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

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 accu

SUBMITTER: Kulyabin M 

PROVIDER: S-EPMC10648817 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature