Empirical Mode Decomposition and Neural Network for the Classification of Electroretinographic Data

Dominique Persano Adorno, Rosita Maria Luisa Barraco, Abdollah Bagheri, Piervincenzo Rizzo, Leonardo Bellomonte

Research output: Contribution to journalArticle

11 Citations (Scopus)


The processing of biosignals is increasingly being utilized in ambulatory situations inorder 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 behaviourcharacterized by strong non-linear and non-stationary properties that cannot always beperceived by simple visual examination. New processing methods need be considered.In this context, we propose to apply a signal processing method, based on empiricalmode decomposition and artificial neural networks, to analyse 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 acone disease, and Congenital Stationary Night Blindness, which affects thephotoreceptoral signal transmission. The results indicate that, under suitableconditions, the method proposed here has the potential to provide a powerful tool forroutine clinical examinations, since it allows us to recognize with high level ofconfidence the eventual presence of one of the two pathologies.
Original languageEnglish
Pages (from-to)619-628
Number of pages10
Publication statusPublished - 2014

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Computer Science Applications

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