Multifocal electroretinogram diagnosis of glaucoma applying neural networks and structural pattern analysis

Glaucoma is a chronic ophthalmological disease that affects 5% of the 40–60-year-old population and can lead to irreversible blindness. The multifocal electroretinogram (mfERG) is a recently developed diagnostic technique that provides objective spatial data on the visual pathway and may be of potential benefit in early diagnosis of glaucoma. This paper analyses 13 morphological characteristics that define mfERG recordings and classifies them using a radial basis function network trained with the Extreme Learning Machine algorithm. When used to detect glaucomatous sectors, the method proposed produces sensitivity and specificity values of over 0.8.

► An analysis of mfERG morphological characteristics and classification with a RBF network trained with the ELM algorithm. ► The mfERG waveform morphology characteristics varies according to their spatial location. ► The method proposed may be able to detect early glaucomatous defects more efficiently than the standard HVF campimetry.