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A Systematic Review of Detecting Sleep Apnea Using Deep Learning

2019Journal article Open access
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dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorMendonça, Fábio
dc.contributor.authorRavelo-García, Antonio G.
dc.contributor.authorDias, Fernando Morgado
dc.date.accessioned2024-02-09T16:15:31Z
dc.date.available2024-02-09T16:15:31Z
dc.date.issued2019
dc.description.abstractSleep 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMostafa, S. S., Mendonça, F., G. Ravelo-García, A., & Morgado-Dias, F. (2019). A systematic review of detecting sleep apnea using deep learning. Sensors, 19(22), 4934.pt_PT
dc.identifier.doi10.3390/s19224934pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5547
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLaboratory for Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCNNpt_PT
dc.subjectDeep learningpt_PT
dc.subjectSleep apneapt_PT
dc.subjectSensors for sleep apneapt_PT
dc.subjectRNNpt_PT
dc.subjectDeep neural networkpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.subjectEscola Superior de Tecnologias e Gestãopt_PT
dc.titleA Systematic Review of Detecting Sleep Apnea Using Deep Learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory for Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50009%2F2019/PT
oaire.citation.issue22pt_PT
oaire.citation.startPage4934pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume19pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMostafa
person.familyNameSilva Mendonça
person.familyNameRavelo-García
person.familyNameMorgado-Dias
person.givenNameSheikh Shanawaz
person.givenNameFábio Rúben
person.givenNameAntonio G.
person.givenNameFernando
person.identifier34497
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-8512-965X
person.identifier.orcid0000-0001-7334-3993
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id55489640900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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