Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.13/5555 |
Resumo: | Obstructive 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. |
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Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detectionBiomedical signal processingCNNGenetic algorithmsMachine intelligenceMedical expert systemsPareto optimizationSleep apneaSpO2.Faculdade de Ciências Exatas e da EngenhariaObstructive 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.IEEEDigitUMaMostafa, Sheikh ShanawazMendonça, FábioRavelo-García, Antonio G.Ravelo-García, Antonio G.Ravelo-García, Antonio G.Ravelo-García, Antonio G.Dias, Fernando Morgado2024-02-15T16:15:24Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.13/5555engMostafa, 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, 129586-129599.10.1109/ACCESS.2020.3009149info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-18T05:33:23Zoai:digituma.uma.pt:10400.13/5555Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:38:50.090076Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection |
title |
Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection |
spellingShingle |
Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection Mostafa, Sheikh Shanawaz Biomedical signal processing CNN Genetic algorithms Machine intelligence Medical expert systems Pareto optimization Sleep apnea SpO2 . Faculdade de Ciências Exatas e da Engenharia |
title_short |
Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection |
title_full |
Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection |
title_fullStr |
Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection |
title_full_unstemmed |
Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection |
title_sort |
Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection |
author |
Mostafa, Sheikh Shanawaz |
author_facet |
Mostafa, Sheikh Shanawaz Mendonça, Fábio Ravelo-García, Antonio G. Dias, Fernando Morgado |
author_role |
author |
author2 |
Mendonça, Fábio Ravelo-García, Antonio G. Dias, Fernando Morgado |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
DigitUMa |
dc.contributor.author.fl_str_mv |
Mostafa, Sheikh Shanawaz Mendonça, Fábio Ravelo-García, Antonio G. Ravelo-García, Antonio G. Ravelo-García, Antonio G. Ravelo-García, Antonio G. Dias, Fernando Morgado |
dc.subject.por.fl_str_mv |
Biomedical signal processing CNN Genetic algorithms Machine intelligence Medical expert systems Pareto optimization Sleep apnea SpO2 . Faculdade de Ciências Exatas e da Engenharia |
topic |
Biomedical signal processing CNN Genetic algorithms Machine intelligence Medical expert systems Pareto optimization Sleep apnea SpO2 . Faculdade de Ciências Exatas e da Engenharia |
description |
Obstructive 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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 2020-01-01T00:00:00Z 2024-02-15T16:15:24Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.13/5555 |
url |
http://hdl.handle.net/10400.13/5555 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Mostafa, 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. 10.1109/ACCESS.2020.3009149 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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