Automatic System for Obstructive Sleep Apnea Events Detection Using Convolutional Neural Network.

Abstract

Obstructive Sleep Apnea (OSA) is characterized by repetitive episodes of airflow reduction (hypopnea) or cessation (apnea), which, as a prevalent sleep disorder, can cause people to stop breathing for 10 to 30 seconds at a time and lead to serious problems such as daytime fatigue, impaired memory, and depression. This work intends to explore automatic detection of OSA events with 1-second annotation based on blood oxygen saturation, oronasal airflow, and ribcage and abdomen movements. Deep Learning (DL) technology, specifically, Convolutional Neural Network (CNN), is employed as a feature detector to learn the characteristics of the highorder correlation among visible data and corresponding labels. A fully-connected layer in the last stage of the CNN is connected to the output layer and constructs the desired number of outputs for sleep apnea events classification. A leave-one-out cross-validation has been conducted on the PhysioNet Sleep Database provided by St. Vincents University Hospital and University College Dublin, and an average accuracy of $79 .61$% across normal, hypopnea, and apnea, classes is achieved.