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Medical & Biological Engineering & Computing
Published in: Medical & Biological Engineering & Computing 7/2014

01-07-2014 | Original Article

Empirical mode decomposition and neural network for the classification of electroretinographic data

Authors: Abdollah Bagheri, Dominique Persano Adorno, Piervincenzo Rizzo, Rosita Barraco, Leonardo Bellomonte

Published in: Medical & Biological Engineering & Computing | Issue 7/2014

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Abstract

The processing of biosignals is increasingly being utilized in ambulatory situations in order to extract significant signals’ features that can help in clinical diagnosis. However, this task is hampered by the fact that biomedical signals exhibit a complex behavior characterized by strong nonlinear and non-stationary properties that cannot always be perceived by simple visual examination. New processing methods need be considered. In this context, we propose a signal processing method, based on empirical mode decomposition and artificial neural networks, to analyze electroretinograms, i.e., the retinal response to a light flash, with the aim to detect and classify retinal diseases. The present application focuses on two retinal pathologies: achromatopsia, which is a cone disease, and congenital stationary night blindness, which affects the photoreceptoral signal transmission. The results indicate that, under suitable conditions, the method proposed here has the potential to provide a powerful tool for routine clinical examinations, since it is able to recognize with high level of confidence the eventual presence of one of the two pathologies.
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Metadata
Title
Empirical mode decomposition and neural network for the classification of electroretinographic data
Authors
Abdollah Bagheri
Dominique Persano Adorno
Piervincenzo Rizzo
Rosita Barraco
Leonardo Bellomonte
Publication date
01-07-2014
Publisher
Springer Berlin Heidelberg
Published in
Medical & Biological Engineering & Computing / Issue 7/2014
Print ISSN: 0140-0118
Electronic ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-014-1164-8

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