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http://elar.urfu.ru/handle/10995/130974
Название: | Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer |
Авторы: | Kulyabin, M. Zhdanov, A. Dolganov, A. Ronkin, M. Borisov, V. Maier, A. |
Дата публикации: | 2023 |
Библиографическое описание: | 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 |
Аннотация: | 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. |
Ключевые слова: | 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 |
URI: | http://elar.urfu.ru/handle/10995/130974 |
Условия доступа: | info:eu-repo/semantics/openAccess cc-by |
Текст лицензии: | https://creativecommons.org/licenses/by/4.0/ |
Идентификатор SCOPUS: | 85176902341 |
Идентификатор WOS: | 001100427400001 |
Идентификатор PURE: | 48542935 |
ISSN: | 1424-8220 |
DOI: | 10.3390/s23218727 |
Располагается в коллекциях: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
Файлы этого ресурса:
Файл | Описание | Размер | Формат | |
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2-s2.0-85176902341.pdf | 3,76 MB | Adobe PDF | Просмотреть/Открыть |
Лицензия на ресурс: Лицензия Creative Commons