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Continuous-wavelet-transform analysis of the multifocal ERG waveform in glaucoma diagnosis


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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This research has been partially supported by the Ministerio de Ciencia e Innovación (Spain) under the program entitled “Advanced Analysis of Multifocal ERG and Visual-Evoked Potentials Applied to Diagnosis of Optic Neuropathies,” reference number TEC2011–26066, and by FIS PI11/00533 and RETICS RD12/0034/0006 Granted to R. Blanco.

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The authors claim no conflicts of interest.

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Correspondence to J. M. Miguel-Jiménez.

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Cite this article

Miguel-Jiménez, J.M., Blanco, R., De-Santiago, L. et al. Continuous-wavelet-transform analysis of the multifocal ERG waveform in glaucoma diagnosis. Med Biol Eng Comput 53, 771–780 (2015).

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  • Glaucoma
  • Multifocal ERG
  • Continuous wavelet transform
  • Neural network