Advanced Analysis of Electroretinograms Based on Wavelet Scalogram Processing
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Advanced Analysis of Electroretinograms Based on Wavelet Scalogram Processing
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| Автор |
Zhdanov, A.
Dolganov, A. Zanca, D. Borisov, V. Ronkin, M. |
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| Тематика |
BIOMEDICAL RESEARCH
CLASSIFICATION ELECTRORETINOGRAM ELECTRORETINOGRAPHY ERG MACHINE LEARNING |
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| Описание |
Featured Application: The results of this work will be used to develop a system to assist ophthalmologist doctor decisions. The electroretinography (ERG) is a diagnostic test that measures the electrical activity of the retina in response to a light stimulus. The current ERG signal analysis uses four components, namely amplitude, and the latency of a-wave and b-wave. Nowadays, the international electrophysiology community established the standard for electroretinography in 2008. However, in terms of signal analysis, there were no major changes. ERG analysis is still based on a four-component evaluation. The article describes the ERG database, including the classification of signals via the 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. Specifically, the study focused on the preprocessing of wavelet scalogram as images, and the extraction of connected components and thier evaluation. As a machine learning method, a decision tree was selected as one that incorporated feature selection. The study results show that the proposed algorithm more accurately implements the classification of adult electroretinogram signals by 19%, and pediatric signals by 20%, in comparison with the classical features of ERG. The promising use of ERG is presented using 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 structures of electrophysiological signal components. © 2022 by the authors.
Ural Federal University Program of Development Ministry of Education and Science of the Russian Federation, Minobrnauka The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority—2030 Program) is gratefully acknowledged. |
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| Дата |
2024-04-08T11:05:48Z
2024-04-08T11:05:48Z 2022 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) Published version (info:eu-repo/semantics/publishedVersion) |
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| Идентификатор |
Zhdanov, A, Dolganov, A, Zanca, D, Borisov, V & Ronkin, M 2022, 'Advanced Analysis of Electroretinograms Based on Wavelet Scalogram Processing', Applied Sciences (Switzerland), Том. 12, № 23, 12365. https://doi.org/10.3390/app122312365
Zhdanov, A., Dolganov, A., Zanca, D., Borisov, V., & Ronkin, M. (2022). Advanced Analysis of Electroretinograms Based on Wavelet Scalogram Processing. Applied Sciences (Switzerland), 12(23), [12365]. https://doi.org/10.3390/app122312365 2076-3417 Final All Open Access; Gold Open Access https://www.mdpi.com/2076-3417/12/23/12365/pdf?version=1670320543 https://www.mdpi.com/2076-3417/12/23/12365/pdf?version=1670320543 http://elar.urfu.ru/handle/10995/131213 10.3390/app122312365 85143686648 000895078900001 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
MDPI
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| Источник |
Applied Sciences
Applied Sciences (Switzerland) |
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