6533b7defe1ef96bd1275d60
RESEARCH PRODUCT
Empirical mode decomposition and neural network for the classification of electroretinographic data
Dominique Persano AdornoL BellomonteAbdollah BagheriPiervincenzo RizzoR. Barracosubject
EngineeringAchromatopsiaBiomedical EngineeringContext (language use)Settore FIS/03 - Fisica Della MateriaHilbert–Huang transformRetinal DiseasesNight BlindnessElectroretinographyMyopiamedicineHumansComputer visionCongenital stationary night blindnessSignal processingArtificial neural networkbusiness.industryVisual examinationEye Diseases HereditaryGenetic Diseases X-LinkedSignal Processing Computer-AssistedPattern recognitionmedicine.diseaseSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Computer Science Applicationselectroretinogram empirical mode decomposition artificial neural network Achromatopsia Congenital Stationary Night BlindnessClinical diagnosisNeural Networks ComputerArtificial intelligencebusinessdescription
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 behaviour characterized by strong non-linear and non-stationary properties that cannot always be perceived by simple visual examination. New processing methods need be considered. In this context, we propose to apply a signal processing method, based on empirical mode 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 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 allows us to recognize with high level of confidence the eventual presence of one of the two pathologies.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2013-05-10 | Medical & Biological Engineering & Computing |