Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer
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| Автор |
Kulyabin, M.
Zhdanov, A. Dolganov, A. Ronkin, M. Borisov, V. Maier, A. |
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| Тематика |
BIOMEDICAL RESEARCH
CLASSIFICATION DEEP LEARNING ELECTRORETINOGRAM ELECTRORETINOGRAPHY ERG WAVELET ANALYSIS ADULT CHILD COLOR VISION ELECTRORETINOGRAPHY HUMAN MACHINE LEARNING PHYSIOLOGY PROCEDURES RETINA WAVELET ANALYSIS ADULT CHILD COLOR VISION ELECTRORETINOGRAPHY HUMANS MACHINE LEARNING RETINA WAVELET ANALYSIS |
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| Описание |
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.
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| Дата |
2024-04-05T16:36:37Z
2024-04-05T16:36:37Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Kulyabin, M, Zhdanov, A, Dolganov, A, Ronkin, M, Borisov, V & Maier, A 2023, 'Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer', Sensors, Том. 23, № 21, 8727. https://doi.org/10.3390/s23218727
Kulyabin, M., Zhdanov, A., Dolganov, A., Ronkin, M., Borisov, V., & Maier, A. (2023). Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer. Sensors, 23(21), [8727]. https://doi.org/10.3390/s23218727 1424-8220 Final All Open Access, Gold, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176902341&doi=10.3390%2fs23218727&partnerID=40&md5=a7422368b7887f59532c6920557cb1d8 https://www.mdpi.com/1424-8220/23/21/8727/pdf?version=1698301695 http://elar.urfu.ru/handle/10995/130974 10.3390/s23218727 85176902341 001100427400001 |
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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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| Источник |
Sensors
Sensors (Basel, Switzerland) |
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