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Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates
Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates
Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.
autism spectrum disorder (ASD), brain connectivity, complex network, classification, synchrostate
46019
Jamal, Wasifa
3f70176e-843e-46b7-8447-4eefaef104f1
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Oprescu, Ioana-Anastasia
a62fc1ef-4f31-45f1-bb9c-11ba5a7dbe43
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Apicella, Fabio
dad56776-dd88-41ab-a190-58537d29ab32
Sicca, Federico
eca600aa-5535-4bff-b5ac-9b82ff3fe034
Jamal, Wasifa
3f70176e-843e-46b7-8447-4eefaef104f1
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Oprescu, Ioana-Anastasia
a62fc1ef-4f31-45f1-bb9c-11ba5a7dbe43
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Apicella, Fabio
dad56776-dd88-41ab-a190-58537d29ab32
Sicca, Federico
eca600aa-5535-4bff-b5ac-9b82ff3fe034

Jamal, Wasifa, Das, Saptarshi and Oprescu, Ioana-Anastasia et al. (2014) Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. Journal of Neural Engineering, 11 (4), 46019. (doi:10.1088/1741-2560/11/4/046019). (PMID:24981017)

Record type: Article

Abstract

Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

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Accepted/In Press date: 28 April 2014
Published date: 1 July 2014
Keywords: autism spectrum disorder (ASD), brain connectivity, complex network, classification, synchrostate
Organisations: Electronic & Software Systems

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Local EPrints ID: 365014
URI: https://eprints.soton.ac.uk/id/eprint/365014
PURE UUID: b43b4c88-c7f2-4f99-b50a-c1b3b5b420df

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Date deposited: 21 May 2014 10:55
Last modified: 09 Sep 2019 18:44

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