  Visual electrophysiology is often used clinically to determine functional
changes associated with retinal or neurological conditions. The full-field
flash electroretinogram (ERG) assesses the global contribution of the outer and
inner retinal layers initiated by the rods and cone pathways depending on the
state of retinal adaptation. Within clinical centers reference normative data
are used to compare with clinical cases that may be rare or underpowered within
a specific demographic. To bolster either reference or case datasets the
application of synthetic ERG waveforms may offer benefits to disease
classification and case-control studies. In this study and as a proof of
concept, artificial intelligence (AI) to generate synthetic signals using
Generative Adversarial Networks is deployed to up-scale male participants
within an ISCEV reference dataset containing 68 participants, with waveforms
from the right and left eye. Random Forest Classifiers further improved
classification for sex within the group from a balanced accuracy of 0.72 to
0.83 with the added synthetic male waveforms. This is the first study to
demonstrate the generation of synthetic ERG waveforms to improve machine
learning classification modelling with electroretinogram waveforms.
