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Classification of Autism Spectrum Disorder From EEG-based functional brain connectivity analysis

Classification of Autism Spectrum Disorder From EEG-based functional brain connectivity analysis
Classification of Autism Spectrum Disorder From EEG-based functional brain connectivity analysis
Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.
1530-888X
1914-1941
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Alotaibi, Noura
50f2f4d2-9c21-4ed5-841b-94b39a16a16c
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Alotaibi, Noura
50f2f4d2-9c21-4ed5-841b-94b39a16a16c

Maharatna, Koushik and Alotaibi, Noura (2021) Classification of Autism Spectrum Disorder From EEG-based functional brain connectivity analysis. Neural Computation, 33 (7), 1914-1941. (doi:10.1162/neco_a_01394).

Record type: Article

Abstract

Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.

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Classification of Autism Spectrum Disorder From EEG-Based Functional Brain Connectivity Analysis - Accepted Manuscript
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Accepted/In Press date: 4 February 2021
e-pub ahead of print date: 11 June 2021

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Local EPrints ID: 450987
URI: http://eprints.soton.ac.uk/id/eprint/450987
ISSN: 1530-888X
PURE UUID: 667cee82-c19d-4ff2-a6c0-81c3b2dcc131

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Date deposited: 31 Aug 2021 16:31
Last modified: 25 Nov 2021 21:43

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Contributors

Author: Koushik Maharatna
Author: Noura Alotaibi

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