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- Title
Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer.
- Authors
Kulyabin, Mikhail; Zhdanov, Aleksei; Dolganov, Anton; Ronkin, Mikhail; Borisov, Vasilii; Maier, Andreas
- Abstract
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.
- Subjects
TRANSFORMER models; ELECTRORETINOGRAPHY; RETINAL diseases; WAVELET transforms; WAVELETS (Mathematics); TIME-frequency analysis; CLASSIFICATION
- Publication
Sensors (14248220), 2023, Vol 23, Issue 21, p8727
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s23218727