Diagnosis of multiple sclerosis using multifocal erg data feature fusion

A. López-Dorado, J. Pérez, M.J. Rodrigo, J.M. Miguel-Jiménez, M. Ortiz, L. de Santiago, E. López-Guillén, R. Blanco, C. Cavalliere, E. Mª Sánchez Morla, L. Boquete, E. Garcia-Martin
2021 Information 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 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
more » ... 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.
doi:10.1016/j.inffus.2021.05.006 pmid:34867127 pmcid:PMC8475498 fatcat:o2hbkirkifgwtkh2yddspvyoku