Open AccessSleep apnea detection using deep learning
Author(s) -
Hnin Thiri Chaw,
Sinchai Kamolphiwong,
Krongthong Wongsritrang
Publication year - 2019
Publication title -
tehnički glasnik
Language(s) - English
Resource type - Journals
eISSN - 1848-5588
pISSN - 1846-6168
DOI - 10.31803/tg-20191104191722
Subject(s) - polysomnography , sleep (system call) , sleep apnea , apnea , medicine , convolutional neural network , breathing , deep learning , computer science , slow wave sleep , sleep stages , artificial intelligence , anesthesia , electroencephalography , psychiatry , operating system
Sleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full night polysomnography are time consuming and it requires much space for sleep recording such as sleep lab comparing to type 4 sleep studies. The detection of sleep apnea using deep convolutional neural network model based on SPO2 sensor is the valid alternative for efficient polysomnography and it is portable and cost effective. The total number of samples from SPO2 sensors of 50 patients that is used in this study is 190,000. The performance of the overall accuracy of sleep apnea detection is 91.3085% with the loss rate of 2.3 using cross entropy cost function using deep convolutional neural network.