Synthetic Electroretinogram Signal Generation Using Conditional Generative Adversarial Network for Enhancing Classification of Autism Spectrum Disorder

TL;DR

This paper introduces a GAN-based method to generate synthetic ERG signals for children with ASD and controls, aiming to augment datasets and improve AI classification of neurodevelopmental disorders.

Contribution

It presents a novel GAN framework for creating realistic synthetic ERG signals, enhancing data availability for ASD classification tasks.

Findings

Synthetic ERG signals closely resemble real data.

Enhanced classification accuracy with extended synthetic dataset.

Potential application to other psychiatric disorder classifications.

Abstract

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 reciprocal social interactions. However, in heterogeneous populations, such as ASD, where the ability to collect large datasets is limited, the application of artificial intelligence (AI) is complicated. Synthetic ERG signals generated from real ERG recordings carry similar information as natural ERGs and, therefore, could be used as an extension for natural data to increase datasets so that AI applications can be fully utilized. As proof of principle, this study presents a Generative Adversarial Network capable of generating synthetic ERG signals of children with ASD and typically developing control individuals. We applied a Time Series Transformer and Visual Transformer with Continuous Wavelet Transform to enhance classification results on the extended synthetic signals dataset. This approach may support classification models in related psychiatric conditions where the ERG may help classify disorders.