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.
Highlights
The type of studies used in our system is type 4 sleep studies, which refers to continuous single bio-parameter or dual-bio parameter recording
The objective of proposing the biological signal to deep learning model is to fill the gap between the nature of deep learning and continuous nature of the biological signal from the sensors
A deep learning approach using convolutional neural network is proposed for the detection of sleep apnea in order to explore the segment of the time series data whether apnea occurs or not
Summary
The type of studies used in our system is type 4 sleep studies, which refers to continuous single bio-parameter or dual-bio parameter recording. The primary purpose of using the deep convolutional neural network model for the detection of sleep apnea is to learn parameters or features for the model from the training dataset [7]. The relationship between the interpretability of the different parameters and the performance of the deep convolutional model is a challenging task for the detection of sleep apnea. The use of TEHNIČKI GLASNIK 13, 4(2019), 261-266 convolutional neural network model is a plausible and proper solution to interpret and learn the time series nature of the high frequency SPO2 signal [30,31,32]. The major contribution of the paper can be summarized as follows: A deep learning approach using convolutional neural network is proposed for the detection of sleep apnea in order to explore the segment of the time series data whether apnea occurs or not.
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