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Journal of Autism and Developmental Disorders

Gepubliceerd in:

23-02-2024 | Original Article

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

Auteurs: Sultan Mohammad Manjur, Luis Roberto Mercado Diaz, Irene O Lee, David H Skuse, Dorothy A. Thompson, Fernando Marmolejos-Ramos, Paul A. Constable, Hugo F. Posada-Quintero

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 4/2025

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.

vorige artikel Assessing the Contribution of Measures of Attention and Executive Function to Diagnosis of ADHD or Autism

volgende artikel The Comprehension of Grammatical Structures in a Pediatric Population with ASD and Epilepsy: A Comparative Study

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Titel: Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths
Auteurs: Sultan Mohammad Manjur
Luis Roberto Mercado Diaz
Irene O Lee
David H Skuse
Dorothy A. Thompson
Fernando Marmolejos-Ramos
Paul A. Constable
Hugo F. Posada-Quintero
Publicatiedatum: 23-02-2024
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders / Uitgave 4/2025
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-024-06290-w

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

Abstract

Purpose

Andere artikelen Uitgave 4/2025

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The Comprehension of Grammatical Structures in a Pediatric Population with ASD and Epilepsy: A Comparative Study

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The Social Validity of Behavioral Interventions: Seeking Input from Autistic Adults

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Abstract

Purpose

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Friendship in Autism Spectrum Disorder Is Related to Diverse Developmental Changes Between Toddlerhood and Adolescence

The Comprehension of Grammatical Structures in a Pediatric Population with ASD and Epilepsy: A Comparative Study

The Economic Impacts of COVID-19 on Autistic Children and Their Families

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