Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/146266 |
Resumo: | Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning-among others-mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the InceptionV3 model, which achieved a score of 88.06%. |
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Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer LearningCiências Tecnológicas, Ciências médicas e da saúdeTechnological sciences, Medical and Health sciencesHeart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning-among others-mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the InceptionV3 model, which achieved a score of 88.06%.2022-122022-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleimage/pngapplication/pdfhttps://hdl.handle.net/10216/146266eng1424-321010.3390/s22249569Ali HarimiYahya MajdAbdorreza Alavi GharahbaghVahid HajihashemiZeynab EsmaileyanJosé J. M. MachadoJoão Manuel R. S. Tavaresinfo: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:RCAAP2023-11-29T13:17:24Zoai:repositorio-aberto.up.pt:10216/146266Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:37:38.474016Repositório 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 |
Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning |
title |
Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning |
spellingShingle |
Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning Ali Harimi Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
title_short |
Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning |
title_full |
Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning |
title_fullStr |
Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning |
title_full_unstemmed |
Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning |
title_sort |
Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning |
author |
Ali Harimi |
author_facet |
Ali Harimi Yahya Majd Abdorreza Alavi Gharahbagh Vahid Hajihashemi Zeynab Esmaileyan José J. M. Machado João Manuel R. S. Tavares |
author_role |
author |
author2 |
Yahya Majd Abdorreza Alavi Gharahbagh Vahid Hajihashemi Zeynab Esmaileyan José J. M. Machado João Manuel R. S. Tavares |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Ali Harimi Yahya Majd Abdorreza Alavi Gharahbagh Vahid Hajihashemi Zeynab Esmaileyan José J. M. Machado João Manuel R. S. Tavares |
dc.subject.por.fl_str_mv |
Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
topic |
Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
description |
Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning-among others-mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the InceptionV3 model, which achieved a score of 88.06%. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12 2022-12-01T00:00:00Z |
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 |
https://hdl.handle.net/10216/146266 |
url |
https://hdl.handle.net/10216/146266 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1424-3210 10.3390/s22249569 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
image/png application/pdf |
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) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799135689512583168 |