Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer.
Sensors (Basel)
; 23(21)2023 Oct 26.
Article
em En
| MEDLINE
| ID: mdl-37960427
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Visão de Cores
/
Análise de Ondaletas
Limite:
Adult
/
Child
/
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha