Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths
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| Title: | Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths |
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| Language: | English |
| Authors: | 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 (ORCID |
| Source: | Journal of Autism and Developmental Disorders. 2025 55(4):1365-1378. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Autism Spectrum Disorders, Attention Deficit Hyperactivity Disorder, Symptoms (Individual Disorders), Clinical Diagnosis, Measurement Equipment, Physiology, Screening Tests |
| DOI: | 10.1007/s10803-024-06290-w |
| ISSN: | 0162-3257 1573-3432 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1464139 |
| Database: | ERIC |
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| 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. |
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| ISSN: | 0162-3257 1573-3432 |
| DOI: | 10.1007/s10803-024-06290-w |
