SYNTHETIC ELECTRORETINOGRAM SIGNALS FOR ENHANCING CLASSIFICATION OF AUTISM SPECTRUM DISORDER

The electroretinogram (ERG) is a clinical test that records the retina's electrical response to light. The ERG is a promising way to study different neurodevelopmental and neurodegenerative disorders, including Autism Spectrum Disorder (ASD) - a neurodevelopmental condition that impacts language, communication, and social interactions. However, privacy issues and a lack of data complicate Artificial Intelligence applications in this domain. Synthetic ERG signals generated from real ERG recordings should carry similar information and could be used as an extension for natural data. The synthetic dataset consists of ASD and Control with flash strengths of 1.204, 1.114, 0.949, and 0.799 (log cd.s.m^−2). Synthetic reference signals can enhance medical operational efficiency by offering a feasible alternative to natural ones. Synthesizing facilitates dataset expansion within specialized domains, enabling training resource-intensive networks such as transformers.