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Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection

2020Journal 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.accessioned2024-02-15T16:15:24Z
dc.date.available2024-02-15T16:15:24Z
dc.date.issued2020
dc.description.abstractObstructive sleep apnea (OSA) is a common sleep disorder characterized by interrupted breathing during sleep. Because of the cost, complexity, and accessibility issue related to polysomnography, the gold standard test for apnea detection, automation of the diagnostic test based on a simpler method is desired. Several signals can be used for apnea detection, such as airflow and electrocardiogram. However, the reduction of airflow normally leads to a decrease in the blood oxygen saturation level (SpO2). This signal is usually measured by a pulse oximeter, a sensor that is cheap, portable, and easy to assemble. Therefore, the SpO2 was chosen as the reference signal. Feature based classifiers with shallow neural networks have been developed to provide apnea detection using SpO2. However, two main issues arise, the need for feature creation and the selection of the more relevant features. Deep neural networks can solve these issues by employing featureless methods. Among multiple deep classifiers that have been developed, convolution neural networks (CNN) are gaining popularity. However, the selection of the CNN structure and hyperparameters are typically done by experts, where prior knowledge is essential. With these problems in mind, an algorithm for automatic structure selection and hyper parameterization of a one dimension CNN was developed to detect OSA events using only the SpO2 signal. Three different input sizes and databases were tested, and the best model achieved an average accuracy, sensitivity, and specificity of 94%, 92%, and 96%, respectively.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMostafa, S. S., Mendonca, F., Ravelo-Garcia, A. G., Juliá-Serdá, G. G., & Dias, F. M. (2020). Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection. IEEE Access, 8, 129586-129599.pt_PT
dc.identifier.doi10.1109/ACCESS.2020.3009149pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5555
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationLaboratory for Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBiomedical signal processingpt_PT
dc.subjectCNNpt_PT
dc.subjectGenetic algorithmspt_PT
dc.subjectMachine intelligencept_PT
dc.subjectMedical expert systemspt_PT
dc.subjectPareto optimizationpt_PT
dc.subjectSleep apneapt_PT
dc.subjectSpO2pt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleMulti-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detectionpt_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.endPage129599pt_PT
oaire.citation.startPage129586pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume8pt_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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