  Identifying sleep problem severity from overnight polysomnography (PSG)
recordings plays an important role in diagnosing and treating sleep disorders
such as the Obstructive Sleep Apnea (OSA). This analysis traditionally is done
by specialists manually through visual inspections, which can be tedious,
time-consuming, and is prone to subjective errors. One of the solutions is to
use Convolutional Neural Networks (CNN) where the convolutional and pooling
layers behave as feature extractors and some fully-connected (FCN) layers are
used for making final predictions for the OSA severity. In this paper, a CNN
architecture with 1D convolutional and FCN layers for classification is
presented. The PSG data for this project are from the Cleveland Children's
Sleep and Health Study database and classification results confirm the
effectiveness of the proposed CNN method. The proposed 1D CNN model achieves
excellent classification results without manually preprocesssing PSG signals
such as feature extraction and feature reduction.
