convenience of using these multi-signal models to improve
the classification. The best overall model, an ensemble of Depth-wise
Separational Convolutional Neural Networks, has achieved an accuracy of 86.06\%
with a Cohen's Kappa of 0.80 and a $F_{1}$ of 0.77. Up to date, those are the
best results on the complete dataset and it shows a significant improvement in
the precision and recall for the most uncommon class in the dataset.
