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Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer. / Kulyabin, Mikhail; Zhdanov, Aleksei; Dolganov, Anton et al.
In: Sensors, Vol. 23, No. 21, 8727, 2023.

Research output: Contribution to journal › Article › peer-review

Harvard

Kulyabin, M, Zhdanov, A, Dolganov, A, Ronkin, M, Borisov, V & Maier, A 2023, 'Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer', Sensors, vol. 23, no. 21, 8727. https://doi.org/10.3390/s23218727

APA

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

Vancouver

Kulyabin M, Zhdanov A, Dolganov A, Ronkin M, Borisov V, Maier A. Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer. Sensors. 2023;23(21):8727. doi: 10.3390/s23218727

Author

Kulyabin, Mikhail ; Zhdanov, Aleksei ; Dolganov, Anton et al. / Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer. In: Sensors. 2023 ; Vol. 23, No. 21.

BibTeX

@article{1c070fdd8b7b45e4bde7453d64dcebf7,
title = "Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer",
abstract = "The electroretinogram (ERG) is a clinical test that records the retina{\textquoteright}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.",
author = "Mikhail Kulyabin and Aleksei Zhdanov and Anton Dolganov and Mikhail Ronkin and Vasilii Borisov and Andreas Maier",
year = "2023",
doi = "10.3390/s23218727",
language = "English",
volume = "23",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "21",

}

RIS

TY - JOUR

T1 - Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer

AU - Kulyabin, Mikhail

AU - Zhdanov, Aleksei

AU - Dolganov, Anton

AU - Ronkin, Mikhail

AU - Borisov, Vasilii

AU - Maier, Andreas

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85176902341

UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=001100427400001

U2 - 10.3390/s23218727

DO - 10.3390/s23218727

M3 - Article

VL - 23

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 21

M1 - 8727

ER -

ID: 48542935