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Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer
The electroretinogram (ERG) is a clinical test that records the retina’s electrical response to light. Analysis of the ERG signal offers a promising way to study different retinal diseases and disorders. Machine learning-based methods are expected to play a pivotal role in achieving the goals of ret...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648817/ https://www.ncbi.nlm.nih.gov/pubmed/37960427 http://dx.doi.org/10.3390/s23218727 |
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author | Kulyabin, Mikhail Zhdanov, Aleksei Dolganov, Anton Ronkin, Mikhail Borisov, Vasilii Maier, Andreas |
author_facet | Kulyabin, Mikhail Zhdanov, Aleksei Dolganov, Anton Ronkin, Mikhail Borisov, Vasilii Maier, Andreas |
author_sort | Kulyabin, Mikhail |
collection | PubMed |
description | The electroretinogram (ERG) is a clinical test that records the retina’s electrical response to light. Analysis of the ERG signal offers a promising way to study different retinal diseases and disorders. Machine learning-based methods are expected to play a pivotal role in achieving the goals of retinal diagnostics and treatment control. This study aims to improve the classification accuracy of the previous work using the combination of three optimal mother wavelet functions. We apply Continuous Wavelet Transform (CWT) on a dataset of mixed pediatric and adult ERG signals and show the possibility of simultaneous analysis of the signals. The modern Visual Transformer-based architectures are tested on a time-frequency representation of the signals. The method provides 88% classification accuracy for Maximum 2.0 ERG, 85% for Scotopic 2.0, and 91% for Photopic 2.0 protocols, which on average improves the result by 7.6% compared to previous work. |
format | Online Article Text |
id | pubmed-10648817 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106488172023-10-26 Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer Kulyabin, Mikhail Zhdanov, Aleksei Dolganov, Anton Ronkin, Mikhail Borisov, Vasilii Maier, Andreas Sensors (Basel) Article The electroretinogram (ERG) is a clinical test that records the retina’s electrical response to light. Analysis of the ERG signal offers a promising way to study different retinal diseases and disorders. Machine learning-based methods are expected to play a pivotal role in achieving the goals of retinal diagnostics and treatment control. This study aims to improve the classification accuracy of the previous work using the combination of three optimal mother wavelet functions. We apply Continuous Wavelet Transform (CWT) on a dataset of mixed pediatric and adult ERG signals and show the possibility of simultaneous analysis of the signals. The modern Visual Transformer-based architectures are tested on a time-frequency representation of the signals. The method provides 88% classification accuracy for Maximum 2.0 ERG, 85% for Scotopic 2.0, and 91% for Photopic 2.0 protocols, which on average improves the result by 7.6% compared to previous work. MDPI 2023-10-26 /pmc/articles/PMC10648817/ /pubmed/37960427 http://dx.doi.org/10.3390/s23218727 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kulyabin, Mikhail Zhdanov, Aleksei Dolganov, Anton Ronkin, Mikhail Borisov, Vasilii Maier, Andreas Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer |
title | Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer |
title_full | Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer |
title_fullStr | Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer |
title_full_unstemmed | Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer |
title_short | Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer |
title_sort | enhancing electroretinogram classification with multi-wavelet analysis and visual transformer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648817/ https://www.ncbi.nlm.nih.gov/pubmed/37960427 http://dx.doi.org/10.3390/s23218727 |
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