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Brain Sciences
Volume 11
Issue 4
10.3390/brainsci11040409
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Article

Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder

by
Lina Abou-Abbas
1,*,
Stefon van Noordt
1,
James A. Desjardins
2,
Mike Cichonski
2 and
Mayada Elsabbagh
1
1
Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
2
Cognitive and Affective Neuroscience Lab, Brock University, St. Catharines, ON L2S 3A1, Canada
*
Author to whom correspondence should be addressed.
Brain Sci. 2021, 11(4), 409; https://doi.org/10.3390/brainsci11040409
Submission received: 16 February 2021 / Revised: 17 March 2021 / Accepted: 18 March 2021 / Published: 24 March 2021
(This article belongs to the Special Issue Advances in Autism Research: Series II)
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Abstract

Event-related potentials (ERPs) activated by faces and gaze processing are found in individuals with autism spectrum disorder (ASD) in the early stages of their development and may serve as a putative biomarker to supplement behavioral diagnosis. We present a novel approach to the classification of visual ERPs collected from 6-month-old infants using intrinsic mode functions (IMFs) derived from empirical mode decomposition (EMD). Selected features were used as inputs to two machine learning methods (support vector machines and k-nearest neighbors (k-NN)) using nested cross validation. Different runs were executed for the modelling and classification of the participants in the control and high-risk (HR) groups and the classification of diagnosis outcome within the high-risk group: HR-ASD and HR-noASD. The highest accuracy in the classification of familial risk was 88.44%, achieved using a support vector machine (SVM). A maximum accuracy of 74.00% for classifying infants at risk who go on to develop ASD vs. those who do not was achieved through k-NN. IMF-based extracted features were highly effective in classifying infants by risk status, but less effective by diagnostic outcome. Advanced signal analysis of ERPs integrated with machine learning may be considered a first step toward the development of an early biomarker for ASD.
Keywords: autism spectrum disorder; event-related potential; empirical mode decomposition; intrinsic mode functions; support vector machine; k-nearest neighbor autism spectrum disorder; event-related potential; empirical mode decomposition; intrinsic mode functions; support vector machine; k-nearest neighbor

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MDPI and ACS Style

Abou-Abbas, L.; van Noordt, S.; Desjardins, J.A.; Cichonski, M.; Elsabbagh, M. Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder. Brain Sci. 2021, 11, 409. https://doi.org/10.3390/brainsci11040409

AMA Style

Abou-Abbas L, van Noordt S, Desjardins JA, Cichonski M, Elsabbagh M. Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder. Brain Sciences. 2021; 11(4):409. https://doi.org/10.3390/brainsci11040409

Chicago/Turabian Style

Abou-Abbas, Lina, Stefon van Noordt, James A. Desjardins, Mike Cichonski, and Mayada Elsabbagh. 2021. "Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder" Brain Sciences 11, no. 4: 409. https://doi.org/10.3390/brainsci11040409

APA Style

Abou-Abbas, L., van Noordt, S., Desjardins, J. A., Cichonski, M., & Elsabbagh, M. (2021). Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder. Brain Sciences, 11(4), 409. https://doi.org/10.3390/brainsci11040409

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