Automate Obstructive Sleep Apnea Diagnosis Using Convolutional Neural Networks

Identifying sleep problem severity from overnight polysomnography (PSG) recordings plays an important role in diagnosing and treating 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, prone to subjective errors. One of solutions use Convolutional Neural Networks (CNN) where convolutional pooling layers behave feature extractors some fully-connected (FCN) are used for making final predictions OSA severity. In this paper, a CNN architecture with 1D FCN classification presented. The PSG data project Cleveland Children’s Health Study database results confirm effectiveness proposed method. model achieves excellent without preprocesssing signals extraction reduction.