Classification of autism in young children by phase angle clustering in magnetoencephalogram signals

Kasturi Barik, Katsumi Watanabe, Joydeep Bhattacharya, Goutam Saha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Autism spectrum disorder (ASD) is a complex neu- rodevelopmental condition that appears in early childhood or infancy, causing delays or impairments in social interaction and restricted range of interests of a child. In this work, our goal is to classify autistic children from typically developing children using a machine learning framework. Here, we have used magnetoencephalography (MEG) signals of thirty age and gender matched children from each group. We perform a spectral domain analysis in which the features are extracted from both power and phase of large-scale neural oscillations. In this work, we propose a novel phase angle clustering (PAC) based feature and have compared its performance with commonly used power spectral density (PSD) based feature. It is observed that with Artificial Neural Network (ANN) classifier, PAC yields better classification accuracy (88.20±3.87%) than the PSD feature (82.13±2.11%). To investigate laterality of brain activity, we evaluate the classification performance of each feature type over all channels as well as over individual hemispheres. Using machine learning framework it is found that the discriminating PSD features are mostly from high gamma band i.e. 50-100 Hz frequency oscillations and the PSD features are dominant in right hemisphere. These findings are in line with studies carried before in other framework. However, PAC based feature in our study shows that the whole brain contains important attributes of autism. The discriminating PAC features are mostly from theta band (i.e. 4-8 Hz frequency oscillations) that signifies memory formation and navigation. In this study, it is found that impaired theta oscillations correlate with autistic symptoms. Overall, our findings show the potential of such signal processing and classification based study to aid the clinicians in diagnosis of ASD.

Original languageEnglish
Title of host publication26th National Conference on Communications, NCC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728151205
DOIs
Publication statusPublished - 2020 Feb
Event26th National Conference on Communications, NCC 2020 - Kharagpur, India
Duration: 2020 Feb 212020 Feb 23

Publication series

Name26th National Conference on Communications, NCC 2020

Conference

Conference26th National Conference on Communications, NCC 2020
CountryIndia
CityKharagpur
Period20/2/2120/2/23

Keywords

  • Artificial Neural Network
  • Autism Spectrum Disorder
  • Children
  • Magnetoencephalogram Signal
  • Phase Angle Clustering

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Barik, K., Watanabe, K., Bhattacharya, J., & Saha, G. (2020). Classification of autism in young children by phase angle clustering in magnetoencephalogram signals. In 26th National Conference on Communications, NCC 2020 [9056022] (26th National Conference on Communications, NCC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NCC48643.2020.9056022