Documenta ophthalmologica. Advances in ophthalmology · 2025
Synthetic electroretinogram signal generation using a conditional generative adversarial network.
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Kulyabin Mikhail, Zhdanov Aleksei, Lee Irene O, Skuse David H, Thompson Dorothy A, Maier Andreas, Constable Paul A
Аннотация
PURPOSE: The electroretinogram (ERG) records the functional response of the retina. In some neurological conditions, the ERG waveform may be altered and could support biomarker discovery. In heterogeneous or rare populations, where either large data sets or the availability of data may be a challenge, synthetic signals with Artificial Intelligence (AI) may help to mitigate against these factors to support classification models.
METHODS: This approach was tested using a publicly available dataset of real ERGs, n = 560 (ASD) and n = 498 (Control) recorded at 9 different flash strengths from n = 18 ASD (mean age 12.2 ± 2.7 years) and n = 31 Controls (mean age 11.8 ± 3.3 years) that were augmented with synthetic waveforms, generated through a Conditional Generative Adversarial Network. Two deep learning models were used to classify the groups using either the real only or combined real and synthetic ERGs. One was a Time Series Transformer (with waveforms in their original form) and the second was a Visual Transformer model utilizing images of the wavelets derived from a Continuous Wavelet Transform of the ERGs. Model performance at classifying the groups was evaluated with Balanced Accuracy (BA) as the main outcome measure.
RESULTS: The BA improved from 0.756 to 0.879 when synthetic ERGs were included across all recordings for the training of the Time Series Transformer. This model also achieved the best performance with a BA of 0.89 using real and synthetic waveforms from a single flash strength of 0.95 log cd s m-2.
CONCLUSIONS: The improved performance of the deep learning models with synthetic waveforms supports the application of AI to improve group classification with ERG recordings.
METHODS: This approach was tested using a publicly available dataset of real ERGs, n = 560 (ASD) and n = 498 (Control) recorded at 9 different flash strengths from n = 18 ASD (mean age 12.2 ± 2.7 years) and n = 31 Controls (mean age 11.8 ± 3.3 years) that were augmented with synthetic waveforms, generated through a Conditional Generative Adversarial Network. Two deep learning models were used to classify the groups using either the real only or combined real and synthetic ERGs. One was a Time Series Transformer (with waveforms in their original form) and the second was a Visual Transformer model utilizing images of the wavelets derived from a Continuous Wavelet Transform of the ERGs. Model performance at classifying the groups was evaluated with Balanced Accuracy (BA) as the main outcome measure.
RESULTS: The BA improved from 0.756 to 0.879 when synthetic ERGs were included across all recordings for the training of the Time Series Transformer. This model also achieved the best performance with a BA of 0.89 using real and synthetic waveforms from a single flash strength of 0.95 log cd s m-2.
CONCLUSIONS: The improved performance of the deep learning models with synthetic waveforms supports the application of AI to improve group classification with ERG recordings.