Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/64233
Title: A systematic review of detecting sleep apnea using deep learning
Authors: Shanawaz Mostafa, Sheikh
Mendonça, Fábio
Ravelo-García, Antonio G. 
Morgado-Dias, Fernando
UNESCO Clasification: 6105 Evaluación y diagnóstico en psicología
Keywords: CNN
Deep learning
Sleep apnea
Sensors for sleep apnea
RNN, et al
Issue Date: 2019
Journal: Sensors 
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.
URI: http://hdl.handle.net/10553/64233
ISSN: 1424-8220
DOI: 10.3390/s19224934
Source: Sensors (Switzerland) [ISSN 1424-8220], v. 19 (22), 4934
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