Diagnosis of multiple sclerosis using multifocal ERG data feature fusion

López-Dorado, A. ; Pérez, J. ; Rodrigo, M.J. (Universidad de Zaragoza) ; 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. (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1016/j.inffus.2021.05.006

Año: 2021

Publicado en: Information Fusion 76 (2021), 157-167
ISSN: 1566-2535

Factor impacto JCR: 17.564 (2021)

Categ. JCR: COMPUTER SCIENCE, THEORY & METHODS rank: 1 / 110 = 0.009 (2021) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 4 / 145 = 0.028 (2021) - Q1 - T1

Factor impacto CITESCORE: 28.4 - Computer Science (Q1)

Factor impacto SCIMAGO: 4.557 - Hardware and Architecture (Q1) - Software (Q1) - Information Systems (Q1)

Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/RETICS-RD16-0008-029
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/DPI2017-88438-R

Tipo y forma: Article (Published version)

Área (Departamento): Área Oftalmología (Dpto. Cirugía)