Temporal dependency in automatic sleep scoring via deep learning based architectures: An empirical study.

The present study evaluates how effectively a deep learning based sleep scoring system does encode the temporal dependency from raw polysomnography signals. An exhaustive range of neural networks, including state of the art architecture, have been used in the evaluation. The architectures have been assessed using a single-channel EEG Fpz-Cz from the open source Sleep-EDF expanded database. The best performing model reached an overall accuracy of 85.2% and a Cohen's kappa of 0.8, with an F1-score of stage N1 equal to 50.2%. We have introduced a new metric, δ