Empirical mode decomposition and neural network for the classification of electroretinographic data.
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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 52; no. 7; pp. 619 - 629 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jul2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103829325&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103829325 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2014 vid: 52 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103829325 NLM24923413 2012619467 10.1007/s11517-014-1164-8 NLM24923413 103829325 ppf: 619 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Empirical mode decomposition and neural network for the classification of electroretinographic data. aug: au: Bagheri, Abdollah Persano Adorno, Dominique Rizzo, Piervincenzo Barraco, Rosita Bellomonte, Leonardo affil: 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. sug: subj: Electroretinography Methods Neural Networks (Computer) Signal Processing, Computer Assisted Eye Diseases, Hereditary Physiopathology Genetic Diseases, X-Linked Physiopathology Myopia Physiopathology Vision Disorders Physiopathology Retinal Diseases Physiopathology ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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