Sleep Apnea Events Detection Using Deep Learning Techniques
This research underlines an automated approach for detecting sleep apnea events from sleep studies. The Polysomnogram test is the gold standard for diagnosing sleep apnea. Unfortunately, it is expensive, time-consuming, and uncomfortable for patients. We selected signals that can be simply obtained by using a portable fingertip pulse oximeter and hexoskin smart shirt. Hence, the cost of polysomnography will be reduced by utilizing less equipment and sufficient at the same time. Therefore, the scientific value of this research is to simplify the used ways by other sleep experts in this field. Two sleep apnea databases were used to train and test four deep learning models. Three physiological signals were combined to form one window of 60 seconds in size. Deep learning approaches were proved to be sufficient in detecting apnea events depending on data quality and the neural network architecture. The hybrid model outperformed other models with 97% and 92% of accuracy.