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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8727 |
| _version_ | 1797631359487836160 |
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| author | Mikhail Kulyabin Aleksei Zhdanov Anton Dolganov Mikhail Ronkin Vasilii Borisov Andreas Maier |
| author_facet | Mikhail Kulyabin Aleksei Zhdanov Anton Dolganov Mikhail Ronkin Vasilii Borisov Andreas Maier |
| author_sort | Mikhail Kulyabin |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T11:21:43Z |
| format | Article |
| id | doaj.art-c90c4220bfdd44cbb351516abfb76374 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:43Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c90c4220bfdd44cbb351516abfb763742023-11-10T15:11:50ZengMDPI AGSensors1424-82202023-10-012321872710.3390/s23218727Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual TransformerMikhail Kulyabin0Aleksei Zhdanov1Anton Dolganov2Mikhail Ronkin3Vasilii Borisov4Andreas Maier5Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, GermanyEngineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, RussiaEngineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, RussiaEngineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, RussiaEngineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, RussiaPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, GermanyThe 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.https://www.mdpi.com/1424-8220/23/21/8727biomedical researchclassificationdeep learningwavelet analysiselectroretinographyelectroretinogram |
| spellingShingle | Mikhail Kulyabin Aleksei Zhdanov Anton Dolganov Mikhail Ronkin Vasilii Borisov Andreas Maier Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer Sensors biomedical research classification deep learning wavelet analysis electroretinography electroretinogram |
| 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 | biomedical research classification deep learning wavelet analysis electroretinography electroretinogram |
| url | https://www.mdpi.com/1424-8220/23/21/8727 |
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