000151616 001__ 151616 000151616 005__ 20251017144615.0 000151616 0247_ $$2doi$$a10.1016/j.inffus.2021.05.006 000151616 0248_ $$2sideral$$a126131 000151616 037__ $$aART-2021-126131 000151616 041__ $$aeng 000151616 100__ $$aLópez-Dorado, A. 000151616 245__ $$aDiagnosis of multiple sclerosis using multifocal ERG data feature fusion 000151616 260__ $$c2021 000151616 5060_ $$aAccess copy available to the general public$$fUnrestricted 000151616 5203_ $$aThe 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. 000151616 536__ $$9info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726$$9info:eu-repo/grantAgreement/ES/ISCIII/RETICS-RD16-0008-029$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/DPI2017-88438-R 000151616 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es 000151616 590__ $$a17.564$$b2021 000151616 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b1 / 110 = 0.009$$c2021$$dQ1$$eT1 000151616 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b4 / 145 = 0.028$$c2021$$dQ1$$eT1 000151616 592__ $$a4.557$$b2021 000151616 593__ $$aHardware and Architecture$$c2021$$dQ1 000151616 593__ $$aSoftware$$c2021$$dQ1 000151616 593__ $$aInformation Systems$$c2021$$dQ1 000151616 594__ $$a28.4$$b2021 000151616 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000151616 700__ $$aPérez, J. 000151616 700__ $$aRodrigo, M.J.$$uUniversidad de Zaragoza 000151616 700__ $$aMiguel-Jiménez, J.M. 000151616 700__ $$aOrtiz, M. 000151616 700__ $$aSantiago, L. de 000151616 700__ $$aLópez-Guillén, E. 000151616 700__ $$aBlanco, R. 000151616 700__ $$aCavalliere, C. 000151616 700__ $$aSánchez Morla, E.M. 000151616 700__ $$aBoquete, L. 000151616 700__ $$0(orcid)0000-0001-6258-2489$$aGarcia-Martin, E.$$uUniversidad de Zaragoza 000151616 7102_ $$11013$$2646$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Oftalmología 000151616 773__ $$g76 (2021), 157-167$$pInformation Fusion$$tInformation Fusion$$x1566-2535 000151616 8564_ $$s3704611$$uhttps://zaguan.unizar.es/record/151616/files/texto_completo.pdf$$yVersión publicada 000151616 8564_ $$s2454754$$uhttps://zaguan.unizar.es/record/151616/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000151616 909CO $$ooai:zaguan.unizar.es:151616$$particulos$$pdriver 000151616 951__ $$a2025-10-17-14:18:53 000151616 980__ $$aARTICLE