Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr;55(4):1365-1378.
doi: 10.1007/s10803-024-06290-w. Epub 2024 Feb 23.

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths

Sultan Mohammad Manjur  1 , Luis Roberto Mercado Diaz  1 , Irene O Lee  2 , David H Skuse  2 , Dorothy A Thompson  3   4 , Fernando Marmolejos-Ramos  5 , Paul A Constable  6 , Hugo F Posada-Quintero  7
Affiliations

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths

Sultan Mohammad Manjur et al. J Autism Dev Disord. 2025 Apr.

Abstract

Purpose: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual's specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD.

Methods: Previous studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques.

Results: Key features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups.

Conclusion: The ERG to the stronger flash strength provided better separation and the high frequency dynamics (80-300 Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses.

Keywords: Attention deficit hyperactivity disorder; Autism spectrum disorder; Electroretinogram; Machine learning; Time-frequency analysis.

PubMed Disclaimer

Conflict of interest statement

Declarations. Conflict of interest: The authors declare no conflicts of interest.

Similar articles

See all similar articles

Cited by

References

    1. Adamou, M., Fullen, T., & Jones, S. L. (2020). EEG for Diagnosis of Adult ADHD: A Systematic Review With Narrative Analysis. Frontiers in Psychiatry, 11. https://www.frontiersin.org/journals/psychiatry/articles/ https://doi.org/10.3389/fpsyt.2020.00871 .
    1. Aktas, H., Esin, I. S., & Dursun, O. B. (2020). Is it possible to recognize children diagnosed with ADHD from their facial anthropometric measures? A case-control study. Medical Hypotheses, 140, 109649. https://doi.org/10.1016/j.mehy.2020.109649 . - DOI - PubMed
    1. Al Abdlseaed, A., McTaggart, Y., Ramage, T., Hamilton, R., & McCulloch, D. L. (2010). Light- and dark-adapted electroretinograms (ERGs) and ocular pigmentation: Comparison of brown- and blue-eyed cohorts. Documenta Ophthalmologica Advances in Ophthalmology, 121(2), 135–146. https://doi.org/10.1007/s10633-010-9240-3 . - DOI - PubMed
    1. American Psychiatric Association, A. P., &, & Association, A. P. (1994). Diagnostic and statistical manual of mental disorders: DSM-IV (Vol. 4). American psychiatric association Washington, DC. https://www.gammaconstruction.mu/sites/default/files/webform/cvs/pdf-dia... .
    1. Anderson, N. P., Feldman, J. A., Kolko, D. J., Pilkonis, P. A., & Lindhiem, O. (2022). National norms for the Vanderbilt ADHD diagnostic parent rating scale in children. Journal of Pediatric Psychology, 47(6), 652–661. https://doi.org/10.1093/jpepsy/jsab132 . - DOI - PubMed - PMC