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

Abstract

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, δnorm, to better evaluate temporal dependencies. A simple feed forward architecture not only achieves comparable performance to most up-to-date complex architectures, but also does better encode the continuous temporal characteristics of sleep.Clinical relevance - A better understanding of the capability of the network in encoding sleep temporal patterns could lead to improve the automatic sleep scoring.