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. 2024 Sep 18;11(1):1013.
doi: 10.1038/s41597-024-03857-1.

A comprehensive dataset of pattern electroretinograms for ocular electrophysiology research

Itziar Fernández  1   2   3 , Rubén Cuadrado-Asensio  4   5 , Yolanda Larriba  6   7 , Cristina Rueda  8   9 , Rosa M Coco-Martín  4   5
Affiliations

A comprehensive dataset of pattern electroretinograms for ocular electrophysiology research

Itziar Fernández et al. Sci Data. .

Abstract

The Pattern Electroretinogram (PERG) is an essential tool in ophthalmic electrophysiology, providing an objective assessment of the central retinal function. It quantifies the activity of cells in the macula and the ganglion cells of the retina, assisting in the differentiation of macular and optic nerve conditions. In this study, we present the IOBA-PERG dataset, an extensive collection of 1354 transient PERG responses accessible on the PhysioNet repository. These recordings were conducted at the Institute of Applied Ophthalmobiology (IOBA) at University of Valladolid, over an extended period spanning nearly two decades, from 2003 to 2022. The dataset includes 336 records, ensuring at least one PERG signal per eye. The dataset thoughtfully includes demographic and clinical data, comprising information such as age, gender, visual acuity measurements, and expert diagnoses. This comprehensive dataset fills a gap in ocular electrophysiological repositories, enhancing ophthalmology research. Researchers can explore a broad range of eye-related conditions and diseases, leading to enhanced diagnostic accuracy, innovative treatment strategies, methodological advancements, and a deeper understanding of ocular electrophysiology.

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Conflict of interest statement

The authors declare no competing interests. The funder had no role in the design of the study; in the collection of the data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Fig. 1
Fig. 1
Typical PERG waveforms. (a) Transient PERG, generated at temporal rates less than 6 pattern reversals per second (rps). (b) Steady-state PERG, generated at temporal rates higher than 10 rps.
Fig. 2
Fig. 2
Distribution of PERG-IOBA diagnostics. The color scheme differentiates between diagnostic classes, including Normal, Retinal Affectation, Neuro-Ophthalmic Disorder, and Other Class.
Fig. 3
Fig. 3
Distribution of PERG-IOBA records within diagnostic categories and subcategories: (a) Diagnostic classes, (b) Subclasses of retinal impairments, and (c) Subclasses of neuro-ophthalmic disorders.
Fig. 4
Fig. 4
Distribution of the average visual acuity between eyes within diagnostic subcategories.
Fig. 5
Fig. 5
Decomposition of PERG data into signal and noise components from three illustrative recordings exhibiting different levels of background noise. Points: observed data, black line: smoothed signal, orange line: noise component.
Fig. 6
Fig. 6
Distribution of the 1 − R2 across diagnostic subcategories.
See this image and copyright information in PMC

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