ArticleScientific reports2025
Improving the reliability of the pattern electroretinogram with adaptive filtering.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Use of Artificial Intelligence in the Interpretation of Electroretinography (ERG) Studies.International journal of molecular sciences · 2026Pooled it
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electrophysiological testing of the eye and visual system is important in the diagnosis of some diseases. Unlike the electrocardiogram and other more common tests, the amplitudes of the signals are very small and subject to noise and interference. Innovative signal processing algorithms have been shown to be helpful for the electroretinogram (ERG). In this paper, we extend that work to the pattern ERG (PERG), which has utility in the assessment, diagnosis and management of central retinal and optic nerve disease. The use of the PERG has been limited by the small amplitude signal and low signal-to-noise ratio. The aim of this study was to use adaptive denoising techniques to improve the reliability and utility of the PERG. PERGs were performed on 70 eyes of 36 patients following ISCEV protocols. After a short break of approximately 2-5 min, a second recording was obtained to assess test-retest reliability of denoising. Denoising was performed on both recordings using five different adaptive filtering algorithms. Denoising algorithms generally improved test-retest reliability measured with intraclass correlation coefficient (ICC) and coefficient of repeatability (CoR). Empirical Wavelet Transform provided the best overall improvement in reliability, improving ICC from 0.79 to 0.84 (p = 0.049) and CoR from 2.12 µV to 1.69 µV (p = 0.02). Adaptive denoising algorithms may improve the reliability of PERG.
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