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

Authors:
L. Boquete;J. M. Miguel-Jiménez;S. Ortega;J. M. Rodríguez-Ascariz;C. Pérez-Rico;R. Blanco
Affiliations:
Department of Electronics, Biomedical Engineering Group, University of Alcalá, Spain;Department of Electronics, Biomedical Engineering Group, University of Alcalá, Spain;Department of Electronics, Biomedical Engineering Group, University of Alcalá, Spain;Department of Electronics, Biomedical Engineering Group, University of Alcalá, Spain;Department of Surgery, University of Alcalá, Madrid, Spain;Department of Surgery, University of Alcalá, Madrid, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 2
Cited 1

The fast m-transform: a fast computation of cross-correlations with binary m-sequences

SIAM Journal on Computing
A fast recognition framework based on extreme learning machine using hybrid object information

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mfERG_LAB: Software for processing multifocal electroretinography signals

Computer Methods and Programs in Biomedicine

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Abstract

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.