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

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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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Acknowledgments

The signal processing utilized in this study was developed as part of a U.S. National Science Foundation grant, award number CMMI 1029457.

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Authors and Affiliations

  1. Laboratory for Nondestructive Evaluation and Structural Health Monitoring Studies, Department of Civil and Environmental Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, PA, 15261, USA

    Abdollah Bagheri & Piervincenzo Rizzo

  2. Department of Physics and Chemistry and CNISM, University of Palermo, Viale delle Scienze, Ed.18, 90128, Palermo, Italy

    Dominique Persano Adorno, Rosita Barraco & Leonardo Bellomonte

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  1. Abdollah Bagheri
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  2. Dominique Persano Adorno
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Correspondence to Dominique Persano Adorno.

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Bagheri, A., Persano Adorno, D., Rizzo, P. et al. Empirical mode decomposition and neural network for the classification of electroretinographic data. Med Biol Eng Comput 52, 619–628 (2014). https://doi.org/10.1007/s11517-014-1164-8

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  • DOI: https://doi.org/10.1007/s11517-014-1164-8

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