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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

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Related Experiment Video

Updated: Jan 16, 2026

Electroretinogram Recording for Infants and Children under Anesthesia to Achieve Optimal Dark Adaptation and International Standards

Electroretinogram Recording for Infants and Children under Anesthesia to Achieve Optimal Dark Adaptation and International Standards

Published on: September 3, 2020

Time Series Classification of Autism Spectrum Disorder Using the Light-Adapted Electroretinogram.

Sergey Chistiakov¹, Anton Dolganov¹, Paul A Constable²

¹Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University named after the First President of Russia B. N. Yeltsin, Yekaterinburg 620002, Russia.

Bioengineering (Basel, Switzerland)

|September 27, 2025

Summary

This summary is machine-generated.

Machine learning models, particularly ROCKET and TS-KNN, accurately classify electroretinogram (ERG) signals for autism spectrum disorder (ASD) detection. These models interpret ERG data, focusing on key waveform components for improved diagnostic insights.

Keywords:

electroretinogram explainable AI neurodevelopment retina time-series classification waveform

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Eye Tracking Young Children with Autism

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Last Updated: Jan 16, 2026

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Published on: September 3, 2020

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Published on: March 27, 2012

Area of Science:

Neuroscience
Biomedical Engineering
Data Science

Background:

The clinical electroretinogram (ERG) is a vital diagnostic tool assessing retinal function.
ERG waveforms can aid in diagnosing retinal dystrophies and neurological disorders, including autism spectrum disorder (ASD).

Purpose of the Study:

To apply and interpret time-series-based machine learning (ML) methods for classifying ERG signals from individuals with ASD and typically developing controls.
To understand the decision-making process of ML models in ERG signal classification.

Main Methods:

Utilized various time-series classification (TSC) algorithms to analyze ERG signals.
Employed the Random Convolutional Kernel Transform (ROCKET) algorithm for its accuracy.
Applied SHapley Additive exPlanations (SHAP) for model interpretability.

Main Results:

The ROCKET algorithm achieved the highest accuracy in classifying ERG signals for ASD.
ROCKET and KNeighborsTimeSeriesClassifier (TS-KNN) provided clear explanations by focusing on clinically significant a- and b-waves, discarding baseline noise.
SHAP analysis revealed the suitability of ROCKET and TS-KNN for ASD classification based on ERG data.

Conclusions:

Time-series classification (TSC) effectively identifies critical regions in ERG signals for neurological disorder classification.
This approach supports the diagnostic potential of visual electrophysiology in identifying neurological and retinal diseases.