A Systematic Review of Detecting Sleep Apnea Using Deep Learning

Abstract

Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.

Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals

“…A recent comprehensive survey showed that the vast majority of deep learning methods for sleep breathing disorders have been devoted to signals [ 46 ]. Few studies considered single channel respiration inputs for deep learning apnea detection models [ 58 , 59 , 60 , 61 ].…”

“…Recent studies that investigated deep learning methods in sleep apnea show improvement over classical machine learning methods [ 46 ]. While the majority of studies considered signal [ 18 , 19 , 20 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ], a very limited number of them investigated respiration signals.…”

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

“…On the other hand, a qualitative technique using a random forest classifier allowed to report 86% of accuracy (with the sensitivity of 96% and the specificity of 76%), when setting a binary border at the level of AHI=15. Interestingly, in the review on detecting sleep apnea using deep learning methods [37], only a single study focused on breathing sounds by Kim et al was found [38]. In that study, they used breathing sounds with the accuracy of 88.3% for four-group AHI classification, and 92.5% using binary classification.…”

Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection

“…Full reviews on commercial devices [11], algorithm performance [12], deep learning [13] and classification methods [14] for OSA detection have been performed. Different source signals, such as oximetry [15] [16], electrocardiogram (ECG) [17] [18], respiration [19] [20], and sound [21], have been previously evaluated.…”