  In this work we describe a new deep learning approach for automatic sleep
staging, and carry out its validation by addressing its generalization
capabilities on a wide range of sleep staging databases. Prediction
capabilities are evaluated in the context of independent local and external
generalization scenarios. Effectively, by comparing both procedures it is
possible to better extrapolate the expected performance of the method on the
general reference task of sleep staging, regardless of data from a specific
database. In addition, we examine the suitability of a novel approach based on
the use of an ensemble of individual local models and evaluate its impact on
the resulting inter-database generalization performance. Validation results
show good general performance, as compared to the expected levels of human
expert agreement, as well as state-of-the-art automatic sleep staging
approaches
