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Diagnosis of multiple sclerosis using multifocal ERG data feature fusion

2021; Elsevier BV; Volume: 76; Linguagem: Inglês

10.1016/j.inffus.2021.05.006

1872-6305

Almudena López-Dorado, J.L. Perez, María Jesús Rodrigo, Juan Manuel Miguel, Miguel Ortiz del Castillo, Luis de Santiago, Elena López, Ricardo Blanco, C. Cavalliere, E. Ma Sánchez Morla, Luciano Boquete, Elena García‐Martín,

Photoacoustic and Ultrasonic Imaging

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.