Diagnosis of multiple sclerosis using multifocal ERG data feature fusion

The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI-port/scan 21 (Roland Consult) device from 15 eyes of patients...

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Autores: López-Dorado, A., Pérez, J., Rodrigo, M.J., Miguel-Jiménez, J.M., Ortiz, M., Santiago, L. de, López-Guillén, E., Blanco, R., Cavalliere, C., Sánchez Morla, E.M., Boquete, L., Garcia-Martin, E.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Zaragoza
Repositorio:Zaguán. Repositorio Digital de la Universidad de Zaragoza
OAI Identifier:oai:zaguan.unizar.es:151616
Acceso en línea:http://zaguan.unizar.es/record/151616
Access Level:acceso abierto
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spelling Diagnosis of multiple sclerosis using multifocal ERG data feature fusionLópez-Dorado, A.Pérez, J.Rodrigo, M.J.Miguel-Jiménez, J.M.Ortiz, M.Santiago, L. deLópez-Guillén, E.Blanco, R.Cavalliere, C.Sánchez Morla, E.M.Boquete, L.Garcia-Martin, E.The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI-port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants). For each group, the correlation with a normative database of adaptively filtered signals, based on empirical model decomposition (EMD) and three features from the continuous wavelet transform (CWT) domain, are obtained. Of the initial 40 features, the 4 most relevant are selected in two stages: a) using a filter method and b) using a wrapper-feature selection method. The Support Vector Machine (SVM) is used as a classifier. With the optimal CAD configuration, a Matthews correlation coefficient value of 0.89 (accuracy = 0.95, specificity = 1.0 and sensitivity = 0.93) is obtained. This study identified an outer retina dysfunction in patients with recent MS by analysing the outer retina responses in the mfERG and employing an SVM as a classifier. In conclusion, a promising new electrophysiological-biomarker method based on feature fusion for MS diagnosis was identified.2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://zaguan.unizar.es/record/151616reponame:Zaguán. Repositorio Digital de la Universidad de Zaragozainstname:Universidad de ZaragozaInglésinfo:eu-repo/grantAgreement/ES/ISCIII/PI17-01726info:eu-repo/grantAgreement/ES/ISCIII/RETICS-RD16-0008-029info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/DPI2017-88438-Rinfo:eu-repo/semantics/openAccessoai:zaguan.unizar.es:1516162026-05-29T13:59:51Z
dc.title.none.fl_str_mv Diagnosis of multiple sclerosis using multifocal ERG data feature fusion
title Diagnosis of multiple sclerosis using multifocal ERG data feature fusion
spellingShingle Diagnosis of multiple sclerosis using multifocal ERG data feature fusion
López-Dorado, A.
title_short Diagnosis of multiple sclerosis using multifocal ERG data feature fusion
title_full Diagnosis of multiple sclerosis using multifocal ERG data feature fusion
title_fullStr Diagnosis of multiple sclerosis using multifocal ERG data feature fusion
title_full_unstemmed Diagnosis of multiple sclerosis using multifocal ERG data feature fusion
title_sort Diagnosis of multiple sclerosis using multifocal ERG data feature fusion
dc.creator.none.fl_str_mv López-Dorado, A.
Pérez, J.
Rodrigo, M.J.
Miguel-Jiménez, J.M.
Ortiz, M.
Santiago, L. de
López-Guillén, E.
Blanco, R.
Cavalliere, C.
Sánchez Morla, E.M.
Boquete, L.
Garcia-Martin, E.
author López-Dorado, A.
author_facet López-Dorado, A.
Pérez, J.
Rodrigo, M.J.
Miguel-Jiménez, J.M.
Ortiz, M.
Santiago, L. de
López-Guillén, E.
Blanco, R.
Cavalliere, C.
Sánchez Morla, E.M.
Boquete, L.
Garcia-Martin, E.
author_role author
author2 Pérez, J.
Rodrigo, M.J.
Miguel-Jiménez, J.M.
Ortiz, M.
Santiago, L. de
López-Guillén, E.
Blanco, R.
Cavalliere, C.
Sánchez Morla, E.M.
Boquete, L.
Garcia-Martin, E.
author2_role author
author
author
author
author
author
author
author
author
author
author
description The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI-port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants). For each group, the correlation with a normative database of adaptively filtered signals, based on empirical model decomposition (EMD) and three features from the continuous wavelet transform (CWT) domain, are obtained. Of the initial 40 features, the 4 most relevant are selected in two stages: a) using a filter method and b) using a wrapper-feature selection method. The Support Vector Machine (SVM) is used as a classifier. With the optimal CAD configuration, a Matthews correlation coefficient value of 0.89 (accuracy = 0.95, specificity = 1.0 and sensitivity = 0.93) is obtained. This study identified an outer retina dysfunction in patients with recent MS by analysing the outer retina responses in the mfERG and employing an SVM as a classifier. In conclusion, a promising new electrophysiological-biomarker method based on feature fusion for MS diagnosis was identified.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://zaguan.unizar.es/record/151616
url http://zaguan.unizar.es/record/151616
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726
info:eu-repo/grantAgreement/ES/ISCIII/RETICS-RD16-0008-029
info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/DPI2017-88438-R
dc.rights.none.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
publisher.none.fl_str_mv
dc.source.none.fl_str_mv reponame:Zaguán. Repositorio Digital de la Universidad de Zaragoza
instname:Universidad de Zaragoza
instname_str Universidad de Zaragoza
reponame_str Zaguán. Repositorio Digital de la Universidad de Zaragoza
collection Zaguán. Repositorio Digital de la Universidad de Zaragoza
repository.name.fl_str_mv
repository.mail.fl_str_mv
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