Sleep apnea detection using deep learning - Chaw, Hnin Thiri (Array, 2019)
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.