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 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.

Diagnostic ability of multifocal electroretinogram in early multiple sclerosis using a new signal analysis method.

PURPOSE: To determine if a novel analysis method will increase the diagnostic value of the multifocal electroretinogram (mfERG) in diagnosing early-stage multiple sclerosis (MS). METHODS: We studied the mfERG signals of OD (Oculus Dexter) eyes of fifteen patients diagnosed with early-stage MS (in all cases < 12 months) and without a history of optic neuritis (ON) (F:M = 11:4), and those of six controls (F:M = 3:3). We obtained values of amplitude and latency of N1 and P1 waves, and a method to assess normalized root-mean-square error (FNRMSE) between model signals and mfERG recordings was used. Responses of each eye were analysed at a global level, and by rings, quadrants and hemispheres. AUC (area under the ROC curve) is used as discriminant factor. RESULTS: The standard method of analysis obtains further discrimination between controls and MS in ring R3 (AUC = 0.82), analysing N1 waves amplitudes. In all of the retina analysis regions, FNRMSE value shows a greater discriminating power than the standard method. The highest AUC value (AUC = 0.91) was in the superior temporal quadrant. CONCLUSION: By analysing mfERG recordings and contrasting them with those of healthy controls it is possible to detect early-stage MS in patients without a previous history of ON.