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Biosignal comparison for autism assessment using machine learning models and virtual reality.

Maria Eleonora Minissi¹, Alberto Altozano¹, Javier Marín-Morales¹

¹Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain.

Computers in Biology and Medicine

|March 1, 2024

Summary

Motor skills show promise for objective autism spectrum disorder (ASD) assessment. This study found motor skills, using virtual reality, were more reliable for identifying ASD than eye movements or behavioral responses.

Keywords:

Autism spectrum disorder Biosignal Eye movements Motor skills Statistical machine learning Virtual reality

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Published on: October 4, 2015

Area of Science:

Computational psychiatry
Neurodevelopmental disorders
Biomedical engineering

Background:

Clinical assessments for autism spectrum disorder (ASD) often lack objectivity due to reliance on subjective data.
Computational psychiatry and virtual reality (VR) offer potential for objective, biomarker-based assessments in ecological settings.
Existing ASD research lacks systematic comparisons of biosignals for automatic classification in simultaneous, ecological recordings.

Purpose of the Study:

To compare the effectiveness of different biosignals for the automatic classification of ASD within a VR environment.
To evaluate machine learning models based on motor skills, eye movements, and behavioral responses for ASD detection.
To assess the robustness and reliability of biosignal-based assessments in identifying ASD.

Main Methods:

Development and comparison of machine learning models using implicit (motor skills, eye movements) and explicit (behavioral responses) biosignals recorded in VR.
Utilized a VR screening tool with four distinct virtual scenes for data collection.
Employed a linear support vector classifier with recursive feature elimination, validated through nested cross-validation.

Main Results:

The machine learning model based on motor skills demonstrated the highest robustness in ASD identification, achieving an Area Under the Curve (AUC) of 0.89 (SD = 0.08).
The best-performing behavioral response model achieved an AUC of 0.80.
Eye-movement models showed limitations, necessitating further research due to issues with eye-tracking glasses.

Conclusions:

Motor skills represent a highly promising biosignal for enhancing objectivity and reliability in the early assessment of ASD.
VR-based assessment tools integrating motor skill analysis offer a potential advancement over traditional subjective methods.
Further development is required for eye-tracking technologies to be effectively utilized in ASD assessment within VR.