Continuous‑wavelet‑transform analysis of the multifocal ERG waveform in glaucoma diagnosis
Autores
Miguel Jiménez, Juan Manuel; Blanco, R.; Santiago Rodrigo, Luis de; Fernández Rodríguez, Alfredo José; Rodríguez Ascariz, José Manuel; [et al.]Identificadores
Enlace permanente (URI): http://hdl.handle.net/10017/49179DOI: 10.1007/s11517-015-1287-6
Editor
Springer
Fecha de publicación
2015-04-08Patrocinadores
Ministerio de Ciencia e Innovación
Instituto de Salud Carlos III
Cita bibliográfica
Miguel Jiménez, J.M. et al. 2015, "Continuous‑wavelet‑transform analysis of the multifocal ERG waveform in glaucoma diagnosis", Medical & Biological Engineering & Computing, vol. 53, no. 9, pp. 771-780.
Palabras clave
Glaucoma
Multifocal ERG
Continuous wavelet transform
Neural network
Proyectos
info:eu-repo/grantAgreement/MICINN//TEC2011-26066/ES/ANALISIS AVANZADO DE LAS SEÑALES DE LOS POTENCIALES EVOCADOS MULTIFOCALES Y DE LA ELECTRORRETINOGRAFIA MULTIFOCAL APLICADOS AL DIAGNOSTICO DE LAS NEUROPATIAS OPTICAS/
info:eu-repo/grantAgreement/MICINN//FIS PI11/00533
info:eu-repo/grantAgreement/ISCIII//RD12%2F0034%2F0006
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Versión del editor
https://doi.org/10.1007/s11517-015-1287-6Derechos
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
The vast majority of multifocal electroretinogram (mfERG) signal analyses to detect glaucoma study the signals’ amplitudes and latencies. The purpose of this paper is to investigate application of wavelet analysis of mfERG signals in diagnosis of glaucoma. This analysis method applies the continuous wavelet transform (CWT) to the signals, using the real Morlet wavelet. CWT coefficients resulting from the scale of maximum correlation are used as inputs to a neural network, which acts as a classifier. mfERG recordings are taken from the eyes of 47 subjects diagnosed with chronic open-angle glaucoma and from those of 24 healthy subjects. The high sensitivity in the classification (0.894) provides reliable detection of glaucomatous sectors, while the specificity achieved (0.844) reflects accurate detection of healthy sectors. The results obtained in this paper improve on the previous findings reported by the authors using the same visual stimuli and database.
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