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A Systematic Review of Detecting Sleep Apnea Using Deep Learning

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Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
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Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal
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Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas, Spain
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Faculdade de Ciências Exatas e da Engenharia, Universidade da Madeira, 9000-082 Funchal, Portugal
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(22), 4934; https://doi.org/10.3390/s19224934
Received: 13 September 2019 / Revised: 29 October 2019 / Accepted: 4 November 2019 / Published: 12 November 2019
(This article belongs to the Section Biomedical Sensors)
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. View Full-Text
Keywords: CNN; deep learning; sleep apnea; sensors for sleep apnea; RNN; deep neural network CNN; deep learning; sleep apnea; sensors for sleep apnea; RNN; deep neural network
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MDPI and ACS Style

Mostafa, S.S.; Mendonça, F.; G. Ravelo-García, A.; Morgado-Dias, F. A Systematic Review of Detecting Sleep Apnea Using Deep Learning. Sensors 2019, 19, 4934. https://doi.org/10.3390/s19224934

AMA Style

Mostafa SS, Mendonça F, G. Ravelo-García A, Morgado-Dias F. A Systematic Review of Detecting Sleep Apnea Using Deep Learning. Sensors. 2019; 19(22):4934. https://doi.org/10.3390/s19224934

Chicago/Turabian Style

Mostafa, Sheikh S.; Mendonça, Fábio; G. Ravelo-García, Antonio; Morgado-Dias, Fernando. 2019. "A Systematic Review of Detecting Sleep Apnea Using Deep Learning" Sensors 19, no. 22: 4934. https://doi.org/10.3390/s19224934

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