Original scientific paper

https://doi.org/10.31803/tg-20191104191722

Sleep apnea detection using deep learning

Hnin Thiri Chaw ; Dept of Computer Engineering, Prince of Songkla University, 90112 Hat Yai, Thailand
Sinchai Kamolphiwong ; Dept of Computer Engineering, Prince of Songkla University, 90112 Hat Yai, Thailand
Krongthong Wongsritrang ; Dept. of Otolaryngology Head Neck Surgery, Songklanagrind Hospital, 90112 Hat Yai, Thailand

Abstract

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.

Keywords

continuous single bio-parameter recording; deep convolutional neural network; deep learning; type 4 sleep study; portable sleep apnea detection

Hrčak ID:

229492

URI

https://hrcak.srce.hr/229492

Publication date:

11.12.2019.

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