A Comprehensive Dataset of Pattern Electroretinograms for Ocular Electrophysiology Research: The PERG-IOBA Dataset

  1. Fernández, Itziar 123
  2. Cuadrado Asensio, Ruben 45
  3. Larriba, Yolanda 16
  4. Rueda, Cristina 7
  5. Coco-Martin, Rosa M 8
  1. 1 Department of Statistics and Operations Research, University of Valladolid
  2. 2 Applied Ophthalmology Institute (IOBA), University of Valladolid
  3. 3 Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Carlos III Health Institute, Valladolid
  4. 4 Facultad de Medicina, Universidad de Valladolid
  5. 5 Applied Ophthalmology Institute (IOBA), Universidad de Valladolid
  6. 6 Mathematics Research Institute of the University of Valladolid (IMUVA), University of Valladolid
  7. 7 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

  8. 8 IOBA, Facultad de Medicina, Universidad de Valladolid
Erakutsi afiliazioak +

Argitaratzaile: PhysioNet

Argitalpen urtea: 2024

Mota: Dataset

DOI: 10.13026/D24M-W054 lock_openSarbide irekia editor

Laburpena

The pattern electroretinogram (PERG) is a valuable tool in ophthalmic electrophysiology, offering a non-invasive and objective method to evaluate central retinal function. By measuring electrical activity in the macula and retinal ganglion cells, PERG provides essential quantitative insights, especially when distinguishing between macular and optic nerve diseases remains challenging. This dataset comprises 1354 transient PERG responses from 304 subjects in 336 records, representing a comprehensive collection of ocular electrophysiological data. The evaluations took place at the Institute of Applied Ophthalmobiology (IOBA) at the University of Valladolid, Spain, from 2003 to 2022. Detailed clinical information, including age, gender, and diagnoses, is provided in CSV format. Additionally, the dataset, which guarantees the presence of at least one PERG signal for each eye, incorporates visual acuity measurements in logMar scale, facilitating assessments of vision quality. Addressing a significant gap in ocular electrophysiological signal repositories, this dataset serves as a valuable resource for the advancement of ophthalmology research. Researchers can explore a wide range of eye-related conditions and diseases, leading to improved diagnostic techniques, treatment strategies, and a deeper understanding of ocular electrophysiology.