  Sleep arousals transition the depth of sleep to a more superficial stage. The
occurrence of such events is often considered as a protective mechanism to
alert the body of harmful stimuli. Thus, accurate sleep arousal detection can
lead to an enhanced understanding of the underlying causes and influencing the
assessment of sleep quality. Previous studies and guidelines have suggested
that sleep arousals are linked mainly to abrupt frequency shifts in EEG
signals, but the proposed rules are shown to be insufficient for a
comprehensive characterization of arousals. This study investigates the
application of five recent convolutional neural networks (CNNs) for sleep
arousal detection and performs comparative evaluations to determine the best
model for this task. The investigated state-of-the-art CNN models have
originally been designed for image or speech processing. A detailed set of
evaluations is performed on the benchmark dataset provided by
PhysioNet/Computing in Cardiology Challenge 2018, and the results show that the
best 1D CNN model has achieved an average of 0.31 and 0.84 for the area under
the precision-recall and area under the ROC curves, respectively.
