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Electroretinography (ERG) has great potential in visual health detection in early diagnosis and intervention. To date, optical coherence tomography and other diagnostic tests are mainly used. Clinically used ERG is an important diagnostic assessment for various retinal diseases, such as hereditary diseases (retinitis pigmentosa, choroideremia, cone dystrophy, etc.), diabetic retinopathies, glaucoma, macular degeneration, toxic retinopathies etc. A database of five types of adult and pediatric biomedical electroretinography signals is presented in this study. In this study, the database is focused on the Maximum 2.0 ERG Response (80 pediatric signals, 42 adult signals) protocol. The tests were performed at the IRTC Eye Microsurgery Ekaterinburg Center. In this study, the EP-1000 Multifocal computerized electrophysiology workstation manufactured by Tomey GmbH was used. The aim of this study is to describe the existing database including the classification of signals and to determine the pediatric norms of the binocular program.

ERG is a biopotential response of various retinal cells formed by visual stimulation with a bright flashlight. The retina produces an appropriate response which is depending on the visual stimulation conditions. To ensure uniform flash intensity throughout the patient's field of view, the patient's chin is placed on a special ganzfeld platform. This installation realizes the uniformity of light stimulation distribution. During the examination, the patient looks at one point indicated in the ganzfeld. Flash intensity replacements are routinely regulated by the ISCEV standard, however, this study used standard EP-1000 (Tomey GmbH) protocols. Filtering is a key part of signal analysis. In accordance with the requirements of the standard, the filtration of (ERG) signals should be implemented using a posteriori methods. The source of this recommendation is the complex waveform and the presence of frequency components of different private ranges. Thus, this study described the selection of the optimal filtering method for the Maximum 2.0 ERG Response signal based on the posteriori method. The study used the original biomedical signals of adult patients previously published on the IEEE DataPort.

Electroretinogram (ERG) is the non-invasive ocular test to assess the retinal function by measuring the photic irrational induced electrical responses of the cells in the retina, including the photoreceptors in the outer retina, inner retinal layers, and the final output neurons. Clinically used ERG is an important diagnostic assessment for various retinal diseases, such as hereditary diseases, diabetic retinopathies, glaucoma, macular degeneration, toxic retinopathies, etc. The presented ERG examination includes a series of electrophysiological tests of various types of electroretinography. The tests were performed at the IRTC Eye Microsurgery Ekaterinburg Center. In this study, the EP-1000 Multifocal computerized electrophysiology workstation manufactured by Tomey GmbH was used. This station is located in a separate isolated room and complies with ISCEV standards. Manual analysis of medical ophthalmic signals is a time-consuming process that involves operator training and skill. Analysis of ophthalmic signal data could advance much faster and be more cost-effective while reducing time and material costs through automation and optimization using signal processing and analysis methods available in the modern mathematical theory of signal processing. This paper described a Maximum 2.0 ERG Response time-frequency analysis.

ERG is a biopotential response of various retinal cells formed by visual stimulation with a bright flashlight. The retina produces an appropriate response which is depending on the visual stimulation conditions. To ensure uniform flash intensity throughout the patient's field of view, the patient's chin is placed on a special ganzfeld platform. This installation realizes the uniformity of light stimulation distribution. During the examination, the patient looks at one point indicated in the ganzfeld. Flash intensity replacements are routinely regulated by the ISCEV standard, however, this study used standard EP-1000 (Tomey GmbH) protocols. Filtering is a key part of signal analysis. In accordance with the requirements of the standard, the filtration of (ERG) signals should be implemented using posteriori methods. The source of this recommendation is the complex waveform and the presence of frequency components of different private ranges. Thus, this study described the selection of the optimal filtering method for the Maximum 2.0 ERG Response signal based on the posteriori method. The study used the original biomedical signals of adult patients previously published on the IEEE DataPort.

To date, electrophysiological research (EPR) remains an important source of objective assessment of the visual analyzer functional state. The ability to exclude the EPR subject subjectivity is no doubt an attractive advantage of EPR. In particular, an essential advantage is the functional topographic assessment of the malfunction of the visual analyzer systems. Localized electrophysiology of the vision organ is submitted by a variety of methods for recording the biomedical signals. High complexity and many confounder factors during signal receiving may be complicated by the final result analyzing given by ranging from stimulation parameters to the state of the patient himself. Differential diagnosis, preclinical toxicology, and scientific and experimental models are the main emphasis of EPR. Potential prospects of EPR provide new opportunities for effective implementation of multifocal electroretinography. Classical EPR does not provide detailed and quantitative information about the state of the visual analyzer. This implies the need to optimize and algorithmize assessment data of ophthalmic pathology, even more than before, environmental concerns differentiated criteria inherent into a particular ocular pathology. This article presents literature review of electrophysiological methods in ophthalmology and the processing of electroretinograms using wavelet transform. To efficient processing of electroretinograms, it is necessary to separate the high-frequency and low-frequency components from the experimental signal. The low-frequency component is an oscillatory response or oscillatory potentials generated by the cells of the inner layers of the retina. This article notes the need to vary the basis functions of the wavelet transform and the methods of time-frequency analysis based on the results of electroretinograms processing.