Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Advanced Analysis of Electroretinograms Based on Wavelet Scalogram Processing

Version 1 : Received: 7 October 2022 / Approved: 10 October 2022 / Online: 10 October 2022 (11:11:37 CEST)

How to cite: Zhdanov, A.; Dolganov, A.; Zanca, D.; Borisov, V.; Ronkin, M. Advanced Analysis of Electroretinograms Based on Wavelet Scalogram Processing. Preprints 2022, 2022100123. https://doi.org/10.20944/preprints202210.0123.v1 Zhdanov, A.; Dolganov, A.; Zanca, D.; Borisov, V.; Ronkin, M. Advanced Analysis of Electroretinograms Based on Wavelet Scalogram Processing. Preprints 2022, 2022100123. https://doi.org/10.20944/preprints202210.0123.v1

Abstract

The electroretinography (ERG) is a diagnostic test that measures the electrical activity of the retina in response to a light stimulus. This test has a long history and has been extensively studied among years. Dr. James Dewar recorded the first electroretinogram signal in 1873. Later he described signal analysis using 4 components, namely amplitude, and latency of a-wave and b-wave. Nowadays, the international electrophysiology community has established the standard for electroretinography in 2008. However, from the point of view of signal analysis, the major change did not happen. ERG analysis is still based on the 4 components evaluation. The article describes ERG database including the classification of signals by using advanced analysis of electroretinograms based on wavelet scalogram processing. To implement an extended analysis of the ERG, the parameters extracted from the wavelet scalogram of the signal were obtained using digital image processing and machine learning methods. The results of the study show that the proposed algorithm implements the classification of adult electroretinogram signals by 19% more accurately and pediatric signals by 20% more accurately than the classical algorithm. The promising use of ERG is differential diagnostics, which may also be used in preclinical toxicology and experimental modeling. The problem of developing methods for electrophysiological signals analysis in ophthalmology is associated with the complex morphological structure of electrophysiological signals components, due to the generation of retina cell electrical responses to light stimulus.

Keywords

biomedical research; classification; machine learning; electroretinography; electroretinogram; ERG

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Comments (1)

Comment 1
Received: 20 October 2022
The commenter has declared there is no conflict of interests.
Comment: WHAT DETERMINES THE USAGE OF SOUND BARRIERS ITHIN COMPLEX MORPHOLOGICAL STUDIES OF ELECTROPHYSIOLOGICAL,SIGNAL'S COMPONENTS. I PLACED QUESTION AN FORM TOO STATEMENT OF ERG WHICH COMMONLY USED WITHIN A HOSPITAL SETTING. THOUGHT OCCURED ACTIVITY OF RETINA TOO RESPONSE TOO LIGHT STIMULUS. REGISTERING THE PROCESS THOUGHT OF HOSPITAL SETTING WITH REGARDS TOO BEING WITHIN A BUILDING THAT IS WITHIN THE LAW.
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