Summary of Synthetic Electroretinogram Signal Generation Using Conditional Generative Adversarial Network For Enhancing Classification Of Autism Spectrum Disorder, by Mikhail Kulyabin et al.
Synthetic Electroretinogram Signal Generation Using Conditional Generative Adversarial Network for Enhancing Classification of Autism Spectrum Disorder
by Mikhail Kulyabin, Paul A. Constable, Aleksei Zhdanov, Irene O. Lee, David H. Skuse, Dorothy A. Thompson, Andreas Maier
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to generating synthetic electroretinogram (ERG) signals from real recordings, which can be used to increase datasets for artificial intelligence (AI) applications in neurodevelopmental and neurodegenerative disorders. Specifically, the study demonstrates the use of Generative Adversarial Networks (GANs) to generate synthetic ERG signals for children with autism spectrum disorder (ASD) and typically developing control individuals. The generated signals are then used to train Time Series Transformers and Visual Transformers with Continuous Wavelet Transform, which enhance classification results on a extended synthetic dataset. This approach has the potential to support classification models in related psychiatric conditions where ERG may help classify disorders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence (AI) to create fake but realistic brain signals that can help diagnose and understand autism spectrum disorder (ASD). The idea is to generate these fake signals, called synthetic electroretinogram (ERG) signals, from real recordings of ERGs. This could help us collect more data and improve AI models for diagnosing ASD and other related disorders. |
Keywords
* Artificial intelligence * Classification * Time series
