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Gepubliceerd in: Journal of Autism and Developmental Disorders 7/2015

01-07-2015 | Original Paper

Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities

Auteurs: Alessandro Crippa, Christian Salvatore, Paolo Perego, Sara Forti, Maria Nobile, Massimo Molteni, Isabella Castiglioni

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 7/2015

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Abstract

In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2–4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7 % with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype.
Voetnoten
1
A movement unit is defined as an acceleration phase followed by a deceleration phase higher than 10 mm/s, starting from the moment at which the increase or decrease in cumulative velocity is over 20 mm/s (Von Hofsten 1991; Thelen et al. 1996).
 
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Metagegevens
Titel
Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
Auteurs
Alessandro Crippa
Christian Salvatore
Paolo Perego
Sara Forti
Maria Nobile
Massimo Molteni
Isabella Castiglioni
Publicatiedatum
01-07-2015
Uitgeverij
Springer US
Gepubliceerd in
Journal of Autism and Developmental Disorders / Uitgave 7/2015
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI
https://doi.org/10.1007/s10803-015-2379-8